Businesses need deterministic outcomes. AI produces stochastic outputs. We bridge this gap by engineering deterministic systems around stochastic cores.
Software engineering spent decades building the practices that make production systems reliable. Testing frameworks. CI/CD. Observability. Regression suites. Deployment validation.
AI doesn't have these yet. Not because teams haven't tried — because stochastic systems require fundamentally different approaches. You can't unit-test a system whose outputs are probabilistic. Traditional QA doesn't apply when the same input can produce different outputs.
Evidence-Based AI Engineering is this missing discipline. A systematic way to specify what an AI system must do, produce evidence that it does it, and close the gap when it doesn't.
LAYER 1 — SUCCESS CRITERIA
Not abstract "accuracy" — specific, measurable definitions of success for each scenario the system will face in production.
Scenario coverage. Real production cases combined with synthetic edge cases, known failure triggers, and boundary conditions.
Binary pass/fail. Each scenario has exactly one acceptable outcome. Results can't be gamed.
LAYER 2 — PROOF SYSTEM
A growing body of evidence that validates the system against every defined scenario. This is the primary deliverable, not the AI system itself.
How exactly does it fail, and what's the blast radius? Categorizing failure modes is often more persuasive to stakeholders than showing a 99% pass rate.
Cost of AI failures in production — wrong decisions, customer churn, regulatory exposure, reputational damage. The CFO-level argument.
LAYER 3 — ITERATION ENGINE
Full observability. Every run instrumented with metrics, logs, and traces.
Replayable failures. Any failure can be reproduced on demand. Controlled experimentation, not guesswork.
Change, test against the full scenario suite, measure, repeat. The gap between current reliability and target reliability closes measurably with each cycle.
Graceful degradation by design. The system detects its own uncertainty and falls back — to a human, to a safe default, to an explicit "I'm not confident" response.
Most AI development is one-shot: build, test, ship, hope.
Evidence-Based AI Engineering is compounding.
THE FEEDBACK FLYWHEEL
Every failure makes the system more reliable, not less. The evaluation suite is a living artifact that grows with the system. Six months after deployment, you have a more robust system than the day you launched — not because someone manually monitored it, but because the methodology is architecturally self-improving.
This is what you keep after the engagement ends. Not a dependency on a consultant — a self-sustaining engineering discipline your team owns.
The result: evidence-backed automation with known boundaries, measurable performance, and fast root-cause analysis when something breaks.
A 30-minute conversation about your system, your reliability gaps, and whether this methodology applies.