Playbook

Documentation burden is where clinicians want AI most and where safety demands the most care. This playbook covers consent, capture, source-grounded drafting, clinician sign-off, safety validation, and evaluation.

A code review agent that floods pull requests with noise gets muted. One that finds real defects with evidence gets trusted. This playbook covers scope, context, analysis, noise control, evaluation, and rollout.

Contract analysis AI works when it reviews against a written playbook and shows lawyers the evidence. This playbook covers specification, extraction, deviation detection, review workflow, evaluation, and privilege controls.

Forecasts only matter when they change inventory and capacity decisions. This playbook covers data and hierarchy, probabilistic modeling, backtesting, planner integration, forecast value added, and monitoring.

Document classification routes everything downstream, so its errors compound. This playbook covers taxonomy design, labeled data, model selection, confidence thresholds, human review, per-class evaluation, and deployment.

Dynamic pricing is an optimization problem wrapped in business, legal, and brand constraints. This playbook covers objectives, elasticity models, optimization, guardrails, experimentation, and governance.

Training-serving skew and leakage are the quiet killers of production models. A feature store fixes both by defining features once and serving them consistently. This playbook covers the build.

Fraud detection combines rules, models, and analysts in a loop that adapts as fraud does. This playbook covers data, features, model and rule design, real-time scoring, review, evaluation, and governance.

Graph RAG answers questions that span many documents and hops of relationships, where chunk retrieval falls short. This playbook covers when it fits, extraction, graph construction, combined retrieval, generation, and evaluation.

A churn model is only valuable if it changes what the retention team does. This playbook covers label definition, features, training and calibration, connecting scores to actions, evaluation, and monitoring.

Shipping models and LLM applications safely means pipelines that test data, evaluate behavior, gate promotion, deploy progressively, and roll back fast. This playbook covers the build for classical ML and LLM systems.

Claims triage decides who handles what and how fast. This playbook covers intake, extraction, severity and complexity scoring, fraud indicators, routing rules, adjuster decision rights, evaluation, and controls.

Competitor monitoring produces daily digests nobody reads and misses the changes that matter. This guide covers restricting collection to legitimate public sources, distinguishing material change from marketing noise, routing findings to the people who can act, and drawing clear ethical and legal boundaries around collection.

Machine translation is cheap and product localization still fails, because translation without context, terminology control, and native review produces fluent text that is wrong. This guide covers glossary enforcement, passing the context a translator needs, tiering locales by quality requirement, and drawing a firm line around regulated content.

Research agents produce confident summaries built on whatever they found first, which is how a blog post's guess becomes a strategy assumption. This guide covers tiering sources by quality, citing every claim, surfacing contradictions instead of averaging them, and keeping synthesis and recommendation with analysts.

Public reviews are the most honest feedback a product gets and the least systematically used. This guide covers collecting across sources, handling inauthentic reviews without pretending they do not exist, extracting specific actionable issues, correlating complaints with releases, and building a response workflow that does not sound automated.

Open-text survey responses hold the insight and get skimmed because coding them by hand does not scale. This guide covers automated coding with stable themes, measuring agreement against human coders, being honest about who actually responded, keeping every theme traceable to real quotes, and leaving interpretation to researchers.

Data quality problems are discovered by the executive reading the dashboard, which is the most expensive possible detection point. This guide covers inferring expectations from history, checking freshness and volume before content, routing alerts by lineage to the owner who can fix the source, and publishing trust signals consumers can act on.

Documentation fails because it goes stale silently, not because nobody wrote it. This guide covers grounding drafts in code and configuration rather than imagination, detecting staleness against the artefacts documentation describes, triggering updates on change rather than on calendar, and keeping the reasoning that matters under human authorship.

Log platforms hold the answer to most production questions and make engineers learn a query language to find it. This guide covers generating correct queries from natural questions, framing anomalies against real baselines, correlating with deploys and changes, controlling query cost, and keeping the engineer's judgement in the loop.

User-reported phishing is a high-volume queue that security teams answer slowly, which teaches users to stop reporting. This guide covers analysing reported messages safely, producing verdicts with calibrated confidence, replying to reporters quickly, detecting campaigns across the estate, and keeping mailbox remediation under human authority.

Release notes are written for the wrong audience in the wrong language, which is why customers ignore them. This guide covers separating internal changelogs from customer notes, classifying changes by what they mean to a user, translating commit language into outcomes, treating breaking changes and security fixes with care, and keeping review fast.

Generated tests are easy to produce and easy to make worthless, because a test that asserts whatever the code currently does passes forever and catches nothing. This guide covers generating tests against intended behaviour, measuring whether they detect real defects, keeping review meaningful, and avoiding coverage theatre.

Vulnerability scanners produce more findings than any team can remediate, ranked by a severity score that ignores your environment. This guide covers enriching findings with exploitability and asset context, assessing whether vulnerable code is actually reachable, routing to the owner who can fix it, and measuring risk reduced rather than tickets closed.