Frame the decision
Define the business decision, workflow boundary, affected parties, economic value and unacceptable failure modes.
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Delivery methodology
A repeatable path from business ambiguity to governed AI operation.
We treat AI delivery as a socio-technical systems discipline: product, data, models, tools, people, authority and evidence designed together.
Optimise the whole decision system—not the model in isolation.
A higher benchmark score does not guarantee a better business system. Production quality emerges from the interaction between task definition, context quality, model capability, tool reliability, human oversight and operational feedback.
Model the operating context
Map knowledge, data provenance, permissions, policies, systems, human roles and exception paths.
Engineer the intelligence loop
Select models, retrieval, agents, tools and orchestration patterns around the task—not around vendor fashion.
Design the evaluation system
Create representative test sets and thresholds for task quality, groundedness, policy, safety, latency and cost.
Prototype with real constraints
Run the end-to-end loop using realistic inputs, actual permissions and observable decision traces.
Govern production release
Approve data flows, access, human checkpoints, rollback, monitoring, incident response and change authority.
Operate and improve
Monitor drift, exceptions, user overrides, model/vendor changes and realised business outcomes.
MODEL STRATEGY
Capability, risk and economics determine the model.
We evaluate closed, open-weight and specialised models by task performance, deployment constraints, data boundary, latency, total cost and substitution risk.
AGENT STRATEGY
Autonomy is assigned per action.
Each tool call has a contract, permission boundary, evidence requirement, timeout, error path and approval policy. An “agent” is never a blanket permission.
EVALUATION STRATEGY
Test the system you intend to operate.
Evaluation includes deterministic checks, model-based grading with calibration, human review, adversarial cases and online operational signals.
CHANGE STRATEGY
Models change; accountability cannot drift.
Material changes to prompts, models, tools, knowledge sources or authority require impact review and proportionate re-evaluation.