Playbook

The ERP is the system of record, and AI that touches it must respect its controls. This playbook covers the integration layer, master data grounding, gated transaction tools, reconciliation, audit, and evaluation.

Invoice processing is high-volume, rule-heavy, and measurable, which makes it an ideal first agent. This playbook covers extraction, matching, exceptions, ERP posting with gates, evaluation, and the controls finance requires.

An LLM gateway is the first platform component a serious AI program builds: one API, routing and fallbacks, caching, cost accounting, logging, and policy. This playbook covers the build and rollout.

Model Context Protocol servers expose enterprise data and actions to AI clients through a standard interface. This playbook covers resource and tool design, authentication, validation, error handling, observability, and testing.

Language models have changed what OCR pipelines can deliver: layout understanding, error correction, and structured output. This playbook covers capture, preprocessing, engines, layout, LLM post-correction, validation, and evaluation.

Agents that remember the right things across sessions are more useful; agents that remember the wrong things are a liability. This playbook covers memory types, write policies, retrieval, forgetting, privacy, and evaluation.

Tools are how agents affect the world, which makes tool design the center of agent safety and usefulness. This playbook covers contracts, schemas, permissions, execution, validation, observability, and testing.

A quality gate is where an AI change earns the right to reach production. This playbook covers gate criteria, automated checks, calibrated graders, consequence-based thresholds, CI integration, and reporting.

A research assistant that fabricates a citation is worse than no assistant. This playbook covers building one that retrieves from approved sources, verifies every citation, synthesizes with provenance, and is evaluated for accuracy and coverage.

An AI sales assistant earns its place by removing research, drafting, and CRM busywork from reps without sending anything they did not approve. This playbook covers scope, tools, guardrails, evaluation, and rollout.

Misrouted tickets cost days. An AI ticket routing system classifies, enriches, and routes with explainable rules and a human queue for uncertainty. This playbook covers taxonomy, classification, rules, evaluation, and launch.

AI translation at scale needs terminology control, quality estimation, and human post-editing where it matters. This playbook covers glossaries, machine translation, quality estimation, post-editing, integration, and evaluation.

An underwriting assistant accelerates the work around the decision without taking the decision. This playbook covers intake, extraction, guideline retrieval, cited risk summaries, decision rights, evaluation, and controls.

Voice agents must understand, respond, and act in real time while sounding natural and knowing when to hand off. This playbook covers telephony, streaming speech, dialog design, confirmed actions, handoff, and evaluation.

Anomaly detection fails when it floods analysts with alerts nobody can act on. This playbook covers signal selection, baselines, detection methods, alert tuning, feedback loops, and evaluation that keeps precision high.

CRMs are full of stale records and empty fields because updating them is tedious. An AI CRM assistant summarizes, proposes updates, and drafts, under the user's permissions. This playbook covers the build.

An AI customer service agent that resolves issues, not just answers questions, needs grounding, tools, escalation, and evaluation. This playbook walks through the build from scope to production.

Turning documents into structured data is where LLMs earn their keep, if the pipeline validates and measures. This playbook covers intake, parsing, schema-driven extraction, validation, review, evaluation, and delivery.

Shared inboxes drown teams in unsorted mail. An AI email triage system classifies, extracts, prioritizes, routes, and drafts replies for approval. This playbook covers the build from intake to evaluation.

The month-end close is a choreography of reconciliations, variance explanations, and evidence collection under deadline. This playbook covers task orchestration, reconciliation support, variance analysis, evidence, controls, and evaluation.

HR teams answer the same policy questions daily and handle sensitive matters that no assistant should touch. This playbook covers grounding, identity, routing, sensitive-topic handling, privacy, and evaluation.

Marketing teams want answers from their data without waiting for analysts. An analytics agent delivers them only when metrics are defined, data access is governed, and narratives cite evidence. This playbook covers the build.

Meeting summaries are useful when they capture decisions and actions accurately and respect privacy. This playbook covers capture, transcription, structured summarization, verification, privacy, and evaluation.

Onboarding is a sequence of questions, tasks, and handoffs that AI can guide without replacing the people who matter. This playbook covers journey mapping, grounding, task tools, personalization, evaluation, and rollout.