AI feature build · for startups

The AI feature on your roadmap, working in your product.

Search, assistants, document processing or recommendations, built in production code inside the product you already have. Fixed price after a short scoping call.

Book a free 30-minute call

Working in 21 days from access, or your build fee back. Half paid only when it works. Full terms

The problem

Why it hasn't shipped yet.

We need to ship this AI feature but the team is stretched.
It works in a notebook, but nobody has had time to wire it into the product.
We tried AI and it got things wrong.

AI feature build

What you get.

Built in your codebase

In your repository, your stack and your review process, in code your engineers can read and take over.

Checked before it's trusted

We test the AI's answers against real examples, and a person checks anything that touches customers or money until it's accurate.

Ready for real users

Errors handled, a fallback for when the AI service is slow, and monitoring so you know when something goes wrong.

Handed over properly

Your team gets the code, the keys and a walkthrough of how it works.

Timeline

From scoping call to working feature.

Scoping
We agree exactly what the feature does, and it ends in a fixed price.
Day 1
The 21 days start the day we have access to your repository and accounts.
Weeks 1–2
Built and tested against real examples, with your team reviewing as we go.
Week 3
Released to real users and watched closely.

Our promise

A first working version in 21 days from access, or your build fee back. Half paid only when it works.

Read the full terms

Fit

Who it's for, and who it isn't.

A good fit

Funded or already making money, with a budget for building
An AI feature, AI product or full-stack app to build or finish
A team too small or too stretched to ship it on time

Not a fit

Solo founders without a budget for the build
AI that decides who gets hired, a loan, housing or insurance
Automated texts or calls to consumers

Startups

AI features we buildfor startups.

Each one is built in production code, with the parts that keep it working once real users arrive.

Support answer assistant
Next.jsPostgrespgvectorClaude APIIntercom
1Ticket arrivesFrom Intercom or the in-app help form.
2Sorted by topicThe model tags topic and urgency, and urgent ones jump the queue.
3Docs searchedOnly your help articles and past answers are searched for the facts.
4Reply draftedA draft that links the sources it used, or says it doesn't know.
5Agent sends itEdits are saved and become test cases for the next release.
AI app · Customer support
100%Answers sourced
Support answers from your own docs, or an honest "I don't know".
Tickets are sorted by topic and urgency, answered from your own help articles with the sources linked, and sent by a support agent who can edit the draft.
How the support answer assistant works
Document extraction feature
ReactNode.jsS3GeminiZod
1File uploadedPDFs, scans or photos, stored privately against the user's account.
2Fields readThe model pulls out dates, names, totals and terms into a fixed format.
3Checked in codeEvery field is validated, so a wrong date or total can't get through.
4Unsure fields flaggedAnything the model isn't sure of is highlighted for the user to confirm.
5Saved to the appClean records the rest of the product can search and report on.
AI app · Document processing
EveryField validated
Upload a contract, get clean data back.
Users upload PDFs, scans or photos. The AI pulls out the dates, names, totals and terms into a fixed format, and anything it isn't sure of is highlighted for the user to confirm.
How the document extraction feature works
In-app data assistant
Next.jsPostgresOpenAI APIRedisPostHog
1User asksA plain-English question inside the product.
2Permissions checkedOnly records that user is allowed to see are ever searched.
3Answer builtThe reply links to the records it came from, so users can check it.
4Costs cappedPer-user limits and caching keep the AI bill predictable.
5Usage trackedWhich answers helped and which didn't, to guide the next change.
AI app · In-product assistant
EverySearch permission-checked
Answers from each user's own data, and nobody else's.
Users ask questions in plain English inside the product. Answers come only from records they're allowed to see, with links back to each one and a cap on AI costs.
How the in-app data assistant works

All startup case studies

The team

Who you'll work with.

Senior people scope, build and look after every project. The people you meet on the first call are the people who answer when something needs fixing.

Muhammad Furqan Abid

Founder

Leads engineering on every build: the scope, the architecture, and how the AI parts behave once real people use them. Six years building software.

Muhammad Zaid Ashfaq

Managing Director

Runs delivery and care. He keeps each build on its 21 days, and he's the person you talk to when something needs a decision, during the build and after it.

How we handle your accounts and data

FAQ

AI feature build, answered plainly.

See every question

Yes. We build in your repository, in production code your own engineers can read and take over. If you don't have a codebase yet, we set one up that you own.

Anything the AI does for your customers or your money is checked by a person until it's accurate. You see what it gets right and wrong before anyone relies on it.

It depends on the job, so we don't quote a number before we've seen it. For businesses, the Ops Map ends in a fixed price for the build. For startups, the scoping session does. Either way you know the price before any build starts.

You do, from day one. We build in accounts you own, the code lives in your repository, and the keys are yours. If we stop working together, nothing has to be moved or bought back.

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