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Emerging Technology · AI-Powered Applications

Software with AI built in, where AI actually helps.

We design and build applications where AI performs a defined job inside a product: summarising, drafting, classifying, searching or extracting. Inputs are controlled, outputs are validated and people stay in the loop where it matters. It is a product build, not an off-the-shelf chatbot.

Defined AI taskInput and output designValidationHuman review

Why AI features disappoint

AI added without a clear job

  • “Add AI” with no task defined

    The feature is impressive in a demo and unreliable in real use.

  • Unchecked output

    Wrong or invented answers reach users because nothing validates them.

  • No fallback

    When the model is wrong or unavailable, the user is stuck.

What is included

Application and AI capability, together

01

Use-case definition

The specific task, users, inputs and what a good output looks like.

02

Application build

Web or mobile application, backend and data storage.

03

AI integration

Model selection, prompts and retrieval from your own content where needed.

04

Validation and guardrails

Input limits, output checks, source grounding and human-review steps.

05

Evaluation

Test cases to check quality before and after release.

06

Monitoring and improvement

Logging, feedback capture and cost tracking.

Deliverables

What you receive, and the limits

You receive

  • Use-case specification
  • Working application
  • AI integration with prompts and configuration
  • Evaluation set and results
  • Safety and review workflow
  • Documentation and handover

Boundaries

  • AI output can be wrong, so we design for review and do not claim accuracy figures without testing.
  • Model usage costs are yours and depend on volume.
  • Sensitive data handling is agreed before build.
  • Not a replacement for professional advice in regulated areas.

How it works

From a defined task to a monitored release

We test with real examples before building more.

  1. Define the task

    Users, inputs, good and bad outputs.

  2. Prototype the AI step

    Quality tested on real examples.

  3. Build the application

    Interface, backend and data.

  4. Add safeguards

    Validation, review and fallback.

  5. Release and monitor

    Feedback and costs tracked.

Questions

Common questions

How is this different from AI Solutions & AI Agents?

That page covers AI strategy and agents in your operations. This one is building an application product around an AI capability.

Can it use our documents?

Yes, through retrieval, with access controls agreed in advance.

How accurate will it be?

We test on your examples and report results. We do not promise a figure up front.

Which models?

We choose based on task, cost and data requirements, and avoid locking you in where we can.

Have an AI product idea?

Describe the task and who does it today. We will reply with how we would test it.