Prototype to production · for startups

It works in the demo. Now make it work for real users.

We take the prototype you have, fix what breaks under real use, and keep it working. 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 prototypes break with real users.

Our prototype works in a demo but breaks with real users.
Nothing handles the AI service being slow, down or wrong.
Keys and passwords sit in the code instead of somewhere safe.
When it fails, nobody can see why.

Prototype to production

What you get.

An honest review first

We look at what's there in the scoping session and fix a price for making it solid.

The breaking points fixed

Errors handled, slow or failed AI calls caught, and data stored properly.

Keys and access locked down

Secrets moved out of the code and into accounts you own, with access limited to who needs it.

Monitoring

You see errors and the AI's mistakes before your users tell you about them.

Timeline

From demo to production.

Scoping
We review the prototype and fix the price.
Week 1
Keys, data and the biggest breaking points fixed first.
Week 2
Error handling, fallbacks and monitoring.
Week 3
Tested with real users, next to the old version until it works.

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

Prototype to production, answered plainly.

See every question

Yes. A demo that breaks with real users is one of the most common things we're asked to fix. The scoping session looks at what's there and fixes a price for making it solid.

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.

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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