AI production readiness
Move AI, RAG, and agentic workflows from prototype to production boundaries.
Practical review for production AI workflows, RAG boundaries, retrieval freshness, agentic workflow controls, human-in-the-loop review, fallback paths, telemetry, evals, and auditability.
Buyer pain
AI prototypes become production risk when boundaries stay implicit.
RAG, retrieval design, semantic search, agentic workflow boundaries, human review, and auditability need controls before AI workflows touch real users, records, or operations.
Relevant background
Domain exposure without naming confidential clients.
Applied AI workflow design from ExpenseJournal: OCR/AI extraction, semantic search, evidence-grounded retrieval, human-in-the-loop confirmation, confidence handling, telemetry, privacy boundaries, and cost/latency awareness.
What I review
Architecture decisions that need explicit ownership.
- RAG/source boundaries and retrieval freshness
- Semantic search and evidence-grounded retrieval
- Human-in-the-loop review and confidence handling
- Evals, quality signals, and telemetry
- Fallback paths and cost/latency controls
- Auditability and privacy boundaries
Deliverables
Decision-ready outputs, not generic slideware.
- AI production readiness brief
- Control and fallback checklist
- Evaluation and telemetry plan
- Boundary and ownership recommendations
Patterns from prior work
Anonymized examples of the kind of architecture pressure this work is built for.
No client names, fake outcomes, or invented metrics. These are domain patterns and pressure points from prior work.
Applied AI product work
ExpenseJournal product work applies OCR/AI extraction, semantic search, human confirmation, telemetry, evidence reporting, and privacy/supportability thinking.
What to send before a review
Useful context beats polished decks.
- Current architecture diagram or rough sketch
- Integration inventory
- Known failure modes or incidents
- Roadmap or migration plan
- Constraints: budget, timeline, vendor/platform commitments
- Decision that needs to be made
- People who need to agree
What the first conversation should clarify
Enough clarity to choose the right review shape.
- The decision being made
- Systems and teams affected
- Main risks
- Missing information
- Whether a short review is enough
- Likely artifact: decision memo, risk register, readiness brief, or boundary map
Sample artifacts
Concrete working artifacts for review and action.
Stylized examples only. No client names, fake metrics, or confidential diagrams.
Control · Risk
Signal · Threshold
Human review · Recovery
How the review works
A short path from context to recommendation.
Intake
Goals, constraints, current diagrams, backlog, operating concerns, and known failure points.
Architecture read-through
Boundaries, dependencies, contracts, data movement, failure modes, telemetry, rollback, and ownership.
Working session
Compare options, pressure-test assumptions, and align practical decision criteria.
Decision package
Memo, risk register, recommended path, and next actions with owners.
Related Insights
Further reading before a review.
Insight
Where enterprise AI programs fail first: system boundaries, retrieval design, and operational ownership
Related architecture note for teams evaluating this review area.
Insight
Modern integration architecture in the AI era: pitfalls and common patterns
Related architecture note for teams evaluating this review area.
Next step
Bring the architecture decision that needs pressure testing.
Start with a focused question, a modernization concern, or a production-readiness risk.