Knowledge systems
Secure retrieval, document pipelines, provenance, citations, and answer evaluation over enterprise knowledge.
AI systems development
Prototype intelligence.
Engineer trust.
We design the data, retrieval, tools, controls, evaluations, and feedback loops that turn an AI concept into a dependable operating capability.
AI production stack
EXPERIENCE
Intent, task, and human workflow
INTELLIGENCE
Models, retrieval, memory, and tools
CONTROL PLANE
Evaluation, policy, review, and audit
FOUNDATION
Identity, data, integration, and observability
The production reality
A good answer is only one part of the requirement.
Production AI must also know when it lacks evidence, respect authority boundaries, expose its assumptions, recover from tool failure, and give operators a way to inspect and improve behaviour.
Evidence before fluencyGround decisions in the data and retrieval paths the system is allowed to use.
Controls before scaleDefine evaluation, review, and escalation before increasing traffic or autonomy.
What we build
High-value systems with explicit operating boundaries.
Agentic workflows
Tool-using systems with constrained actions, human approval gates, state, and recoverable execution.
Decision support
AI-assisted analysis and recommendations with evidence, uncertainty, comparison, and accountable review.
AI evaluation harnesses
Task suites, quality thresholds, regression tests, adversarial cases, and human-rated feedback loops.
Production observability
Traces, outcome metrics, cost, latency, refusal analysis, drift signals, and operator-visible failure states.
AI governance foundations
Data boundaries, access policy, review workflows, audit evidence, model-change controls, and ownership.
Delivery approach
Prove the riskiest path, not the easiest demo.
A production slice should touch the real data, integration, policy, user, and failure conditions the business will depend on.
01 · FRAME
Define the decision and its risk
Identify the user action, business outcome, unacceptable failure, data boundary, and human authority.
02 · MEASURE
Define evidence before tuning
Create a representative evaluation set and agree on quality, cost, latency, and escalation thresholds.
03 · BUILD
Implement one vertical path
Connect the system to realistic inputs, tools, and user workflow with observable decisions and constrained authority.
04 · OPERATE
Close the feedback loop
Monitor outcomes, review failures, manage model and prompt changes, and make improvements visible to operators.
Useful starting points
Strong AI candidates are specific.
Good fit
- Knowledge-intensive work with reviewable sources
- Structured decisions with useful test cases
- Tool-using workflows with bounded authority
- Repetitive analysis where feedback is available
Not yet ready
- “Add AI” with no business outcome
- Irreversible actions without approval controls
- Unclear data ownership or access boundaries
- A demo treated as proof of production value
Start with the system question
What must this AI system know, decide, and never do?
Share the use case, current systems, and the risk you are most concerned about. We can help shape a production-ready first step.