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Service 05

AI Development Services

AI is useful when it is attached to a real task. We build it into the workflow where the work actually happens — reading the documents, answering the question, drafting the reply — and measure whether it holds up before it goes live.

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  • Use case
  • Data
  • Evaluation
  • Production

What we build

6 areas

Inside ai & intelligent systems.

AI assistants

Assistants grounded in your own documents and data, answering questions with a source rather than a guess.

Document processing

Reading invoices, contracts, forms and scans, extracting structured fields, and routing them into your systems.

Intelligent search

Search that understands the question rather than matching keywords, across content spread over several systems.

Recommendations

Surfacing the next relevant product, article or action based on behaviour and context.

Predictive analytics

Forecasting demand, churn or load from your historical data, with the confidence of the prediction shown honestly.

Evaluation and guardrails

Test sets, quality checks, fallbacks and human review points, so an AI feature behaves predictably in production.

When people come to us
  • Your team answers the same questions from the same documents daily
  • Unstructured data arrives faster than anyone can process it
  • You want to test whether an AI feature is worth building
  • An existing AI prototype needs to become a real product
Typical stack
  • Python
  • LLM APIs
  • Vector search
  • Machine learning
  • Node.js
  • PostgreSQL
Chosen per project. If you already run something specific, we work with what you have rather than replacing it for its own sake.

How it runs

5 stages

From first conversation to live software.

  1. 01

    Discover

    Understand your business, users and goals. We map the process as it runs today and agree what success looks like.

    • Requirement notes
    • Process map
    • Scope and estimate
  2. 02

    Design

    Define the experience, the architecture and the product direction — before a line of production code is written.

    • Screen designs
    • Data model
    • Technical plan
  3. 03

    Build

    Develop the product in short cycles using modern engineering practice, with something reviewable at the end of each one.

    • Working builds
    • Code review
    • Automated tests
  4. 04

    Launch

    Test, deploy and make the product available to users, with monitoring in place from the first day it is live.

    • Deployment
    • Monitoring
    • Handover docs
  5. 05

    Grow

    Improve, automate, scale and evolve — guided by what real usage tells you rather than what we assumed.

    • Usage insight
    • Enhancements
    • Support

Questions

2 answers

About ai & intelligent systems.

We start small. A focused proof of concept on your real data, measured against examples where you already know the right answer, tells you whether the idea is worth building before the budget is committed.

That is decided with you up front — which provider is used, what is retained, and what stays inside your own infrastructure — and written into the design.

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Need ai & intelligent systems?

Send a short description of what you're trying to achieve. We'll reply with questions, then an approach and an estimate.
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