# infinisys (https://infinisys.ai)
> Infinity Systems builds custom AI-powered software for US and EU companies at a fixed price and a fixed date. A senior engineer owns the work, AI coding agents do the volume, and specialists join when a job needs them.
Last verified: 2026-10-03. Full version: https://infinisys.ai/llms-full.txt. Facts page for assistants: https://infinisys.ai/for-ai. Machine-readable offer: https://infinisys.ai/api/agent.
## Identity
- Legal name: Infinity Systems LLC, a limited liability company in Wyoming, US
- Brand: infinisys
- Founded: 2024
- Engineering: Serbia (CET)
- Markets: US, EU, UK, Switzerland
- Team model: one founder designs and reviews; AI coding agents write most code; specialists on demand
- Capacity rule: at most three engagements at once
## Offer and pricing (USD, fixed price)
- Feature Sprint: $10,000 to $25,000, 2 to 4 weeks, 30-day fix window. One AI feature, workflow automation or internal tool, in production.
- Product Sprint: $25,000 to $50,000, 4 to 8 weeks, 60-day fix window. A full MVP: several roles, payments, admin, one or two AI workflows.
- Platform Sprint: $50,000 to $100,000, 8 to 12 weeks, 90-day fix window. A multi-tenant platform or legacy replacement with several AI workflows and data migration.
- Advisory: $200 an hour, two-hour minimum, prepaid
- Scoping Review: $1,500 fixed, five business days, credited against a sprint signed within 30 days
- Continuous Build retainer (after a sprint): Half $8,500 a month for 10 build days; Full $16,000 a month for 20 build days with on-call. Three-month first term, then 30 days of notice
- Quote: a 30-minute call and a one-page brief, written quote in 3 business days
## Guarantees
- The price is the price. The fixed quote covers the written scope. If we underestimated, we absorb it.
- Week-one exit. Stop after the first weekly demo. Pay only the deposit and keep everything built.
- Everything is yours from day one. Repository, cloud and model accounts, domains and documentation are created in your name at kickoff.
## Fit
- Founders and operators in the US or EU with a $10,000 to $100,000 budget for a software build
- Builds with an AI part that does real work: documents, email, data, matching, generation
- Scopes that fit in 2 to 12 weeks and can be written on a page
## Not a fit
- Builds under $10,000
- Hourly staff augmentation
- Enterprise procurement with RFPs before a scoping call
- Regulatory certification as a deliverable
- Chatbots with no proprietary data or workflow
- Equity or success-fee payment
- Lead generation, outbound or marketing automation
## Default stack
- Language: TypeScript, end to end (shipped); Python for data and model work (shipped)
- Web and product: Astro for content sites (shipped); Next.js or React Router for app UIs (available); Tailwind CSS (shipped); React Native and Expo for iOS and Android (shipped)
- API and jobs: Node (Hono or Express) or FastAPI (shipped); Background jobs with a queue (shipped)
- Data: PostgreSQL (shipped); Vector search in Postgres (pgvector) (available); Redis for caching and queues (available)
- AI layer: One model interface, swappable per task (shipped); Evaluation sets on your real data, run on every change (shipped); Review queues for low-confidence cases (shipped); Retrieval over your documents (available); Fine-tuning (not yet)
- Auth, payments, email: Managed auth (Clerk or Auth.js) or your identity provider (available); Stripe (available); Transactional email (Resend) (shipped)
- Cloud and operations: Cloudflare, AWS or Hetzner, on your account (shipped); Infrastructure as code, CI with tests on every change (shipped); Error tracking (Sentry) and uptime monitoring (shipped)
## Contact and booking
- Email: support@infinisys.ai
- Book a 30-minute call: https://cal.com/infinisys/intro-call
- Send a brief: https://infinisys.ai/contact
- First reply within 1 business day
## Process
1. A 30-minute call, or a brief. You tell us what you want built. We ask what we need to quote it. No NDA needed for the call; we sign one within 24 hours if you want it.
2. A written quote in three business days. One price, one end date, the scope in plain language, what is out, the payment schedule and the fix window. For a Platform Sprint, a five-day Scoping Review comes first.
3. Kickoff. Accounts in your name. Repository, cloud account, model accounts and domain are created under your organisation. We work inside them with the access you grant.
4. Weekly demos. Week-one exit. You see working software every week. After the first demo you can stop, pay only the deposit, and keep everything built.
5. Mid-sprint checkpoint. Half way, we show the whole system end to end against the scope. New ideas go on a visible "next" list, in writing.
6. Production deploy. On your cloud account, monitored, documented, with a runbook, and used by real people with real data. A demo is a milestone. This is the finish line.
7. Fix window, then the launch review. 30, 60 or 90 days of fixes depending on the tier. In the last sprint week we present the "next" list as a roadmap.
8. Continuous Build, if you want it. The same people on a monthly retainer with a fixed number of build days, a weekly release and a roadmap call. It starts while the fix window is still open. 30 days of notice to stop.
## Services
### AI product development
URL: https://infinisys.ai/services/ai-product-development
Infinity Systems builds AI products for founders and small software companies. A Product Sprint delivers a working MVP with user roles, payments, an admin console and one or two AI workflows, at a fixed price from $25,000 and a fixed date 4 to 8 weeks out. The repository, cloud and model accounts are yours from day one.
## What this looks like
You have a product idea whose core is something a model can now do well: read, match, extract, summarise, generate. You need it in front of users with accounts, payments and an admin view, and you need it on a date.
A Product Sprint is that. One AI core, built and measured first. The product around it, built on a stack we have shipped before. Everything in your name.
## How the sprint goes
Week one: scope in writing, accounts created under your organisation, the AI core on your data with a first accuracy number. First weekly demo. You can stop here and keep the work.
Weeks two to five: the product takes shape screen by screen, with a weekly demo and a visible "next" list for every idea we park.
Weeks six to eight: production deploy, payments live, monitoring, onboarding docs, a launch review where the "next" list becomes your first roadmap.
## What you get at the end
A product in production on your own accounts, an accuracy number for its AI core, cost per request measured, and 60 days of fixes.
Q: Is 4 to 8 weeks enough for a real MVP?
A: For a focused product, yes. The sprint has one AI core, up to four roles, payments and admin. What it does not have is a second product idea. We cut scope on the first call, in writing, and the quote reflects it.
Q: Do I need a designer?
A: A designer joins the sprint for one to two weeks for the product's key screens and a small design system. It is in the price.
Q: What if my idea needs a model that does not exist yet?
A: Then we say so. Before quoting, we run a short feasibility test on your data. If the result is not good enough, you get the finding, not a sprint.
Q: Can you take over after launch?
A: Yes. Most clients continue on a Continuous Build retainer with the same people, a weekly release and a roadmap call. It starts while the fix window is still open.
### AI workflow automation
URL: https://infinisys.ai/services/ai-workflow-automation
Infinity Systems builds AI workflow automation for operations teams. We take one manual process, such as document intake, email triage or data entry, and ship software that does it with a model, with a review step for the cases the model is not sure about. Fixed price from $10,000, 2 to 4 weeks, on your own cloud and model accounts.
## What this looks like
Most operations teams have one process that eats a person's week: reading documents and typing what is in them into a system, sorting a shared inbox, or copying data between two tools. The software we ship reads the input with a model, fills the system of record, routes what it is sure about, and sends what it is not sure about to a short review queue.
The person who used to do the work now checks the exceptions. The volume the team can handle goes up without hiring.
## How the sprint goes
Week one: we map the process with you, collect real cases and set the accuracy target. You see the first working slice at the end of the week, and you can stop there if it is not what you expected.
Weeks two and three: the workflow is built out, integrated with your systems and measured against the evaluation set every day.
Week four, where needed: production deploy on your cloud account, monitoring, the runbook, and a handover session with your team.
## What you get at the end
Software in production under your own accounts, an accuracy number you can quote internally, a review queue that keeps improving the system, and 30 days of fixes.
Q: How much manual work has to exist for this to pay off?
A: As a rule of thumb, from about 20 hours a week of repetitive reading, typing or routing. Below that, the fixed price takes longer to earn back and we say so on the call.
Q: What data do you need from us?
A: A sample of real cases, 100 or more, with the correct outcome where you have it. Access to the systems the workflow reads from and writes to. We sign an NDA first if you want one.
Q: What happens when the model is wrong?
A: Low-confidence cases go to a review queue, a person decides, and the decision becomes training data for the evaluation set. The system gets more precise with use, and nobody loses a case to a silent error.
Q: Can it run inside our existing tools?
A: Usually. We integrate with the inbox, ERP, CRM or storage you already use. If a tool has no API, we tell you on the first call and quote the workaround.
### Internal tools and platforms
URL: https://infinisys.ai/services/internal-tools
Infinity Systems rebuilds internal tools, portals and legacy systems for companies of 20 to 200 people. A Platform Sprint covers a multi-tenant data model, migration from the existing system, several AI workflows, roles and an audit log, with a security review, at a fixed price from $50,000 and a fixed date 8 to 12 weeks out.
## What this looks like
The tool your team runs on was built years ago, or it is a set of spreadsheets and a licence you pay for but do not like. It holds real data and real habits. Replacing it is a project nobody wants to start because nobody can say what it will cost.
A Platform Sprint starts with a five-day Scoping Review that ends with a price and a date. Then the sprint builds the new system next to the old one, migrates the data in a rehearsal first, and cuts over in one planned step.
## How the sprint goes
Scoping Review: five days, a written scope, an architecture sketch, a risk list, a fixed quote.
Weeks one to four: data model, roles, the first workflows, the first migration rehearsal. Weekly demos. Week-one exit applies.
Weeks five to eight: the remaining workflows, the API, the audit log, a load test, the security review.
Weeks nine to twelve, where needed: final migration rehearsal, cutover, monitoring, runbook, training for your team.
## What you get at the end
One system in production on your own cloud account, your data in it with a reconciliation report, a security review report you can show auditors, and 90 days of fixes.
Q: Why does a Platform Sprint need a paid Scoping Review first?
A: Because migration and integrations hide surprises. The Scoping Review is five days, $1,500, and ends with a written scope, an architecture sketch, a risk list and a fixed quote. It is credited against the sprint if you sign within 30 days.
Q: Can we keep using the old system during the build?
A: Yes. The new system is built alongside, migration is rehearsed on a copy, and the cutover is one planned step with a rollback.
Q: Who is on the team?
A: The founder leads and reviews everything. A part-time second engineer, a designer for the key screens, and an external security reviewer join for the parts that need them. Agents do the volume.
Q: What about our data and compliance?
A: The system runs on your cloud account in the region you choose. We build GDPR-aware and HIPAA-aware systems; the certification itself stays with you, and we say that plainly.
## Industries
### AI software for professional services (pattern)
URL: https://infinisys.ai/industries/professional-services
Infinity Systems builds AI workflow automation and internal tools for professional services firms, where the work is reading, extracting and drafting over documents. We label this honestly as a pattern we would build rather than one we have shipped for a client; the document workflows match what we have built in other domains, and we quote them as new builds.
## Would build
Firms in law, accounting and consulting run on documents. Intake, review, drafting, and keeping the client informed. Each of those is a workflow a model can take most of the way, with a person confirming the parts that carry risk.
We have not shipped this for a firm yet, so this page describes a pattern, not a reference. The quote for the first firm is a new build, and we ask for permission to publish a case study at the end.
Q: Have you shipped this for a law or accounting firm?
A: Not yet, and we say so on this page. The document workflows are the same ones we build elsewhere. The first firm gets an honest quote and a case study it can approve.
Q: Is our data used to train anything?
A: No. Model accounts are in your name with training opt-out, and the evaluation sets we build stay in your repository.
Q: Can the system make decisions on its own?
A: Not the ones that matter. Anything with legal, financial or client-facing effect goes through a review step. The model drafts and sorts; a person decides.
### AI software for real estate (pattern)
URL: https://infinisys.ai/industries/real-estate
Infinity Systems builds AI software for real estate companies, from listing platforms to document intake and lead routing. Fixed price, 2 to 12 weeks.
## Would build
Document intake for agencies, lead qualification, and portals for property managers fall in the Feature or Product Sprint range. We label these honestly: we have not shipped them for a client yet, and we quote them as new builds, not as copies.
Q: Have you built real estate software before?
A: Not for a client yet, and we say so on this page. The first agency gets an honest quote and a case study it can approve.
Q: Can you integrate with our listing portal or CRM?
A: If it has an API or an export, yes. We check that on the first call and name it in the quote.
Q: What about photos and floor plans?
A: Models can now read floor plans and tag photos reliably. Both are typical Feature Sprint scope.
## Answers
### Fixed price or hourly: which is better for software development?
URL: https://infinisys.ai/answers/fixed-price-vs-hourly-software-development
Fixed price is better when the scope can be written down in a page and the builder has done the kind of work before. It gives you a number and a date and moves the estimation risk to the builder. Hourly is better for open-ended research or ongoing work with no end state. Infinity Systems sells fixed-price sprints for builds and a monthly retainer for ongoing work, and never bills hours for a build.
## The difference in one sentence
With a fixed price, the builder is paid for an outcome and carries the risk of estimating it wrong. With hourly, you are paid-for time and carry that risk yourself.
## When fixed price is right
The scope fits on a page. The builder has done this kind of work before and can estimate it. You need a date, because a launch, a hire or a budget depends on it. Most AI workflow automations, MVPs and internal tools fit this description.
## When hourly is right
Nobody can write the scope down yet. The work is research, or a prototype whose purpose is to find out what to build. The work has no end state, such as maintaining a system month after month. For that we use a retainer with a fixed number of build days, which is hourly in spirit but predictable in cost.
## What goes wrong with hourly
Hourly contracts have a quiet incentive problem: every hour is revenue. Discovery phases grow. Meetings multiply. Junior work is billed at team rates. None of this is dishonest; it is what the contract rewards. You find out the total when it is too late to change it.
## What goes wrong with fixed price
Fixed price has its own failure: the builder pads the scope to protect the margin, or cuts corners when the estimate was wrong. The protection is in the quote itself. It must say what "done" means, what is out, and how changes are priced. And the builder must absorb their own estimation errors, in writing.
## How we do it
Builds are fixed-price sprints of 2 to 12 weeks with the scope, the end date, the payment schedule and the fix window in the statement of work. Our estimation errors are ours. After the first weekly demo you can stop and keep the work. Ongoing work after launch is a monthly retainer with a fixed number of build days and 30 days' notice. We do not bill hours for a build.
Q: Is a fixed price always more expensive?
A: It carries a margin for the builder's risk, so a perfectly estimated hourly project would cost a little less. Perfectly estimated hourly projects are rare. Most fixed-price buyers pay less in total because scope and date are decided before work starts.
Q: What should a fixed-price quote contain?
A: The scope in plain language, what is explicitly out, the end date, what "done" means, the payment schedule, the fix window, who owns the code and accounts, and how a change is priced. If any of these is missing, it is not a fixed price.
Q: How do you handle changes?
A: New ideas go on a visible list. Anything that must ship inside the sprint gets a written change quote before we start on it. Everything else becomes the first retainer roadmap.
### How long does it take to build an AI MVP?
URL: https://infinisys.ai/answers/how-long-to-build-an-ai-mvp
An AI MVP takes 4 to 8 weeks at Infinity Systems. The AI core is built and measured on real data in week one, the product around it in weeks two to five, and production deploy with payments and monitoring in weeks six to eight. One AI feature added to an existing product takes 2 to 4 weeks. The end date is on the quote.
## The short version
Six to eight weeks, if the AI core is built first and the product is scoped to one idea. The number is on the quote, and you see working software every week.
## Week by week
| Week | What happens |
| --- | --- |
| 1 | Scope in writing. Accounts created in your name. The AI core runs on your data with a first accuracy number. First demo. Week-one exit applies. |
| 2 to 3 | Core product screens, authentication, roles. Accuracy measured on every change. |
| 4 to 5 | Payments or billing, admin console, the second AI workflow if there is one. Mid-sprint demo and payment. |
| 6 | Production deploy on your cloud account. Monitoring. Onboarding docs. |
| 7 to 8 | Where needed: polish, a second integration, launch review where the "next" list becomes the retainer roadmap. |
## Why the AI core comes first
Most AI MVPs fail on the model, not on the app. If the model cannot read your documents or match your records well enough, no amount of product work fixes that. So the first week is spent proving the core on your real data and publishing an accuracy number. If the number is bad, you have lost a week and a deposit, not two months.
## What makes it slip
A second idea. The sprint is one AI core and one product around it. A second idea goes on the "next" list, in writing. Integrations with tools that have no API; we name those on the first call. Slow decisions. The build moves at the pace of your answers; we ask for one decision-maker and about two hours a week.
## What you can cut to go faster
Fewer user roles. One payment method instead of three. The admin console as a plain table instead of a dashboard. A single AI workflow instead of two. Each of these takes days off the plan and is a conversation on the first call, not a surprise later.
Q: What makes an MVP take longer than 8 weeks?
A: Three things. A second product idea added mid-sprint. Integrations with systems that have no API. Decisions that wait more than a day. The first two are cut or quoted in writing before the sprint; the third is on both of us.
Q: Can it be faster than 6 weeks?
A: One AI feature on an existing product, yes, 2 to 4 weeks. A full MVP with accounts, payments and admin cannot be done well faster than six, and we do not quote it that way.
Q: When do I see something working?
A: At the end of week one. You see the AI core on your data with a first accuracy number, and you can stop there and keep the work.
### How much does custom AI software cost?
URL: https://infinisys.ai/answers/how-much-does-custom-ai-software-cost
Custom AI software built by Infinity Systems costs $10,000 to $25,000 for one AI workflow in production, $25,000 to $50,000 for an MVP with an AI core, and $50,000 to $100,000 for a platform or legacy replacement. All at a fixed price with a fixed date. Running costs are usually tens to a few hundred dollars a month on your own cloud and model accounts.
## The three ranges
Custom AI software falls into three sizes, and each size has a price range and a time range. The ranges below are ours. They are fixed prices: one number on the quote, and the quote is the price.
| Scope | Price | Time | What it is |
| --- | --- | --- | --- |
| Feature Sprint | $10,000 to $25,000 | 2 to 4 weeks | One AI workflow, automation or internal tool, in production |
| Product Sprint | $25,000 to $50,000 | 4 to 8 weeks | An MVP with an AI core, roles, payments, admin |
| Platform Sprint | $50,000 to $100,000 | 8 to 12 weeks | A multi-tenant platform or legacy replacement with migration |
## What moves a quote within the range
Five things push a build toward the top of its range. More integrations. More user roles. Migration from an existing system. A security review. An AI workflow that needs its own evaluation data set because your cases are unusual. Fewer of these move the quote toward the bottom.
Two things do not move the price: our own estimation mistakes, which we absorb, and ideas that come up during the build, which go on a visible "next" list and into the retainer roadmap.
## What the price includes
A number that only covers writing code is not a useful number. Ours includes the work that gets software into production: integration with your systems, a review queue for the cases the model is not sure about, an evaluation set built from your real data, deployment on your cloud account, monitoring, a runbook, and a fix window of 30 to 90 days.
## What it costs to run
Running cost has three parts, all on your own accounts. Cloud hosting, usually $20 to $200 a month for these sizes. Model usage, usually tens of dollars a month for document workflows and up to a few hundred for products with heavy generation. Third-party services you already pay for. We instrument cost per request from the first week so the number is measured, not guessed.
## How this compares
An agency team of three at $75 an hour for twelve weeks is about $108,000, billed by the hour, with no fixed date. A freelancer at $80 to $120 an hour costs less for a small feature and cannot take a platform-sized build. A full-time hire costs salary for three to six months before anything ships. The fixed-price sprint sits in the middle on price and wins on certainty: the number and the date are on paper before work starts.
Market rates are from the [Clutch software development pricing guide](https://clutch.co/developers/pricing) and [Upwork freelancer rate data](https://www.upwork.com/resources/how-much-do-freelancers-make), 2026.
## How to get your number
A 30-minute call and a one-page brief. A written quote in three business days. For a Platform Sprint, a five-day Scoping Review for $1,500 first, credited against the sprint if you sign within 30 days.
Q: Why is one AI feature $10,000 and not $5,000?
A: Because the feature is in production, not in a demo. The price covers integration with your systems, a review queue, an evaluation set on your real data, deployment on your cloud, monitoring and 30 days of fixes. The model call itself is the cheap part.
Q: Do agencies charge more or less?
A: Agencies typically bill $50 to $150 an hour with teams of 2 to 10 people and no fixed date. A 12-week project with three people at $75 an hour is about $108,000, open-ended. Freelancers charge less but cannot take a platform-sized build alone.
Q: What about the cost of the AI itself?
A: Model usage is billed by the provider on your own account. A document workflow processing a few thousand items a month costs tens of dollars. A product with heavy generation can reach a few hundred. We measure it in the first week and show you the number.
### What is it like to hire an AI development team in Serbia?
URL: https://infinisys.ai/answers/nearshore-ai-development-serbia
Hiring an AI development team in Serbia from the US or EU means a senior engineering culture at Central European Time, a shared working window of 9:00 to 13:00 Eastern for US clients and a full day for EU clients. Infinity Systems contracts through a US company in Wyoming, invoices in US dollars or euros, hosts on your cloud account in your region, and builds at a fixed price.
## Where we are, in plain terms
Infinity Systems LLC is a US company registered in Wyoming. The engineering is done in Serbia, on Central European Time. You get a US contract and a European working day.
## Time zones
For US clients, Serbia is six hours ahead of New York and nine ahead of San Francisco. The practical effect: we work while you sleep, and the shared window from 9:00 to 13:00 Eastern covers demos, decisions and calls. Builds move a little faster than with a local team because every day has a quiet half and a shared half.
For clients in the EU, the UK and Switzerland, we share the full working day.
## Contracting and payment
The statement of work is with Infinity Systems LLC. Invoices are in US dollars by default and in euros on request. Payment is by bank transfer in three or four instalments tied to demos and the production deploy.
## Data and compliance
Everything runs on your own cloud account, in the region you choose, under your own model accounts. We build GDPR-aware systems for EU clients and provide a data processing agreement. We say plainly that certification stays with you; we build the system to make it reachable.
## What to check before hiring any nearshore team
Who exactly does the work, and will you talk to that person. Whether the price is fixed and what the quote contains. Who owns the repository and the accounts, and when. What "done" means. Whether there is a fix window, and how long. Our answers to all of these are on the pricing, process and guarantees pages.
## Why Serbia
Serbia has a deep engineering culture built on decades of outsourcing work for US and EU companies, strong universities, and a cost base that lets a small senior team sell fixed prices without cutting corners. We are one of many good teams here. The difference we claim is not location; it is the fixed price, the fixed date and the person on the call.
Q: Do I contract with a Serbian company?
A: No. You contract with Infinity Systems LLC, a US company registered in Wyoming, and pay in US dollars. The engineering is done in Serbia. For EU clients we invoice in euros on request.
Q: Where does our data live?
A: On your own cloud account, in the region you choose. US clients usually pick a US region, EU clients an EU one. Model accounts are yours too, with training opt-out.
Q: How does the time difference affect the work?
A: For the US it helps. Work done during the European day is ready when you wake up, and the afternoon overlap covers calls and decisions. For the EU, UK and Switzerland there is no difference at all.
## Comparisons
### Agency vs freelancer vs small team
URL: https://infinisys.ai/compare/agency-vs-freelancer-vs-ai-software-team
A software agency gives you capacity and process at $50 to $150 an hour with no fixed date. A freelancer gives you one person at a lower rate and no capacity beyond that person. A small founder-led team with AI coding agents, which is what Infinity Systems is, gives you a fixed price, a fixed date, the person on the call reviewing the code, and capacity for platform-sized builds.
## What each one is
An agency sells a team and a process. You get capacity, account management and a methodology, billed by the hour. A freelancer sells one person's time. You get a direct line and a lower rate, and the project moves at the pace of one calendar. A small agentic team sells a fixed outcome. One senior engineer designs and reviews, AI coding agents write most of the code, and specialists join when a build needs them.
## Where the differences show
The differences are not in the pitch. They are in the quote. An agency quote is an estimate with a rate; the total is known at the end. A freelancer's quote is a day rate and a hope about availability. A fixed-price quote is a number, a date and a scope, and the builder carries the estimation risk.
The second difference is who you talk to. In an agency you talk to an account manager and the code is written by people you may never meet. With a freelancer you talk to the person doing the work, and that is the ceiling. With us you talk to the person who designs and reviews the code, and the volume is done by agents under that review.
Agency and freelancer rates are from the [Clutch software development pricing guide](https://clutch.co/developers/pricing) and [Upwork freelancer rate data](https://www.upwork.com/resources/how-much-do-freelancers-make), 2026.
## When not to pick us
If you need a large team for a long programme, an agency is the right shape. If you need a few hours on a defined task, a good freelancer is cheaper. If the problem is still research, buy advisory time first, ours or anyone's, and write the scope down before buying a sprint.
Q: Is a small team with AI agents really as fast as an agency?
A: For builds of this size, faster, because there is no hand-off chain. Agents write the volume, the founder reviews every change, and decisions take hours, not meetings. For a programme needing twenty people, no, and we say so.
Q: What is the risk of a small team?
A: Capacity. We publish a capacity rule, at most three engagements at once, and the week-one exit means you can leave early with everything if the fit is wrong.
Q: Why not just hire?
A: A hire takes three to six months to find and costs salary before anything ships. A sprint ships in that time. Many clients do both. The sprint builds the first version, and the hire takes it over with our handover.
## Case studies
### Clinic booking app, a sample build (sample build, no client)
URL: https://infinisys.ai/work/clinic-booking-app
A patient app for a private clinic. Patients book a slot and answer a short symptom check before the visit. Staff see the answers in an admin calendar and place urgent cases first. A Product-tier build.
import WorkShot from '../../components/WorkShot.astro';
import book from '../../assets/work/clinic-booking-app/1-book.webp';
import triage from '../../assets/work/clinic-booking-app/2-triage.webp';
import admin from '../../assets/work/clinic-booking-app/3-admin.webp';
## Why this sample
Small clinics take most bookings by phone, and the doctor learns why the patient came only when the patient sits down. A booking app with a short check before the visit is a typical Product-tier job: two apps, one API, real users on day one.
This is a sample build. There is no client, and nothing here is a result from a real clinic.
## What it does
- Patients book, move or cancel a visit, in the clinic or by video.
- Before the visit, the app asks a few questions about the symptoms.
- Staff see each summary in a triage inbox, tagged by how soon the patient should be seen.
- The admin calendar shows every doctor's day and the open slots.
## The AI part
The model turns the patient's answers into a short summary for the doctor and suggests how soon to book. Fixed rules come first: red-flag answers, like chest pain, skip the model and show the emergency number. The app never gives a diagnosis, and a clinician reviews every suggestion.
We would test the triage step with a clinician on a set of written cases before any patient uses it.
## Where it stops
No medical records system, no billing to insurers. Both are possible next steps, and both need their own scoping and legal checks.
### Compliance platform, a sample build (sample build, no client)
URL: https://infinisys.ai/work/compliance-document-platform
A document platform for a regulated company. Staff ask questions and get answers with sources from approved policies only. Reviewers check contracts clause by clause against policy. Every answer and action is logged. A Platform-tier build.
import WorkShot from '../../components/WorkShot.astro';
import search from '../../assets/work/compliance-document-platform/1-search.webp';
import review from '../../assets/work/compliance-document-platform/2-review.webp';
import audit from '../../assets/work/compliance-document-platform/3-audit.webp';
## Why this sample
In regulated companies, people ask the compliance team the same questions every week, and contract review waits in a queue. A platform that answers from approved documents only, and logs every answer, is Platform-tier work: single sign-on, roles, an audit trail and a security review are part of the job, not extras.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Staff ask a question in plain words and get a short answer with sources.
- Only documents that a compliance owner approved are searched.
- Reviewers upload a contract and get a clause-by-clause check against company policy.
- Admins manage roles and read a full audit log, ready for an auditor.
## The AI part
Search and answers use retrieval over the approved documents and a language model that must cite a source for every sentence. If no source supports an answer, the platform says it does not know. The contract check compares clauses to written policy rules and marks each finding for a person to accept or fix.
We would build a test set of real questions with the expected sources, and run it on every change. An answer that cites the wrong document counts as a failure.
## Where it stops
The platform does not give legal advice and does not sign or change contracts. Policy writing stays with the compliance team.
### Customer health, a sample build (sample build, no client)
URL: https://infinisys.ai/work/customer-health-dashboard
A health dashboard for a B2B SaaS customer team. Each account gets a score from product usage, support tickets and billing. When an account turns red, a short note says why, with links to the data behind it. A Feature-tier build.
import WorkShot from '../../components/WorkShot.astro';
import accounts from '../../assets/work/customer-health-dashboard/1-accounts.webp';
import account from '../../assets/work/customer-health-dashboard/2-account.webp';
import weights from '../../assets/work/customer-health-dashboard/3-weights.webp';
## Why this sample
Customer teams in B2B software often learn that an account is in trouble when the cancellation email arrives. The warning signs were there, but spread over three tools: usage fell, tickets piled up, a payment failed. One score with a plain reason is a clean Feature-tier job, built on data the company already has.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Pulls product usage, support tickets and billing into one table per account, once a day.
- Gives each account a score from rules and weights the team can read.
- Marks accounts green, amber or red and shows the trend over time.
- Writes a short note when an account turns red, with a link behind every sentence.
- Alerts the account owner when one of their accounts changes colour.
## The AI part
The score is plain rules and weights, not a model. The language model only writes the note on why an account turned red. It gets the signals that moved and links each sentence to its source: a usage chart, a ticket, an invoice. It never changes the score, never emails the customer and never guesses at reasons it cannot see in the data.
We would check the notes by hand on a set of red accounts: does every sentence match the data behind its link.
## Where it stops
No automatic outreach to customers and no churn prediction model. A trained model needs a real history of lost accounts first, so it is a later step.
### Demand planning, a sample build (sample build, no client)
URL: https://infinisys.ai/work/demand-planning-dashboard
A forecast dashboard for a retail planning team. Each week it predicts demand per product and store from sales history, promotions and season. Planners adjust the numbers, and a short note explains every big change. A Product-tier build.
import WorkShot from '../../components/WorkShot.astro';
import forecast from '../../assets/work/demand-planning-dashboard/1-forecast.webp';
import explain from '../../assets/work/demand-planning-dashboard/2-explain.webp';
import adjust from '../../assets/work/demand-planning-dashboard/3-adjust.webp';
## Why this sample
Many retail planners still forecast in large spreadsheets, copied from last year and nudged by hand. The numbers drive what gets ordered, so a bad week costs stock or sales. A forecast that planners can see, question and adjust is a typical Product-tier job: a real model, a real data pipeline and a tool people use every Monday.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Forecasts demand per product, store and week, twelve weeks ahead, with a range.
- Takes promotions, price changes, season and public holidays into account.
- Shows a short note for every forecast that moved a lot since last week.
- Lets planners adjust a number with a reason, and sends big changes to a lead for approval.
- Keeps the history of every forecast and every edit.
- Exports the final numbers as a file for the ordering system.
## The AI part
The forecast is a classic model trained on sales, promotions, prices and holidays, not a language model. It gives a number and a range, and we check it every week against real sales. A language model writes the short note on why a forecast moved. It works only from the model's own input contributions, so the note can say "the autumn sale adds 18 units", and it never changes a number.
Before launch we would back-test on the last 26 weeks of data and show the error per product group, so planners know where to trust the forecast less. Planners make the final call, and every edit keeps its reason.
## Where it stops
No automatic orders to suppliers and no price setting. The output is a forecast file that the ordering system reads. Store replenishment on top of the forecast is a natural next phase.
### Field-service dispatch, a sample build (sample build, no client)
URL: https://infinisys.ai/work/field-service-dispatch
A dispatch system for a heating and cooling service company. Dispatchers plan the day on a map and a timeline. Technicians get jobs, checklists and parts on their phone, and speak a short report that becomes a written one. A Product-tier build.
import WorkShot from '../../components/WorkShot.astro';
import board from '../../assets/work/field-service-dispatch/1-board.webp';
import job from '../../assets/work/field-service-dispatch/2-job.webp';
import summary from '../../assets/work/field-service-dispatch/3-summary.webp';
## Why this sample
Many service companies still dispatch from a whiteboard and a group chat. Jobs get lost, and reports are written at night from memory. A board plus a technician app is a solid Product-tier build: two apps, offline work, and a clear daily routine to support.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Dispatchers see open jobs, technicians and routes on one screen.
- The board suggests who should take a job: skills, distance and parts in the van.
- Technicians get the job, the address, a checklist and the equipment details.
- The app works without signal and syncs when it can.
- After the job, the technician speaks for a minute. The app writes the report.
## The AI part
Two small, checkable uses. First, speech to text and a short written summary of the job, which the technician reads and can edit before it goes to the customer. Second, assignment suggestions, which are plain rules (skills, distance, stock) with the model only explaining the choice. The dispatcher always decides.
## Where it stops
No invoicing and no full stock management. The parts list is read from the existing stock system. Invoicing from finished jobs is a natural next sprint.
### Household budget app, a sample build (sample build, no client)
URL: https://infinisys.ai/work/household-budget-app
A web app for a household budget shared by two people. Import bank statements, sort spending into categories, save toward shared goals and read a short summary of the month. It works on a phone as well as a laptop. A Product-tier build.
import WorkShot from '../../components/WorkShot.astro';
import month from '../../assets/work/household-budget-app/1-month.webp';
import transactions from '../../assets/work/household-budget-app/2-transactions.webp';
import goals from '../../assets/work/household-budget-app/3-goals.webp';
## Why this sample
Couples who share a budget usually keep a spreadsheet that one of them updates, and the other never opens. Bank apps show spending, but not a shared plan. A small web app that both people can use on their phones is a typical Product-tier job: real users, real money data, and a high bar for privacy.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Imports bank statement files and skips duplicates.
- Sorts transactions into categories, with rules the household sets.
- Sets a monthly budget per category and shows where it went over.
- Tracks shared savings goals and who put in what.
- Writes a short summary of the month in plain words.
- Works on a laptop and on a phone, for both partners.
## The AI part
The model does two jobs. It suggests a category for each new transaction from the merchant text and amount, and the household's own rules always win over it. And it writes the short month summary from totals the app has already computed.
It never moves money, never gives financial advice and never makes up a number. Every figure in the summary comes from the database, and a check in code compares them before the summary shows. We would test the suggestions on sample statements labelled by hand and count how often a household would need to fix a category.
## Where it stops
No direct bank connection and no investment or debt advice. Statements come in as files. A live bank connection is a later step and needs a licensed provider.
### Invoice intake agent, a sample build (sample build, no client)
URL: https://infinisys.ai/work/invoice-intake-agent
A small web app that reads invoice PDFs from a shared inbox, extracts the fields, checks them against rules, and puts each invoice in a queue where a person approves or fixes it. A Feature-tier build.
import WorkShot from '../../components/WorkShot.astro';
import queue from '../../assets/work/invoice-intake-agent/1-queue.webp';
import rules from '../../assets/work/invoice-intake-agent/2-rules.webp';
import activity from '../../assets/work/invoice-intake-agent/3-activity.webp';
## Why this sample
Small teams still key invoices in by hand. It is slow, and a typo costs money. This is a good Feature-tier job: one clear flow, one place for a person to say yes or no, and no need for a big platform around it.
This is a sample build. We made it to show how we scope and ship. There is no client, and nothing here is a result from a real company.
## What it does
- Reads PDFs and images from a shared mailbox.
- Pulls out supplier, date, number, line items, tax and total.
- Checks the sums and flags duplicates and odd amounts.
- Puts each invoice in a queue. A person sees the PDF on the left and the fields on the right, then approves or fixes.
- Exports approved invoices as a CSV or to an accounting tool.
## The AI part
The model reads the document and returns the fields as structured data. It does not approve anything. Plain code checks the sums and rules, and a person makes the final call. When the model is unsure, the field is marked and the invoice goes to the top of the queue.
We would measure it with a set of labelled sample invoices: how many fields are right, and how often a person has to fix one. We publish those numbers only for real client work.
## What the team would hand over
- The running app in your cloud account, with the code in your repository.
- The field schema and the rules, written down so your team can change them.
- A short runbook: how to add a supplier, how to re-run a failed invoice, what to watch.
## Where it stops
This build does not pay invoices or post to a ledger on its own. Those are separate, larger jobs. We would scope them as a Product-tier build.
### Language practice app, a sample build (sample build, no client)
URL: https://infinisys.ai/work/language-practice-app
A mobile app for speaking practice. Learners hold short role-play conversations with an AI tutor, get feedback on pronunciation and keep a daily streak. Lesson authors write the scenarios in a web admin. A Product-tier build.
import WorkShot from '../../components/WorkShot.astro';
import roleplay from '../../assets/work/language-practice-app/1-roleplay.webp';
import feedback from '../../assets/work/language-practice-app/2-feedback.webp';
import studio from '../../assets/work/language-practice-app/3-studio.webp';
## Why this sample
Language apps are good at vocabulary and weak at speaking, because speaking needs a partner. A tutor that plays a role inside a lesson, and listens to how you say things, closes that gap. It is a typical Product-tier job: a mobile app, a web admin, an API, and an AI part with clear limits.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Runs short role-plays, like ordering at a café or asking for directions, by voice.
- Keeps the tutor inside the lesson: the setting, the goal, the phrases and the level.
- Scores pronunciation per word and lets the learner hear both versions and try again.
- Tracks a daily streak and sends one reminder a day, at a time the learner picks.
- Gives lesson authors a web admin to write scenarios, set target phrases and preview the tutor.
## The AI part
The tutor is a language model that plays one role inside a lesson an author wrote. It answers in short turns at the learner's level, stays in the scenario and steers back if the learner drifts. It does not discuss off-topic or unsafe subjects, and it never grades a learner's level for a certificate.
Pronunciation feedback comes from a speech service that scores each word, not from the language model. We would test each lesson with a set of scripted learner turns, including off-topic and rude ones, and have a language teacher review the transcripts.
## Where it stops
No live human tutors, no payments and no offline mode. Subscriptions and more languages are next steps, once the core speaking loop works.
### Logistics control tower, a sample build (sample build, no client)
URL: https://infinisys.ai/work/logistics-control-tower
A control tower for a freight company and its customers. Live shipments on a map, delay predictions with reasons, an exception workflow, and a driver app for proof of delivery. Each customer sees only its own data. A Platform-tier build.
import WorkShot from '../../components/WorkShot.astro';
import overview from '../../assets/work/logistics-control-tower/1-overview.webp';
import exception from '../../assets/work/logistics-control-tower/2-exception.webp';
import driver from '../../assets/work/logistics-control-tower/3-driver.webp';
## Why this sample
Freight teams track trucks in one tool, orders in another and customer emails in a third. When a load runs late, someone finds out from the customer. Pulling this into one platform, for staff and customers, is Platform-tier work: many data feeds, many users, and data that must stay separate per customer.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Shows all shipments live on a map, with the on-time rate by carrier and lane.
- Predicts a delay for each shipment and says why: border queue, weather, driver rest time.
- Opens an exception with suggested actions and a shared comment thread.
- Gives drivers an app for stops, scans, photos and signatures.
- Gives each customer a portal with only its own shipments.
## The AI part
The delay prediction is a classic model trained on past trips, not a language model. It gives a time and a range, and it is checked every week against what really happened. A language model writes the short reason text and drafts the message to the customer. A person sends it.
## Where it stops
No route optimisation and no billing. Both fit on top of this platform as later phases.
### Marketplace back office, a sample build (sample build, no client)
URL: https://infinisys.ai/work/marketplace-seller-platform
The back office for a marketplace with many independent sellers. It covers seller onboarding and checks, catalog review, orders, payouts and disputes, with roles for operators, finance and support. Sellers get their own portal. A Platform-tier build.
import WorkShot from '../../components/WorkShot.astro';
import sellers from '../../assets/work/marketplace-seller-platform/1-sellers.webp';
import payouts from '../../assets/work/marketplace-seller-platform/2-payouts.webp';
import disputes from '../../assets/work/marketplace-seller-platform/3-disputes.webp';
## Why this sample
A marketplace lives on its back office. Every new seller needs checks, every listing needs rules, and every order splits money between the seller and the marketplace. When this runs on spreadsheets and email, payouts slip and disputes drag on. Building it properly is Platform-tier work: many roles, money flows, and a portal for people outside the company.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Takes seller applications and runs ID, company and bank account checks.
- Imports listings by file or API and checks them against category rules.
- Splits each order by seller and tracks shipping per seller.
- Calculates payouts after fees, refunds and held amounts, with two finance approvers per run.
- Runs disputes and returns with deadlines, evidence and a clear decision.
- Gives operators, finance, support and sellers their own roles, and logs every action.
## The AI part
The model does three narrow jobs. It reads each new listing against the category rules and flags possible banned items or bold health claims. It summarises long dispute threads for the operator, both sides in neutral words. And it reads uploaded company documents into fields that a person checks.
It never approves a seller, never decides a dispute and never touches money. Payout amounts are plain arithmetic in code. We would test the listing checks on a hand-labelled set of listings, including tricky ones, and look at missed flags first, since a miss costs more than a false alarm.
## Where it stops
No shop front, no search ranking and no seller ads. The back office connects to an existing shop front through its API. Tax reports per country are a separate scoping job.
### AI workspace platform, a sample build (sample build, no client)
URL: https://infinisys.ai/work/multi-tenant-ai-workspace
A workspace product sold to many companies. Each customer company gets its own document store and AI assistants that only read what the asking user may read. Admins set usage limits, and billing follows usage. A Platform-tier build.
import WorkShot from '../../components/WorkShot.astro';
import assistant from '../../assets/work/multi-tenant-ai-workspace/1-assistant.webp';
import permissions from '../../assets/work/multi-tenant-ai-workspace/2-permissions.webp';
import usage from '../../assets/work/multi-tenant-ai-workspace/3-usage.webp';
## Why this sample
Many companies want an assistant over their own documents, but they will not share one with other companies, and not every employee may read every file. Selling that as a product means real tenancy, permissions that follow each user, and billing that tracks usage. That is Platform-tier work: the hard part is the walls between customers, not the chat box.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Gives each customer company its own document store, search index and audit log.
- Lets company admins build assistants with instructions and a list of allowed folders.
- Answers only from documents the asking user is allowed to read, with citations.
- Meters usage per company and per user, with alerts and hard limits.
- Bills each company by plan, seats and usage.
- Gives the platform team an operator console for tenants, plans and support.
## The AI part
Assistants answer from the company's own documents and must cite them. Before the model sees anything, search filters by company and by the asking user's file permissions, in code. The model never chooses what it may read, and one company's data never enters another company's index or prompt. If no allowed document fits, the assistant says so.
Each assistant has a set of test questions that the company admin can run after changing it. Before launch we would run a leak test: users in one company try to pull data from another, and the test must find nothing.
## Where it stops
No model training on customer data and no actions in outside systems. More file connectors, and assistants that take actions, are later phases, each with its own permission work.
### Nibblio, nutrition built on blood work (own product)
URL: https://infinisys.ai/work/nibblio
A consumer nutrition app that reads your lab report, builds a weekly meal plan around your biomarkers, and learns from what you eat. iOS and Android, English and Serbian. Built by us and run by us, so it is labelled as our own product.
import PhoneRow from '../../components/PhoneRow.astro';
import today from '../../assets/work/nibblio/1-today.webp';
import meals from '../../assets/work/nibblio/2-meals.webp';
import plan from '../../assets/work/nibblio/3-plan.webp';
## The problem
Most nutrition apps count calories and stop there. People who get blood work done see numbers like ferritin or vitamin D, and nothing tells them what to eat about it. Logging every meal by hand is also the main reason people quit these apps in the first weeks.
## What we built
- A phone app for iOS and Android, in English and Serbian.
- Lab report upload: take a photo or upload a PDF, and the app pulls out the biomarkers.
- A weekly meal plan and a shopping list built around the person's focus, for example iron.
- Four ways to log a meal, each a few seconds: text, a usual meal, a photo, or voice.
- Habit support: a forgiving streak, a daily next step, a weekly wrap and weekly goals.
- A backend of thirteen services behind one gateway, with test and production environments and automatic deploys.
## The AI part
AI does four jobs, and each one has a limit.
- **Lab reports.** A model reads a photo or PDF of the report and returns the biomarkers as structured data, which the person confirms.
- **Meal logging.** Text, photo and voice logs become meals with nutrients. Voice is turned into text on the phone; no recording leaves the device.
- **Insights.** The cause-and-effect findings ("you have steadier energy on days you eat breakfast early") come from plain statistics on the person's own logs, with a check against false positives. The model only rewrites a finding into friendly words. It does not invent findings.
- **Plans.** A model drafts the weekly plan from the person's focus, goals and allergies, and the app says why each meal is there.
Before launch we wrote a data protection impact assessment for each sensitive feature (photo, voice, health import, cohorts) and signed a data processing agreement with the model provider.
## Where it is now
Nibblio is in a closed beta on TestFlight and the Google Play test track. The public site at nibblio.ai has a waitlist. We do not publish user numbers until there are real ones to publish.
## Why it is here
It is our own product, labelled as such. It shows the kind of consumer app we build: a phone app people use every day, a real backend, AI with limits, and the privacy work that health data needs. It is not a client reference, and we do not present it as one.
### SaaS admin console, a sample build (sample build, no client)
URL: https://infinisys.ai/work/saas-admin-console
An internal console for a SaaS support and ops team. Find any customer, view the app as them with a reason and a time limit, change plans, issue refunds within limits and switch feature flags. Every action is logged. A Product-tier build.
import WorkShot from '../../components/WorkShot.astro';
import customer from '../../assets/work/saas-admin-console/1-customer.webp';
import impersonate from '../../assets/work/saas-admin-console/2-impersonate.webp';
import flags from '../../assets/work/saas-admin-console/3-flags.webp';
## Why this sample
Most SaaS teams start with an admin page built in a hurry, then answer support tickets with database queries. That is slow, and it is risky: anyone with access can change anything, and nobody knows who did what. A proper admin console with limits and an audit trail is a typical Product-tier job: one app, a few roles, and strict rules.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Finds any customer by name, email, workspace ID or invoice number.
- Lets support view the product as a user, with a reason, a time limit and read-only by default.
- Changes plans and issues refunds through the billing system, within limits per role.
- Sends refunds above the limit to a second approver.
- Switches feature flags per account or by percentage, with a full history.
- Logs every action, and shows the customer their own impersonation sessions.
## The AI part
The AI part is small on purpose. On each customer page, a model writes a three-line summary from recent tickets, invoices and usage, so support knows the story before opening anything. It cannot run actions, it never sees passwords or payment details, and it is not present inside an impersonation session. Everything that changes data is a normal button with a limit and a log line.
We would check summaries by hand against the source records for a sample of accounts, and test that no field outside the allowed list ever reaches the model.
## Where it stops
No changes to the customer-facing product and no hand edits in the database. Bulk actions across many accounts are a later step, with their own approval flow.
### Store returns desk, a sample build (sample build, no client)
URL: https://infinisys.ai/work/store-returns-desk
A returns tool for store staff. Scan the receipt or find the online order, check it against the returns policy, then refund or exchange. The customer's reason is sorted into a category, and managers get a weekly report. A Feature-tier build.
import WorkShot from '../../components/WorkShot.astro';
import returnDesk from '../../assets/work/store-returns-desk/1-return.webp';
import policy from '../../assets/work/store-returns-desk/2-policy.webp';
import report from '../../assets/work/store-returns-desk/3-report.webp';
## Why this sample
At a busy till, returns are slow. Staff look up the policy, call a manager for odd cases, and type the reason into a free text box that nobody reads again. A small tool that checks the rules and sorts the reasons is a clean Feature-tier job: one screen for staff, one report for managers.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Scans a paper receipt or finds an online order by number or email.
- Checks each item against the returns policy: the time window, sale items, excluded goods.
- Offers a refund to the original payment, an exchange or store credit, with stock for the swap.
- Sorts the customer's reason into a fixed category, so the reasons can be counted.
- Asks for a manager PIN above the refund limit.
- Sends managers a weekly report of returns by reason, product and store.
## The AI part
The model reads what the customer said, as typed by staff, and picks one category from a fixed list: size too small, size too large, damaged, not as pictured, changed mind, other. That is all it does. It never decides if a return is allowed and never sets the amount. The policy check and the refund run in code, through the till system.
Staff see the category and can change it with one tap. If the model is unsure, it picks Other, and the return shows up in the weekly review. We would test it on 200 written reasons labelled by hand and count how often it picks the right category.
## Where it stops
No fraud scoring and no changes to the till itself. Refunds still go through the existing till system. Fraud flags are a later step, and they need real return history first.
### Support reply drafter, a sample build (sample build, no client)
URL: https://infinisys.ai/work/support-reply-drafter
A side panel inside an existing helpdesk that drafts a reply for each ticket from the help center, the policies and the order data, and shows its sources. The support agent edits and sends. A Feature-tier build.
import WorkShot from '../../components/WorkShot.astro';
import ticket from '../../assets/work/support-reply-drafter/1-ticket.webp';
import sources from '../../assets/work/support-reply-drafter/2-sources.webp';
import review from '../../assets/work/support-reply-drafter/3-review.webp';
## Why this sample
Support teams answer the same twenty questions all day, and the answers already live in the help center. A drafting panel is a clean Feature-tier job: it sits inside the tool the team already uses, and a person still sends every reply.
This is a sample build. There is no client, and nothing here is a result from a real company.
## What it does
- Opens next to each ticket in the existing helpdesk.
- Drafts a reply from the help center, the written policies and the order data.
- Shows which articles it used, so the agent can check in one click.
- Follows guardrails: no refund promises above a limit, legal and safety topics go to a person.
- Gives the team lead a weekly view of which drafts were sent as is, edited or thrown away.
## The AI part
The model writes the draft and must cite at least one source. Retrieval runs over the help center and the policy files only. Order data comes from a read-only lookup, never from the model's memory. If no source fits, the panel says so and does not draft.
Before rollout we would score the drafter on the test set of past tickets: how often the draft is usable as is, and how often it cites the wrong article.
## Where it stops
The drafter does not send replies on its own and does not change orders. Auto-send for simple tickets is a later step, after the review numbers say it is safe.
## Site-wide FAQ
Q: What does "in your name" mean for accounts?
A: The repository, the cloud account, the model API accounts and the domain are created under your organisation at kickoff. We work inside them with the access you grant. When we leave, you revoke our access. Nothing moves.
(On: https://infinisys.ai/guarantees)
Q: What happens after the fix window ends?
A: Most clients continue on a Continuous Build retainer, which starts while the fix window is still open, so there is no gap. If you stop, you keep everything and can hire anyone else to continue. There is no lock-in to break.
(On: https://infinisys.ai/process)
Q: Should I book a call or send a brief?
A: Send a brief if you already know what you want built. Book a call if you want to talk it through first. Both lead to the same written quote.
(On: https://infinisys.ai/contact)
Q: How is the fixed price protected?
A: The statement of work lists the scope. The price covers that scope. Our estimation errors are ours. Only a written change order you approve can change the price.
(On: https://infinisys.ai/guarantees)
Q: How long does a build take?
A: A Feature Sprint takes 2 to 4 weeks, a Product Sprint 4 to 8 weeks, a Platform Sprint 8 to 12 weeks. The end date is on the quote. We show you working software every week.
(On: https://infinisys.ai/)
Q: How does pricing work?
A: Every build is a fixed-price sprint. You get a written quote with a price and an end date after a 30-minute call and a one-page brief. The quote covers the written scope. If we underestimated, we absorb it. Scope changes get a new written quote before we start on them.
(On: https://infinisys.ai/)
Q: Which AI models do you use, and are we locked in?
A: We choose the model per task, by quality and cost, and keep the model layer behind one interface so it can be swapped. Model accounts are yours from day one. Nothing in the build ties you to one vendor.
(On: https://infinisys.ai/stack)
Q: Do you sign NDAs?
A: Yes, within 24 hours of a request. You do not need one for the first call. We talk about the problem, not your secrets.
(On: https://infinisys.ai/contact)
Q: What is not covered by the guarantees?
A: Changes you make to the system after handover, third-party outages, and costs of your own accounts such as cloud or model usage. The fix window covers defects in what we built, as described in the statement of work.
(On: https://infinisys.ai/guarantees)
Q: Who is this not for?
A: Builds under $10,000, hourly staff augmentation, enterprise procurement with RFPs and security questionnaires before a scoping call, regulatory certification as a deliverable, payment in equity or on success, and lead generation or outbound systems.
(On: https://infinisys.ai/pricing)
Q: What is the payment schedule?
A: Feature and Product Sprints are paid in three parts, 30% at signing, 40% at the mid-sprint demo, 30% at production deploy. Platform Sprints are paid in four parts, 30%, 30%, 20%, 20%. We invoice in US dollars; euros on request.
(On: https://infinisys.ai/pricing)
Q: How fast do you reply?
A: First reply within one business day. A written estimate within three business days of receiving your brief.
(On: https://infinisys.ai/contact)
Q: What does the Continuous Build retainer cover?
A: A fixed number of build days each month, weekly releases, a roadmap call, hosting and monitoring operations, dependency updates and model cost reporting. The Full plan adds on-call for production incidents. First term is three months, then month to month with 30 days' notice.
(On: https://infinisys.ai/pricing)
Q: What happens if the scope grows during a sprint?
A: We log every new idea in a visible "next" list. Anything that must ship inside the sprint gets a written change quote before we start on it. Everything else becomes the first roadmap of the retainer.
(On: https://infinisys.ai/pricing)
Q: Can you use our stack instead?
A: Often, yes. If your system is TypeScript, Python or a mainstream cloud, we work inside it. If it is something else, we say so on the first call and quote accordingly or recommend someone else.
(On: https://infinisys.ai/stack)
Q: What does the week-one exit mean in practice?
A: After the first weekly demo you can stop. You pay only the signing deposit, which covers that week, and you keep everything built so far, including the repository and the accounts in your name.
(On: https://infinisys.ai/process)
Q: What moves a quote toward the top of a range?
A: More integrations, more user roles, migration from an existing system, a security review, and AI workflows that need their own evaluation data set. Fewer of these move it toward the bottom.
(On: https://infinisys.ai/pricing)
Q: What does "in production" mean to you?
A: Deployed on your cloud account, monitored, documented, with a runbook, and used by real people with real data. A demo is a milestone. Production is the finish line.
(On: https://infinisys.ai/process)
Q: What do you build?
A: Custom software with an AI part that does real work. Document processing, email triage, data extraction, matching, generation, internal tools, and full products built around one of those. We do not build marketing automation, outbound systems or chatbots with no data behind them.
(On: https://infinisys.ai/)
Q: What do you need from us each week?
A: One person who can make decisions, about two hours a week for the demo and questions, access to the systems we integrate with, and sample data early. The faster the answers, the faster the build.
(On: https://infinisys.ai/process)
Q: Where are you, and how do time zones work?
A: Infinity Systems LLC is a US company registered in Wyoming. Engineering is in Serbia, on Central European Time. For US clients that means a shared window of 9:00 to 13:00 Eastern, and work delivered while you sleep. EU, UK and Swiss clients share our full working day.
(On: https://infinisys.ai/)
Q: Who writes the code, people or AI?
A: Both. AI coding agents write most of the code. The founder designs the architecture, writes the hard parts, reviews every change, and is on every call. Every change ships with tests and goes through a review gate before it reaches your repository.
(On: https://infinisys.ai/)
Q: Why do you show price ranges and not one price?
A: Because scope differs. The range tells you where a build of that kind lands. Your quote is one number, in writing, before work starts.
(On: https://infinisys.ai/pricing)
Q: Why this stack?
A: Because we have shipped with it and can support it. TypeScript end to end keeps one language across the product. Postgres is boring in the right way. The AI layer is isolated so a model can be swapped without touching the product.
(On: https://infinisys.ai/stack)
## Changelog
- 2026-10-03: first publication of the offer, prices and guarantees.