AI & Machine Learning Development
Production AI — not just demos.
We build AI-powered products and integrate LLMs, RAG pipelines, computer vision, and custom ML models into software that ships to real users and handles real load.
Why teams choose us
Production-Ready AI
We build AI features that handle real traffic — with proper latency budgets, fallback strategies, cost controls, and evals to prevent regressions.
LLM-Agnostic
We architect for model portability. Switch between OpenAI, Anthropic, Mistral, or your own fine-tuned model without rewriting your product.
Measurable Impact
We define success metrics before we start — latency, accuracy, cost per query — and build evaluation pipelines to track them in production.
How we work
A clear, repeatable process — no surprises.
AI Readiness Assessment
We evaluate your data, use case, and existing stack to determine the right approach — fine-tuning, RAG, prompt engineering, or custom model training.
Prototype & Evaluate
We build a working prototype in 1–2 weeks and run structured evals against your ground-truth data before committing to production architecture.
Production Architecture
We design the retrieval layer, vector database, orchestration, caching, and monitoring stack for production scale and cost efficiency.
Launch & Monitor
We deploy with guardrails — output validation, PII filtering, cost caps — and set up dashboards to track quality and costs over time.
Tech stack
What we build
Common use cases and project types.
- Internal knowledge bases and enterprise search
- AI-powered customer support agents
- Document intelligence and extraction
- Code generation and review tools
- Recommendation engines
- Computer vision and image classification
Frequently asked questions
What's the difference between RAG and fine-tuning?
RAG (Retrieval-Augmented Generation) lets an LLM answer questions using your documents without training a new model — it's faster, cheaper, and keeps knowledge up to date. Fine-tuning trains the model on your data to change its behaviour or writing style. We recommend starting with RAG for most use cases.
How much does an AI integration cost?
A focused RAG pipeline or LLM integration typically costs £8,000–£25,000 depending on complexity, data volume, and latency requirements. Custom model training is more involved and we scope it case by case.
Can you work with our proprietary data?
Yes, and data privacy is a first-class concern. We architect solutions using private cloud deployments, on-premise models, or zero-data-retention API agreements so your data never leaves your control.
How do you ensure the AI doesn't hallucinate?
We implement output validation, citation requirements, confidence scoring, and human-in-the-loop checkpoints depending on the risk level of the use case. We also build eval harnesses to catch regressions before they reach users.
Ready to start?
Tell us about your project and we'll send a detailed estimate within 24 hours.