Our process
Frame. Architect. Build. Deploy. Govern.
Five stages, each producing artifacts the next depends on — and a continuous chain of evidence your auditors, validators, and regulators can inspect at any point.
Stage 01
Frame
Define the decision, the rules, and the evidence — before any code.
Every engagement begins with a structured framing phase. We identify the specific business decision the model will support, the population it will affect, and — critically — the full regulatory context it operates in. Which rules apply? Which internal policies? What will your model risk or clinical governance committee expect to see?
The output is a framing document that reads like a contract with your future auditor: the problem statement, the applicable regulatory obligations mapped to concrete technical controls, the success metrics (statistical and operational), the fairness and safety constraints, and the evidence artifacts each later stage must produce.
What this stage produces
- Problem and decision definition
- Regulatory obligation map with control assignments
- Success criteria and constraint register
- Data availability and quality assessment
Stage 02
Architect
Design the pipeline, the model approach, and every control point.
With requirements fixed, we design the full system: data ingestion and validation, feature engineering, model candidates, and the monitoring and governance layer that will surround them in production. Each architectural decision is written down with its rationale and traced back to a requirement from the framing phase.
This is also where we make deliberate choices about interpretability. In many regulated use cases, a well-engineered interpretable model beats a marginally more accurate black box — because the black box will cost you months in validation and years in maintenance. We put that trade-off in front of you explicitly, with numbers.
What this stage produces
- System architecture and data flow design
- Model approach selection with documented rationale
- Interpretability and explainability plan
- Control point specification (validation gates, human review thresholds)
Stage 03
Build
Develop with full lineage. Validate alongside, not after.
Development happens inside a disciplined environment: versioned datasets, reproducible training runs, and experiment tracking from the first prototype. Nothing that influences the final model goes undocumented — every feature transformation, every hyperparameter search, every excluded data segment and the reason for its exclusion.
Validation is not a phase that follows building; it runs in parallel. Fairness testing, stability analysis, sensitivity checks, and challenger comparisons happen continuously, so problems surface when they are cheap to fix rather than after stakeholders have anchored on a result.
What this stage produces
- Versioned, reproducible training pipeline
- Experiment log with full decision history
- Validation report: performance, stability, fairness, sensitivity
- Model documentation pack for internal review
Stage 04
Deploy
Release behind guardrails, with monitoring live from day one.
Deployment in a regulated environment is a controlled event, not a push to production. We stage rollouts — shadow mode first where feasible, then limited exposure, then scale — with pre-agreed criteria for advancing or rolling back at each step.
Human oversight mechanisms designed in the Architect phase come alive here: confidence thresholds that route cases to reviewers, override workflows that feed back into the audit log, and alerting tied to the metrics your governance committee actually tracks.
What this stage produces
- Staged rollout plan with go/no-go criteria
- Live monitoring dashboards and alert definitions
- Human review and override workflow
- Operational runbook for your team
Stage 05
Govern
A living audit file and a monitoring system that outlasts the engagement.
Models decay. Populations shift, behaviors change, upstream data quietly drifts. The Govern stage establishes the permanent apparatus that catches this: drift detection on inputs and outputs, scheduled revalidation with defined triggers, and periodic model risk reporting formatted for your governance forums.
We also close the loop on evidence. Everything produced across the previous four stages is consolidated into a living audit file — indexed, versioned, and maintained as the system evolves. When an examination arrives, the preparation is already done.
What this stage produces
- Drift detection and revalidation schedule
- Model risk reporting templates and cadence
- Consolidated, versioned audit file
- Handover and training for internal ownership
Curious how this maps to your project?
Send us the outline of what you are trying to build. We will walk you through what each stage would look like for your specific regulatory context.