All field notes

Enterprises can outsource AI offshore and keep governance intact. The controls, vendor selection, and hybrid delivery model that make it work at enterprise scale.

Healthcare AI has to be safe, private, and actually used by busy clinicians. How a forward deployed engineer owns that path from pilot to real-world adoption.

A practical playbook for agencies to add AI to their offering—how to package, price, and deliver white-label AI under your brand without hiring an AI team.

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.

Three engagement models distribute responsibility differently. Use this framework to compare an FDE, a consultant, and staff augmentation against the outcome your organization needs.

Startups live and die on execution speed. How a forward deployed engineer helps you ship the hard bet now and build capability you keep—without over-hiring.

AI agents are easy to demo and hard to ship safely. How to hire AI agent developers who deliver production agents with guardrails, not fragile prototypes.

WhatsApp is where many customers want to talk to businesses, and its rules are strict. This playbook covers platform setup, opt-in, identity verification, grounded answers, confirmed actions, handoff, and evaluation.

SharePoint holds decades of documents behind a complex permission model. AI search over it succeeds only with permission-trimmed indexing, curation, hybrid retrieval, and grounded answers. This playbook covers the build.

Agents act, so evaluating them means evaluating trajectories in realistic environments, not just final text. This playbook covers task suites, simulated environments, trajectory scoring, safety cases, CI gates, and reporting.

Agentic RAG lets the system plan, retrieve iteratively, choose sources, and verify before answering. This playbook covers when it helps, planning, iterative retrieval, verification, budgets, synthesis, and evaluation.

When an AI system is questioned, the audit trail is the answer. This playbook covers the event model, capture points, immutability, redaction, retention, reconstruction, access, and regulatory alignment.

Compliance teams cannot read every communication, transaction, and change. An AI compliance monitor watches defined obligations, flags with evidence, and routes to reviewers. This playbook covers the build.

AI content at scale is only an asset when it is grounded, on-brand, compliant, and reviewed. This playbook covers building a content pipeline with briefs, drafting, validation, editorial gates, and evaluation.

AI spend becomes manageable when it is captured per request, attributed to features and teams, and turned into unit economics. This playbook covers capture, attribution, unit metrics, budgets, alerts, and finance integration.

For SaaS, forward deployed engineers unlock enterprise deals and hard integrations—then feed what they learn back into the core product. The dual-value model.