Services · Artificial intelligence · Custom AI and LLM development

AI features built into your product, not bolted on.

We handle custom AI and LLM development for companies that want AI inside their own product: search over their documents, assistants that draft and summarise, and features that read files users upload. We design the feature, choose the model and build it into your codebase.
Model choice and running cost are part of the design from day one, so the feature still makes sense when usage grows.
Get in touchAll AI services
Built for
Founders, product owners and CTOs who want a working AI feature in their product rather than a demo.
Typical timeline
A first feature such as document search or a drafting assistant typically takes six to ten weeks from design to release.
What you own
You own the source code, prompts, retrieval pipeline, evaluation data and any fine tuned model weights we produce.
Rated
4.7 / 5 from 23 Google reviews

200+ companies across the US and the UAEFrom a first store to a listed developer

Modon
Majid Al Futtaim
HP
Belkin
Dulsco Group
OneClickDrive
Hanayen
HMS
Clocksprings
Ghezlan

What you get

What we build

Each of these is a feature your users touch, built with the same care as the rest of your product.
Explain your project

  • Retrieval over your content

    We build retrieval augmented generation so answers come from your documents, policies or product data, with a link to the source. Content is chunked, embedded and stored in pgvector, Pinecone or your existing database.

    Users get answers they can check rather than confident guesses.

  • Document understanding

    Features read contracts, invoices, forms and reports, pull out the fields you need and flag what is missing. Low confidence results are sent for review instead of saved silently.

    Your users stop retyping information that is already in a file.

  • Generative features in apps

    We add drafting, rewriting, summarising and structured generation to web and mobile apps. Outputs follow your tone rules and can be edited before they are used.

    Your product does more of the writing work its users do every day.

  • Fine tuning where it earns its place

    We fine tune a model only when prompts and retrieval cannot reach the quality you need, or when a smaller tuned model cuts running cost. We test the tuned model against the base model on the same cases.

    You pay for training only when the test results justify it.

  • Model choice

    We compare OpenAI, Claude, Gemini and open models such as Llama and Mistral on your real tasks. The comparison covers quality, speed, cost and where the data is processed.

    The decision is based on your cases, not on a vendor pitch.

  • Cost control

    We use caching, smaller models for simple steps, token limits and per user caps. Spend is tracked per feature so you can see what each one costs to run.

    Running cost stays predictable as more users adopt the feature.

  • Product interface

    We design how the AI feature appears: streaming responses, citations, edit and retry controls, and clear states when the model is unsure.

    Users understand what the feature did and trust it enough to use it.

  • Quality checks

    Every feature ships with an evaluation set and automated checks that run before each release. Your team can add cases when they find a weak spot.

    Quality is measured before each release instead of argued about after it.

Who this is for

When custom development is the right call

Off the shelf assistants cover general tasks. Custom work makes sense when the feature depends on your data or your product.
01

Your knowledge lives in thousands of documents

A retrieval feature lets staff or customers ask questions and get answers with sources, instead of searching folders.

Permissions follow your existing access rules.

02

You are launching an AI product

We build the core AI pipeline, the evaluation setup and the product screens, so your launch is a working product rather than a prototype.

Suits funded startups and new business lines.

03

Your users upload files you process by hand

Document understanding pulls the data out and routes doubtful cases to a reviewer, keeping a person in the loop.

Works for scans and digital files.

From idea to released feature

  1. 01Feature designWe agree what the feature does, who uses it, what good output looks like and what it must never do.
  2. 02Model trialWe test two or three models on your real examples and compare quality, speed and cost.
  3. 03BuildWe build the pipeline, interface and checks inside your codebase, with tests and logging.
  4. 04ReleaseWe release behind a flag, watch quality and cost, and tune before opening to all users.

Technology

What we build this with.

Models are chosen per use case for quality, cost and where your data is allowed to go. The systems around them run on the stack below.

01Web

  • React
  • Next.js
  • TypeScript
  • Node.js
  • Laravel
  • WordPress

02Data and cloud

  • PostgreSQL
  • MySQL
  • MongoDB
  • AWS
  • Azure
  • Docker

Questions about custom AI development

Usually not at the start.

Most features reach good quality with clear prompts and good retrieval. Fine tuning helps when you need a very specific style or format, or when a smaller tuned model can replace a larger one to cut cost. We test before recommending it.

Services that usually go with this one.

AI agent developmentAgents that do real work, with a person signing off.MLOps and AI platformsKeep your models working after launch day.Custom software and business systemsThe system that replaces the spreadsheet nobody dares change.

Tell us what the business needs. We will tell you what to build first.

Schedule a free consultation and get a written recommendation, with costs, before you commit to anything.

Get in touch

PROVEN RESULTS

4.7 / 5from 23 Google reviews