Five service lines, one AI lifecycle.

A short, fixed-fee engagement that turns “we should be doing something with AI” into a ranked list of bets you can act on. We assess your data, your workflows and your readiness, then return a roadmap that says where AI moves your unit economics — and where it does not.

We will tell you when AI is not the right tool, which is roughly half the time. Discovery either becomes a pilot worth funding, or a clear written verdict on why this is not the moment — both are useful, and both are owned within a fixed fee.

What discovery delivers:

• AI readiness and data-maturity assessment
• Use-case inventory ranked by value and effort
• A prioritised, costed roadmap
• Security, privacy and data-residency review
• An honest “not yet” verdict when warranted

The build line. We design and ship custom AI and large language model applications scoped against a single business metric — not a feature wishlist. A pilot proves the value first; only then do we harden it into something a non-technical team can run every day.

Everything is built security-first. Your data stays inside your environment, model access is governed, and the system ships with evaluations so you can see it is working rather than take our word for it.

Typical build work:

• LLM-powered applications and copilots
• Document and language understanding systems
• Scoped pilots against one measurable metric
• Evaluation harnesses and guardrails
• Non-technical-team handoff and training

AI is only as good as the data it can reach. We build retrieval-augmented generation and knowledge systems on top of your own documents, records and institutional knowledge — so answers are grounded in your reality, not a generic model's training set.

The pipeline — ingestion, indexing, retrieval and generation — runs inside your environment. Nothing about your knowledge base leaves your control, and access is governed at the document level.

What this line covers:

• RAG pipelines over your own data
• Knowledge assistants and internal search
• Data ingestion, cleaning and indexing
• Document-level access control
• Grounded answers with source citations

The repetitive, high-volume work inside an operating business is where AI pays back fastest. We map the workflow, automate the parts that are genuinely mechanical, and keep a human in the loop wherever judgement, exception handling or accountability actually matters.

The aim is leverage, not headcount cuts — the same team handling more, with fewer errors and a clear audit trail. Every automation ships with monitoring so a failure is visible before a customer notices it.

Where automation lands:

• Document and data processing pipelines
• Triage, routing and classification
• Back-office and operations workflows
• Human-in-the-loop review steps
• Monitoring and audit trails by default

An AI system is not finished when it ships — models drift, data changes and usage grows. The managed line runs your AI in production: monitoring, evaluation, MLOps, cost control and steady iteration against the metric the system was built to move.

A retainer only begins after a pilot has produced a measurable result. You get a running system and a clear monthly view of how it is performing — not a slide deck and a handover that quietly decays.

What the retainer includes:

• Production monitoring and alerting
• Ongoing evaluation and quality checks
• MLOps, deployment and version control
• Model and inference cost management
• Iteration against the agreed business metric