AI Development
Most AI projects stall between the demo and the deployment. We specialise in the unglamorous half — grounding, evaluation, latency, cost control and the operational scaffolding that keeps an AI system trustworthy once real users touch it.
What this includes
Retrieval-augmented generation
Hybrid semantic and keyword retrieval over your own documents, with reranking and citation tracking so every answer is traceable.
Autonomous agents
Tool-using agents that query systems, take actions and know when to escalate to a human.
Evaluation harnesses
Golden datasets and automated scoring so you can prove a change improved quality instead of guessing.
Inference cost engineering
Model right-sizing, caching layers and context budgeting that cut spend without visible quality loss.
Multimodal pipelines
Document, image and audio understanding wired into a single retrieval and reasoning layer.
Guardrails and safety
Prompt-injection defence, output validation, PII handling and audit trails.
What you end up with
- Answers grounded in your documentation, with citations
- Measurable retrieval quality before full build commitment
- Predictable per-conversation inference cost
- A system your own engineers can extend
Tools we reach for
Frequently asked
Do we need our own model?
Almost never. Fine-tuning solves style and format problems; it rarely solves knowledge problems. Retrieval is usually cheaper, faster to ship and easier to update.
How do you stop hallucination?
By treating it as a retrieval failure. If the right passage reaches the model with a clear instruction to refuse when context is missing, fabrication drops sharply. We measure refusal behaviour explicitly.
Can this run on our own infrastructure?
Yes. We deploy to your cloud, your VPC or fully on-premise, including self-hosted open-weight models where data residency requires it.
Other work in this practice
RAG & Knowledge Systems
Turn scattered documents into a knowledge layer your AI can answer from — accurately, with sources.
Read moreAI Agents & Automation
Agents that use real tools, take real actions, and know their limits.
Read moreLLM Integration
Add language model capability to an existing product without destabilising it.
Read moreComputer Vision
Extract structure and meaning from images, documents and video streams.
Read moreMLOps & Model Infrastructure
The deployment, monitoring and evaluation layer that keeps AI systems honest over time.
Read moreLet's talk about what you're building
Tell us the problem. We'll tell you honestly whether AI is the right tool, and what it would take.