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AI Engineering

AI applications that act, with the controls to trust them.

We build AI into real products: assistants that use your data, agents that call your tools and workflows that keep humans in charge of the decisions that matter. Every system ships with evaluation, logging and permissions, because a demo is not a product.

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What we build

What this service covers, in detail.

Agentic systems, LLM-powered applications and enterprise assistants engineered with evaluation, security and oversight.

  1. 01

    AI application engineering

    LLM-powered features inside web and mobile products, with streaming interfaces, fallbacks and cost controls.

  2. 02

    Agentic AI and multi-agent systems

    Planner, worker and reviewer agents that split a task, call tools and hand off cleanly, with retries and limits.

  3. 03

    RAG and knowledge systems

    Assistants grounded in approved documents with citations, so answers can be checked rather than trusted blindly.

  4. 04

    LLM integration and orchestration

    Model routing, prompt versioning, structured output and provider-independent abstractions to avoid lock-in.

  5. 05

    MCP and tool integrations

    Model Context Protocol servers and typed tool interfaces that give agents safe access to internal systems.

  6. 06

    AI workflow automation

    Multi-step business processes where AI handles the judgement steps and software handles the rest.

  7. 07

    Multimodal AI

    Pipelines that read images, documents and audio, then turn them into structured data your systems can use.

  8. 08

    Evaluation and observability

    Test sets, scoring, tracing and cost dashboards so quality is measured before and after every change.

  9. 09

    Security, permissions and guardrails

    Scoped credentials, approval gates, prompt-injection defences and audit trails around every agent action.

  10. 10

    AI cost and performance optimisation

    Model routing, caching and smaller specialised models, with cost per task measured alongside quality.

Example use cases

Where teams put this to work.

  • 01

    Operations copilot

    An assistant that searches orders, inventory and tickets and drafts the next action for a person to approve.

  • 02

    Document intake

    Contracts, invoices and forms read automatically, validated against rules and routed to the right team.

  • 03

    Research agent

    Gathers sources, extracts the relevant facts and produces a cited brief for an analyst to review.

  • 04

    Support deflection with handoff

    Answers routine questions from your knowledge base and escalates the rest with full context.

Technical approach

Tools we typically use here.

A starting point, not a mandate. We adapt to your existing stack and constraints.

How we choose technology
  • Python
  • TypeScript
  • FastAPI
  • LangGraph
  • MCP
  • PostgreSQL + pgvector
  • Claude
  • OpenAI
  • Open-weight models

How it works

The engagement, step by step.

  1. 1

    Use-case triage

    Rank candidate ideas by value, feasibility and risk, then choose one worth building first.

  2. 2

    Evaluation set first

    Collect real examples and define what a good answer or action looks like before tuning anything.

  3. 3

    Prototype on your data

    A working version in a narrow scope, scored against the evaluation set.

  4. 4

    Integrate with controls

    Permissions, approvals, logging and fallbacks are built around the model.

  5. 5

    Operate and improve

    Monitor drift, cost and quality, and improve from live feedback.

FAQ

Frequently asked questions

Can our data stay private?

Yes. We design around your requirements, including provider settings that disable training on your data and self-hosted models where needed.

How do you reduce wrong answers?

By grounding responses in your sources, showing citations, scoring against evaluation sets and routing uncertain cases to people.

What is an AI agent in practice?

A system that can take multi-step actions with tools, such as looking up an order and drafting a reply, inside permissions you define.

Projects

Reference projects that show this in practice.

Systems we designed as demonstrations, using sample data. Each has features, architecture and security notes.

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Related capabilities

Often combined with

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Planning a AI Engineering project?

Tell us the problem you are solving. We will reply with questions, options and a realistic next step.

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