Agents, models & retrieval,
engineered for production.

We build four production AI capabilities: agentic frameworks that plan, call tools, and complete real multi-step workflows; custom ML models for classification, prediction, scoring, and ranking; RAG systems that retrieve and ground answers from your own data; and fine-tuned models adapted to your domain and tone. Every system is built to enterprise standards - observability, security review, audit trails, and monitoring on day one.

An agentic framework is a system where an AI model plans, calls external tools, and completes multi-step tasks autonomously - not just answering a single prompt. We build these on production rails: human-in-the-loop checkpoints, guardrails, error recovery, and full audit trails so AI-generated actions are always explainable and reversible. The result is AI that completes real business workflows, safely, at scale.

A standard LLM draws only on training data - it cannot access your documents or databases, and hallucinates when it doesn't know something. A RAG system retrieves relevant content from your own data at inference time and grounds every answer in that evidence. We build RAG with hybrid retrieval, semantic and keyword search, answer citations, permission-aware access control, and evaluation harnesses that measure accuracy continuously.

Yes. Our engagements are designed around enterprise security and compliance from day one - not retrofitted later. We work within your existing cloud environment (AWS, Azure, GCP), respect data residency requirements, and produce artefacts for security review: architecture documentation, threat models, and model cards with bias and performance metrics.

Scope varies by capability. A focused RAG deployment or single-agent workflow typically runs 6 to 12 weeks. A full agentic framework with multiple integrated tools, fine-tuned models, and production MLOps infrastructure can run 3 to 6 months. We scope engagements precisely in a discovery phase - you receive a fixed-scope statement of work before we start.

Prompt engineering optimises how you instruct a general-purpose model - faster and cheaper to implement, but limited by what the base model knows and how it behaves. Fine-tuning adapts the model's weights on your labelled data, which produces measurably better performance on specific tasks, consistent tone, and correct domain vocabulary. We use fine-tuning when prompt engineering hits a ceiling, and we benchmark every fine-tuned model against the base to prove the uplift.