AI Full-Stack Development

AI full-stack development that gets your idea in front of real users

Getting an AI demo to work takes an afternoon. Getting it to work for paying customers, on real data, at a cost that makes sense, is the hard part. That’s the part we do: the app, the back end, the data and the AI layer, built and looked after by one team.

Book a free 30-minute callWhat we build

Sound familiar?

Problems we fix

If your AI demo works but isn’t a product yet, these will sound familiar.

The prototype never shipped

It works in a playground or a notebook. It doesn’t have accounts, billing, permissions or a way to tell when it’s wrong.

No in-house AI engineers

Your developers are flat out on the core product, so the AI roadmap slips another quarter.

Worried about lock-in

Model prices and limits change every few months. You don’t want your margins tied to one provider’s roadmap.

Quality nobody can measure

It looks great in the demo, but nobody can tell you how often it’s right on real customer data.

What we build

What you get

AI SaaS MVPs

Sign-up, billing, separate data per customer and the core AI feature, ready for your first paying users.

Copilots inside your app

An assistant that knows your product’s data and can act through your own APIs, not a chat box bolted on the side.

Answers from your data (RAG)

Answers drawn from your documents, tickets and databases, with links to the source so people can check.

Model routing

The right model for each task, on quality and cost, and the freedom to switch without a rewrite.

Evals & monitoring

Test sets built from real questions, plus cost and quality tracking, so you know it works before customers do.

Admin & usage panels

One place to manage customers, prompts, limits and spend without asking a developer.

Real project · Finance operations

AI invoice processing with its own internal app

Invoices and receipts arrived by email, and someone had to open each one, type the details into a system and save the file in the right folder.

Invoices are read, filed and recorded without retyping, and the team sees all of them in one app, with the original file one click away.

ZapierGeminiGoogle DrivePostgreSQL
Read the invoice automation case study →

What we built

  • Email intake that picks up every invoice and expense email automatically
  • Gemini reading each invoice and receipt and assigning a category
  • Documents filed in Google Drive by vendor, year and month
  • Every record stored in PostgreSQL with a link to the original file
  • An internal app that lists every invoice in one place

How we build AI products

From promising demo to product people pay for

Most AI projects stall between the demo and production. Our process is built around that gap: real data early, quality you can measure, and costs you can predict.

  1. 1

    Define the job

    We pin down exactly what the AI should do, for whom, and what “good” looks like, using real examples from your business.

  2. 2

    Build a test set first

    Before writing the feature, we collect real questions and documents with the answers you’d expect. This becomes the yardstick for every change.

  3. 3

    Prototype on your data

    A working slice you can click through, running on your real data, so you see quality and cost long before launch.

  4. 4

    Build the product around it

    Sign-in, permissions, billing, admin screens and the rest of the app, built properly, not bolted on.

  5. 5

    Measure, then launch

    We score the feature against the test set, fix what falls short, and launch with cost and quality tracking switched on.

  6. 6

    Improve with real usage

    Real conversations show where the AI struggles. We tune prompts, data and models, and track that each change actually helps.

Cost & timeline

What affects the price

Every AI product starts with a short discovery, then a fixed price for the first release. These are the things that shape it.

What the first release includes
One sharp AI feature with sign-in and billing is a much smaller build than a full platform. We help you pick the smallest version worth launching.
Your data
Clean, well-structured data is quick to use. Scattered documents and systems need preparation before the AI can rely on them.
How accurate it must be
An internal helper can tolerate the odd miss. A customer-facing or regulated feature needs more testing and safeguards.
Model and hosting choices
Data residency rules, private hosting or specific providers change the architecture and the running costs.
Who maintains it
A handover to your developers, or an ongoing team that keeps improving it.

Industries

AI products by industry

All industries →
SaaS & Startups E-commerce & Retail Healthcare & Clinics Professional Services Education & Training

Proof

Related case studies

All case studies →
Retool · React · Python
Internal tools · Real estate

Engineering for MaverickRE’s real estate analytics

Read the case study →
React · Node.js · PostgreSQL
Portals · Healthcare

A care-plan platform for patients and providers: Nomadic Care

Read the case study →
React · Go · MariaDB
Platform · Parking

Customer and admin apps for Oparko, a Danish parking company

Read the case study →

AI Full-Stack Development FAQs

How long does an AI MVP take?

It depends on scope, so we don’t guess. After a short discovery we agree exactly what the first release includes and give you a timeline and a fixed price before any build work starts.

Which AI model will you use?

Whichever is best for each task on quality, cost, speed and where your data is allowed to go. Model calls sit behind one thin layer, so switching later is a configuration change, not a rebuild.

Who owns the code?

You do. The code lives in your repository and your cloud accounts from day one.

Can you add AI to our existing app?

Yes. We can work inside your codebase and ship AI features alongside your own team.

Can you work with our in-house developers?

Yes. We can own the AI layer while your team owns the core product, or work in your repository alongside them, following your conventions and review process.

How do you keep AI costs under control?

Smaller models for simple steps, caching for repeated questions, limits per user or plan, and a cost dashboard from day one, so a bill never surprises you.

Is our data used to train the AI models?

We use the providers’ business APIs, which don’t use your data for training by default, and we can keep data in a specific region when your rules require it.

What happens after launch?

We keep an eye on quality, cost and errors, improve the prompts as real usage comes in, and add features. Or we hand over to your team with full documentation.

Often paired with

Related services

All services →

AI Agents & LLM Apps

Assistants that answer from your documents, use your tools and hand over to a person when they should, tested before they meet a customer.

Learn more →

AI Wrapper Apps

Turn a prompt that works into a product people pay for: accounts, usage limits, billing and an admin panel on top of Claude, Gemini or OpenAI.

Learn more →

MCP Server Development

Give Claude and other AI agents safe, permissioned access to your databases, APIs and internal tools, with every call logged.

Learn more →

Let’s get your AI product in front of real users.

Bring the prototype, the spec or just the idea. On a free 30-minute call we’ll say what a first release needs, then follow up with a fixed-scope estimate.

Official Retool Agency Partner5.0 on Clutch, 8 verified reviewsBuilding software since 2015Reply within one business day

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