Technical Reference | AI Engineering Playbooks

A practical 14-day blueprint for turning one validated AI use case into a secure, testable micro-product with measurable outcomes. Start with one painful workflow and a measurable business outcome. Keep scope tight: one persona, one trigger, one successful output. Ship with controls for quality, security, and operability from day one. Many AI projects fail because they start broad, not because the technology is weak. A micro-product approach keeps delivery disciplined and outcome-focused. This blueprint is designed for small teams that need to prove value quickly and safely. Prerequisites One clearly owned business problem. Access to subject matter experts. Basic delivery stack (repo, CI, monitoring). A named product and engineering owner. 14-day plan Days 1-2: Define outcome and scope Choose one workflow with repeated manual effort. Define baseline time, error rate, or cycle time. Write acceptance criteria for success. Days 3-4: Design the minimal archite...

A practical list of AI use cases for insurance operations, underwriting support, claims, and service workflows. Insurance teams do not need moonshot AI programmes to create value. They need targeted workflows with clear controls. 10 practical use cases Submission triage and document completeness checks. Broker email summarisation with action extraction. Underwriting note drafting from structured risk inputs. Claims intake classification and routing support. Policy wording comparison assistance. Renewal packet preparation and variance summaries. Internal knowledge retrieval for servicing teams. Meeting preparation briefs for account and placement teams. Escalation risk early-warning summaries. Portfolio-level trend summaries for leadership reviews. Delivery pattern that works Start with one workflow and one business metric. Add human-in-the-loop review for all externally visible outputs. Capture failure cases weekly and retrain prompt/process contracts. Exp...