📅 Schedule an AI-in-QA Strategy Session

Five inputs we ask for in advance so we spend the session on decisions, not introductions.

• Current inspection volume. Units inspected per shift, broken out by line, with current first-pass yield and defect categories.
• Top three pain points. Where human inspectors spend the most time, where errors slip through, and where audits flag issues.
• Existing data sources. MES, SCADA, vision systems, ERP exports — even messy spreadsheets count.
• Regulatory scope. ISO 9001, IATF 16949, FDA 21 CFR Part 11, GMP, or sector-specific mandates that apply to your output.
• Stakeholder map. Names of the 3–5 people who must approve a pilot and the 1–2 who will run it day-to-day.

A tight, working format. No slide deck. Just whiteboarding, prioritization, and a roadmap on the wall by the end.

1. 0:00–0:15 · Context calibrationWe walk your shop floor narrative, confirm goals, and align on what "win" looks like in 6 months.
2. 0:15–0:40 · Use case mappingWe surface 8–12 candidate AI use cases — vision-based defect detection, predictive QA, document automation — and score each on impact vs. effort.
3. 0:40–1:00 · Compliance & data auditWe map your regulatory obligations to data lineage, model explainability, and audit trail requirements.
4. 1:00–1:20 · Phased roadmap draftingWe sequence three phases: 30-day pilot, 90-day rollout, 6-month scale — with measurable milestones for each.
5. 1:20–1:30 · Commitments & next stepsYou leave with a one-page roadmap, an owner for each phase, and a follow-up date locked.

How we score AI opportunities so you invest in the ones that actually move yield, cost, or compliance.

• Defect cost per escape. Use cases protecting against $10K+ field failures rank above cosmetic catches.
• Data readiness score. Cases where labeled data already exists move to Phase 1; cases needing collection move to Phase 2.
• Time-to-value under 60 days. Pilots must show measurable lift in 8 weeks or they get cut.
• Operator adoption risk. If line workers can't trust or override the system in under 30 seconds, redesign before deploying.
• Audit defensibility. Every model decision must produce a logged, human-readable rationale for ISO and customer audits.

The eight questions every QA leader should be able to answer before signing off on an AI system in production.

• Model versioning. Can you retrieve the exact model that approved a part shipped 14 months ago?
• Training data provenance. Every image, sensor reading, and label traceable to its source line and date.
• Drift monitoring. Automatic alerts when production data diverges from training distribution by more than 5%.
• Human-in-the-loop thresholds. Clear confidence cutoffs that route ambiguous decisions to a qualified inspector.
• Override logging. Every operator override captured with reason code, timestamp, and outcome.
• Cybersecurity posture. Edge devices hardened to IEC 62443 standards, with documented patching cadence.
• Validation protocol. Written IQ/OQ/PQ procedures specific to AI components, not just the host equipment.
• Sunset plan. A documented procedure for retiring a model and reverting to manual inspection if needed.

A template of what you walk away with — a one-page plan that travels well to your CFO, your auditor, and your operators.

No black-box "we'll get back to you." Within five business days, you receive a structured set of deliverables.

• Annotated roadmap PDF with prioritized use cases, owners, milestones, and target metrics.
• Compliance gap memo listing each finding, its risk level, and a recommended remediation step.
• Data readiness scorecard rating each candidate use case on a 1–5 scale across five dimensions.
• Vendor-neutral tooling shortlist matched to your stack — we don't sell software, so the recommendations stay honest.
• 30-minute follow-up call scheduled at two weeks to pressure-test the plan once your team has lived with it.