Outcomes
- Metric families
- Test sets
- Drift checks
- Trace model
- Monitoring plan
Service package
AI Evaluation and Reliability Program for Production Readiness
A six-to-ten-week program that turns a promising AI pilot into a measured release decision using representative test sets, review agreement, blocked-action checks, drift signals, and rollback criteria.
Measure before scaling
Starting from $60k.
Buyer fit: Teams already piloting AI but lacking measurable release gates and operational confidence.
Timeline: Typical duration: 6–10 weeks.
Scope boundary: Not a safety certification or formal compliance certification.
Sample artifact: Evaluation and reliability plan with explicit fallback criteria.
Deliverables
- evaluation rubric
- gold-answer set
- blocked-action log design
- release-gate worksheet
Sample artifact template
AI Evaluation Release-Gate Worksheet
A reliability artifact for AI pilots that need rubrics, blocked-action logs, drift checks, and release decision criteria.
Metric family
- source fidelity
- review acceptance
- blocked actions
- fallback quality
Gold-answer set
- known cases
- edge cases
- source conflicts
- unsafe requests
Release decision
- pass
- restricted pilot
- hold
- human review required
Questions answered
What this package resolves before implementation
Use the one-pager and sample artifact to decide whether this scope fits your current risk.
How is output quality judged?
What should trigger a blocked action?
What drift signals matter?
When should rollout stop?
Service detail
Who this package is for, what it covers, and how acceptance is reviewed
The page separates buyer fit, technical scope, integration, governance, client responsibilities, and proof so a technical evaluator can assess the package without relying on generic claims.
Who this is for
- Teams with an AI pilot but no agreed release criteria.
- Organizations that need to compare model, prompt, retrieval, or policy changes over time.
- Risk owners who need observable failure categories and escalation.
Who this is not for
- A certification that an AI system is universally safe or compliant.
- A one-time benchmark presented as permanent proof.
- A substitute for security testing, legal review, or operational ownership.
Systems and workflows in scope
- LLM and RAG pilots
- Classification and summarization workflows
- Reviewer apps
- Prompt and retrieval pipelines
- Human-review systems
- Model or provider migrations
Problems this package answers
- What should the system answer, refuse, or escalate?
- How do reviewers agree on acceptable output?
- What failure categories matter?
- How are prompt, model, or corpus changes compared?
- What evidence is required before release?
Technical approach
Implementation depth without unsupported guarantees
The exact architecture depends on the system, evidence, access, and risk. These sections show the normal design surface and the boundaries buyers should expect to review.
Technical design
- Representative and adversarial test-set design
- Gold-answer or expected-evidence definitions
- Factuality, citation, refusal, consistency, and blocked-action metrics
- Reviewer agreement and escalation analysis
- Regression and drift comparison across versions
- Release, rollback, and monitoring criteria
Integration and data handling
- Evaluation hooks into prompts, retrieval, model gateway, reviewer workflow, and audit data as available.
- No hidden production actions are added by the evaluation program.
- Metrics are tied to the actual workflow, not generic benchmark scores alone.
Security, review, and governance
- Sensitive test data stays within approved handling boundaries
- Red-team prompts and failure examples are access-controlled where needed
- Results distinguish public-safe examples from private evidence
- No certification or regulatory guarantee is implied
Timeline and responsibilities
What the client provides and what acceptance means
The published timeline assumes timely access to the agreed evidence, system owners, reviewers, and decision makers. Delays in access, source ownership, regulated-data handling, or review can change delivery sequence without changing the public price floor.
Client inputs
- Current pilot or workflow
- Representative questions and source evidence
- Known failures and unacceptable outputs
- Reviewers and decision owners
- Release timeline and rollback expectations
Acceptance criteria
- Approved evaluation taxonomy
- Repeatable test harness or executable specification
- Baseline result and known limitations
- Release and rollback gates
- Monitoring and review cadence
Example artifacts
- Evaluation scorecard
- Test set
- Gold-answer/evidence set
- Blocked-action log design
- Drift comparison template
- Release checklist
Package FAQ
Questions to resolve before the engagement begins
Why are demos not enough?
A demo usually shows selected success cases. Production readiness requires representative failures, refusals, evidence checks, and repeatable comparison.
What is a gold-answer set?
It is a reviewed set of expected answers, evidence, or decisions used to compare system behavior across versions.
Does the program certify compliance?
No. It creates operational evidence and controls that can support governance review, but formal certification is outside the public claim.
Next step
Confirm fit before sharing private system details.
Use the fit call for an early conversation or request assessment scope when the buyer, system, and decision are already clear.
Next step
Start with a short fit call, then scope the assessment.
The first conversation should decide whether the next step is a fixed-scope assessment, modernization blueprint, governed AI pilot, or reliability review.
Book a 20-minute fit call