A working prototype, uncertain production path
The model or workflow works well enough to be interesting, but architecture, controls, evaluation, scale, cost, or operational ownership is unresolved.
Primary entry point
Pressure-test the AI decision before you commit more time, money, or credibility.
A Private Analysis Review is a focused advisory engagement for one consequential production AI question. YakData examines the system, assumptions, evidence, tradeoffs, and failure exposure, then gives you a written decision record for what should happen next.
Independent judgment, not a discovery call.The Review is a paid engagement and can end with a decision your team executes internally.
Who it is for
Use the Review when the next decision matters more than another round of experimentation.
The best Review has a real system, a real consequence, and an unresolved decision that an executive or technical owner must make.
A disagreement the team cannot settle internally
Reasonable people disagree about model choice, architecture, data, evaluation, human review, build-versus-buy, or whether the system is ready.
A consequential commitment is approaching
The team is about to fund, deploy, expand, replace, redesign, or stop something and needs independent scrutiny before committing.
A sponsor needs a defensible decision record
The decision must be explainable beyond the AI team, with explicit evidence, assumptions, alternatives, risks, and a recommended next action.
Not designed for: generic AI brainstorming, training, vendor demos, open-ended mentoring, or code review detached from a consequential business or production decision.
How the Review works
One decision. Two conversations. Independent analysis between them.
The engagement is deliberately narrow. The objective is not to create more work. It is to determine what is actually wrong, what evidence matters, and what the team should do next.
Session 1: establish the decision
Define the business consequence, current system boundary, evidence, constraints, ownership, assumptions, and the exact production question that must be resolved.
YakData: pressure-test the system
Independent analysis focuses only on the lenses that matter: architecture, context and data, evaluation, economics, failure behavior, workflow, human control, and ownership.
Session 2: challenge and resolve
Review the findings, challenge assumptions, settle remaining uncertainties where possible, and establish the recommended decision and next action.
What gets examined
The model may not be the problem.
YakData tests the operating system around the AI, not just the model output.
Architecture
Security, latency, scale, integration, maintainability, system boundaries, and production shape.
Context and data
Data quality, business semantics, retrieval, freshness, missing context, and hidden dependencies.
Evaluation
Acceptance logic, benchmarks, failure tests, confidence, comparison baselines, and evidence quality.
Economics
Model cost, throughput, human review, engineering complexity, operating cost, and value at risk.
Operational risk
Failure modes, fallback behavior, monitoring, escalation, recovery, and release controls.
Decision ownership
Who owns the business consequence, who can stop the system, and what standard governs release.
The deliverable
A decision record your team can act on.
The written output is designed to survive the meeting. It records the reasoning so the decision can be executed, challenged, or revisited as evidence changes.
DecisionWhat YakData recommends doing now.
FindingsWhat appears true, false, weak, missing, or materially uncertain.
EvidenceThe facts, observations, tests, and system behavior supporting the conclusion.
AssumptionsWhat the recommendation depends on and what would change it.
AlternativesCredible options considered and the tradeoffs that matter.
Next actionThe smallest concrete action that resolves, reduces, or transfers the remaining risk.
Good Review questions
Bring a consequential question, not a generic request for advice.
Is this architecture actually production-ready?
Are we evaluating the AI against the right success standard?
Is model cost or system complexity going to break the economics?
What context, data, or human review is missing?
Where can this system fail operationally, and who owns that failure?
Should we build, redesign, narrow, replace, or stop?
What happens after the Review
The Review can end with an internal decision. It does not obligate you to buy more work.
Start the Review
Tell YakData what decision is carrying the risk.
Describe the system and decision in non-confidential terms. YakData will confirm fit and provide the next step.
Do not submit confidential data, credentials, proprietary source code, customer records, regulated data, or trade secrets.