Guias
Manuais de implementação
Sequências estruturadas para transportar IA com segurança desde o piloto até operações de longo prazo.
Guias
Pilot to production
A staged path from prototype workflow to governed production service.
- Operating owner assigned before rollout expansion.
- Release gates linked to measurable quality checks.
- Client-side handoff package complete before closeout.
- Scope workflowMap actors, systems, handoffs, and irreversible actions for one production-shaped workflow.
- Define quality gatesConvert expected behavior into measurable release checks tied to business risk.
- Deploy controlled pilotRun shadow, canary, and staged rollout with explicit escalation and observability controls.
- Operational handoffTransfer service ownership with runbooks, alerting norms, and change-management cadence.
Guias
Model operations control plane
A playbook for operating model routing, runtime incidents, fallback drills, and release confidence as one managed service.
- Runtime incidents are triaged by failing layer, impact, fallback state, and owner.
- Fallback policy is rehearsed against provider, quality, latency, cost, and safety failures.
- Model and prompt releases are connected to operating telemetry and rollback evidence.
- Define routing policyMap workflow classes to primary models, fallback paths, cost budgets, latency targets, and review thresholds.
- Instrument runtime healthConnect cost, latency, quality, fallback, provider health, and reviewer signals to one operating view.
- Triage incidentsClassify incidents by failing layer, customer impact, release version, fallback state, and owner.
- Drill fallbackRehearse provider outage, latency spike, cost runaway, unsafe output, and model regression scenarios.
Guias
Reasoning quality control
A playbook for making high-stakes AI reasoning measurable, reviewable, and safe to promote.
- Verifier chains separate answer generation from answer approval.
- Decision rubrics produce comparable quality evidence across model and prompt revisions.
- Escalation policies are triggered by reasoning quality signals, not reviewer intuition alone.
- Define decision classesClassify outputs by business impact, evidence requirements, policy sensitivity, and action readiness.
- Build verifier chainCreate checks for source support, policy fit, missing assumptions, contradictions, and reviewer escalation.
- Run counterfactual suiteEvaluate reasoning behavior against case variants that alter assumptions, constraints, and evidence state.
- Review quality telemetryUse rubric results and verifier outcomes to decide whether to promote, revise, or block workflow changes.
Guias
Retrieval hardening
Improve source quality, freshness, and citation confidence before broad rollout.
- Source freshness tracked as an SLO by source class.
- Citation confidence exposed in review and ops dashboards.
- Drift monitoring alerts operationalized before scale-up.
- Inventory sourcesBuild a source catalog with ownership, update cadence, permissions, and data criticality.
- Define freshness SLOsSet freshness targets per source class and business use case.
- Stress-test citationsProbe citation coverage on edge prompts and low-context requests.
- Monitor driftTrack retrieval drift and source regressions with escalation automation.
Guias
Retrieval operations operating model
An operating model for keeping retrieval systems accurate, permission-safe, fresh, and useful after launch.
- Knowledge sources have named business and technical owners.
- Retrieval drift is detected through probes, reviewer signals, and source health metrics.
- Remediation work is routed to the correct layer instead of defaulting to prompt changes.
- Assign source ownershipCreate a source register with owner, authority level, freshness target, permission contact, and retirement rule.
- Score source qualityEvaluate source authority, conflict risk, freshness, ingestion reliability, and production readiness.
- Monitor retrieval driftRun probes and reviewer-signal analysis to detect degraded source support or permission behavior.
- Route remediationClassify drift by failing layer and assign remediation to source, ingestion, ranking, permission, prompt, or policy owners.
Guias
Support automation rollout
Deploy ticket triage and drafting with risk controls and escalation policies.
- Queue segmentation implemented by impact and SLA risk.
- Draft acceptance and escalation rates tracked per queue.
- Support managers receive ticket packets with full context.
- Queue segmentationPartition support queues by complexity, urgency, and downstream blast radius.
- Draft + reviewDeploy automated drafts with reviewer controls and response-quality feedback loops.
- SLA escalation policiesEncode SLA breach signals and escalation trees as executable policy.
- Quality telemetryAttach operational metrics to queue health and resolution quality decisions.
Guias
Workflow exception control
A playbook for classifying, routing, measuring, and reducing exceptions in AI-assisted workflows.
- Exception classes have named owners, fallback routes, and evidence requirements.
- Containment metrics distinguish safe automation from hidden rework.
- Recurring exceptions become backlog items with operational and technical owners.
- Map exception classesIdentify the exception patterns that stop workflow automation from resolving work safely.
- Assign routing policyMap each exception class to owner queues, fallback paths, service targets, and user-facing messages.
- Measure containmentInstrument clean containment, assisted containment, reopens, manual rescue, and downstream rework.
- Reduce recurrenceReview exception patterns on a fixed cadence and convert recurring failure modes into remediation backlog.
Guias
Governance by design
Embed approval and release controls directly in delivery workflow.
- Authority boundaries documented for every autonomous action class.
- Release and rollback policy treated as first-class artifacts.
- Incident response loops linked to evaluation improvements.
- Authority mappingDefine where autonomy is allowed, restricted, or blocked pending review.
- Approval packet designStandardize escalation packets so reviewers can act without reconstructing context.
- Release policiesTie model/prompt changes to release gates, approvals, and rollback plans.
- Incident loopClose incidents with postmortems that feed directly back into evaluations.
Guias
Value realization operating model
A practical sequence for turning AI delivery into measurable economic outcomes.
- Baseline metrics are captured before implementation choices harden.
- ROI claims are tied to live operating telemetry and owner decisions.
- Scale decisions follow measured value, not demo enthusiasm.
- Baseline economicsMeasure volume, handling time, delay cost, error rate, escalation share, and labor mix before scope is finalized.
- Model rollout scenariosBuild conservative, expected, and stretch scenarios with adoption ramp and support overhead included.
- Instrument go-liveAttach production metrics to request cohorts, reviewer decisions, resolution time, and unit cost.
- Run value reviewsReview economic performance on a fixed cadence and decide whether to expand, tune, or stop.
Guias
AI economics control plane
A playbook for managing AI investment through adoption, unit economics, operating cost, and scale decisions.
- AI value reviews connect adoption, cost, quality, risk, and expansion decisions.
- Unit economics are measured per completed outcome rather than per request.
- Scale decisions are tied to cohort evidence, not one-time launch assumptions.
- Set economic baselineCapture current volume, handling cost, rework, quality loss, delay cost, and risk exposure before rollout.
- Model adoption rampForecast adoption by cohort, workflow eligibility, enablement, reviewer load, and abandonment signal.
- Measure unit economicsTrack cost per completed outcome across model, retrieval, tooling, review, support, and exception recovery.
- Run scale decisionsUse value reviews to decide whether to expand, tune, hold, or retire each workflow class.
Guias
Industry AI rollout
A rollout sequence for adapting production AI to public-sector, retail, insurance, and logistics operating constraints.
- Domain controls are named before solution architecture hardens.
- Industry-specific evidence models are connected to release and review gates.
- Scale decisions account for frontline adoption, policy risk, and measurable operational value.
- Map domain constraintsIdentify policy, regulatory, accessibility, customer-impact, and operational constraints by workflow segment.
- Define evidence modelSpecify the records, source systems, policy references, and reviewer decisions required for each workflow class.
- Pilot controlled cohortsStart with bounded cohorts and compare assisted, automated, and manual paths by quality and cycle time.
- Scale with operating reviewsUse regular reviews to decide where to expand, tune, hold, or retire industry workflows.