All field notes

Data readiness is the best predictor of whether an AI project reaches production. This checklist scores the sources a use case needs across access, structure, metadata, permissions, quality, governance, and tacit knowledge.

AI systems need documentation that regulators, auditors, operators, and successors can use. This checklist covers the specification, model or system card, data documentation, evaluation reports, runbooks, decision records, and change history.

AI agents automate tasks, but someone still has to deploy judgment into a messy workflow. Why agents raise—not erase—the value of forward deployed engineers.

Time zones make or break offshore delivery. How to secure real US-hours overlap, set the right cadence, and keep an offshore AI team responsive.

Accepting an AI system means verifying it meets the specification under real conditions, not watching a demo. This checklist covers acceptance criteria, golden set results, business scenarios, safety, performance, operability, and sign-off.

Security is the top concern when outsourcing AI offshore. The controls, contract terms, and questions that keep your data and IP protected in an offshore engagement.

Most enterprise AI stalls between a promising pilot and production. How forward deployed engineers own the messy last mile that turns AI into an outcome.

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.

The FDE model puts an experienced engineer close to users and operations to connect problem framing, implementation, adoption, and handoff. Here is how the role works and when it fits.

Fintech raises the bar: security, compliance, and reliability are non-negotiable. How a forward deployed engineer ships production systems inside those constraints.

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.