The BUILD Method
A structured method for AI that ships
This is the methodology we apply to every engagement, refined across multiple projects to specifically address the failure modes that kill most enterprise AI initiatives.
B
Baseline
Define the problem worth solving and the data that exists
U
Understand
Architecture, model selection, and production-readiness
I
Implement
Iterative build with continuous evaluation against benchmarks
L
Launch
Staged rollout with human-in-the-loop controls and monitoring
D
Drive
Ongoing monitoring, drift detection, and capability
B — Phase 1
Baseline
Problem Definition
Structured workshops to separate AI-appropriate problems from those better solved by process or software.
Data Audit
Assessment of data quality, availability, labeling, and governance for the target use case.
Success Criteria
Quantified definitions of what good looks like, agreed before architecture decisions.
Feasibility Assessment
Technical and data feasibility validation with a clear go/no-go recommendation.
U — Phase 2
Understand
Architecture Design
System design with explicit tradeoffs documented and reviewed with client engineering.
Model Selection
Model evaluation against the specific task, data, and latency/cost requirements.
Integration Mapping
API contracts, data flows, and integration points with existing systems documented.
Compliance Review
Regulatory and security requirements mapped to architecture decisions before build begins.
I — Phase 3
Implement
Sprint-Based Development
Working software delivered weekly, not a single reveal at the end.
Continuous Evaluation
Automated benchmarks run against every build so quality is tracked from day one.
Test Coverage From Day One
Unit, integration, and safety tests written alongside the code.
Weekly Stakeholder Checkpoints
Regular demos so feedback shapes the system before launch.
L — Phase 4
Launch
Staged Rollout
Released to a controlled group first, expanded once performance holds up.
Monitoring & Alerting
Dashboards and alerts go live with the system, not after something breaks.
Incident Response Runbooks
Documented steps so your team knows what to do if something goes wrong.
30-Day Hypercare
We stay closely engaged through the first month in production.
D — Phase 5
Drive
Drift Monitoring
Ongoing tracking so performance doesn't quietly decay.
Performance Tuning
Regular tuning passes based on real production data.
Quarterly Business Reviews
Impact check-ins against the goals set in phase one.
Capability Expansion
A clear path to new use cases once the first one proves out.
Getting Started
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