Eight engineering commitments.
Decisions we've already made about what serious AI work looks like - so each engagement starts from the methodology, not from scratch.
Data first, model second
Quality of an AI system is mostly the quality of its data pipeline. Models change every year; the data and the domain don't. We invest disproportionately in extraction, structure and validation - that's where durable value compounds.
Retrieval is engineering
The retrieval layer decides what the model can know. Hybrid lexical-semantic search, multi-stage ranking, learned ranking with domain priors - designed and measured per domain. Off-the-shelf retrieval rarely holds up on serious data.
Evaluation is a deliverable
We build the eval harness before the system. Golden sets, regression tests, hallucination measurement - shipped alongside the system and audited continuously. Quality is verifiable, not a matter of trust.
Right tool for the task
Fine-tune when behaviour must be internalized. Retrieve when knowledge changes faster than retraining. Prompt when effort is better spent elsewhere. Real systems combine all three, weighted by the domain.
Agentic systems with guardrails
Agents that take actions need a strict action surface, an explicit trust boundary, and an eval harness that tests multi-step behaviour - not a chatbot with API access and a prayer.
Continuous evaluation in production
The accuracy number at launch is the least interesting one. We design telemetry, drift detection and quality dashboards from day one - so the partner can see whether the system is improving or drifting.
Domain-in-the-loop
Subject-matter experts shape the data, the rubric, and the failure modes. AI does not replace the expert - it changes where the expert spends their time. Every system encodes a working partnership with the domain.
Privacy & deployment posture
Where the system runs is part of the design. On-prem, private cloud, hybrid, on-device - chosen by where the data can legally live. AI capability that creates a new exfiltration path is AI capability we don't ship.
How an engagement actually unfolds.
Six phases, sequenced. The shape varies per engagement, but the order rarely does - because each phase determines what the next one can decide.
Domain modelling
Co-developed ontology with subject-matter experts before any model is selected.
Retrieval architecture
Hybrid retrieval, structured parsing and entity linking designed up front.
Reasoning layer
Models selected to fit the reasoning load, not the other way around.
Evaluation harness
Built before the system. Golden sets, retrieval metrics, regression tests.
Production integration
Auth, audit, monitoring and operational handover, inside existing platforms.
Continuous evaluation
Production traffic feeds back into the eval. Quality measured on a recurring cadence.
Want this kind of engineering around your AI systems?
Tell us about the domain, the data, the constraints, and what the cost of being wrong looks like.