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Generative AI, Grounded & Governed

RAG assistants, autonomous agents and copilots built on your data — with guardrails, evaluation and human oversight.

Overview

Move GenAI from demo to dependable

Impressive demos are easy; trustworthy GenAI is not. We engineer retrieval, guardrails, evaluation and observability into every workflow — so your assistants are accurate, safe and aligned with your policies.

  • RAG grounded in your own knowledge
  • Autonomous agents & tool use
  • Guardrails, evaluation and red-teaming
  • Human-in-the-loop review and audit trails
Discuss Your Project
Capabilities

What's Included

Everything you need, delivered by a senior, security-first team.

RAG Assistants

Answer from your docs, accurately and with citations.

AI Agents

Multi-step automation with tools and oversight.

Coding Copilots

Accelerate developers within your guardrails.

Guardrails

Policy, safety and PII controls.

Evaluation

Measure quality and catch regressions.

Observability

Trace prompts, costs and outcomes.

Generative AI that works in production — with guardrails

Impressive AI demos are easy; reliable, safe, grounded AI in production is hard. We build RAG assistants, copilots and autonomous agents grounded in your own data, with the retrieval quality, evaluation and guardrails that make them accurate, safe and trustworthy.

What we build

  • RAG assistants — chatbots and copilots grounded in your documents and data, with citations.
  • AI agents — autonomous, tool-using agents for real workflows.
  • Guardrails — input/output filtering, prompt-injection defence and PII handling.
  • Evaluation — systematic testing of accuracy, safety and regressions.
  • Security & governance — data controls and auditability for AI systems.

Grounded, evaluated, safe

We focus on retrieval quality and rigorous evaluation — the difference between a flashy prototype and an AI product your users and auditors can rely on. We build on the latest, most capable models including Claude.

Process

How the Engagement Works

Discover

We assess your current state, goals and constraints.

Design

A secure, costed plan with clear milestones and SLAs.

Deliver

Iterative, auditable execution with security built in.

Operate

Ongoing optimisation, monitoring and support.

FAQ

Frequently Asked Questions

RAG (Retrieval-Augmented Generation) grounds a large language model in your own data, so answers are accurate, current and citable rather than made up. It is the standard architecture for reliable, domain-specific AI assistants and copilots.

Through grounding (RAG), retrieval quality, output guardrails, prompt-injection defences, PII handling and systematic evaluation. We treat safety and accuracy as engineering requirements with measurable tests, not hopes.

We build on the leading, most capable models — including Anthropic's Claude — and choose per use case based on quality, latency, cost and data-handling requirements. We are not locked to a single vendor.

Yes. Many AI demos fail in production due to poor retrieval, no evaluation and missing guardrails. We add the retrieval quality, evaluation harness, guardrails and observability that make AI reliable at scale.

Ready to get started with Generative AI & Agents?

Book a free consultation — we'll map a secure, practical path forward.

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