All field notes

The ROI of a forward deployed engineer is the value of an outcome that finally ships versus the quiet cost of an initiative that stalls. How to build the case.

Generative AI talent is scarce and uneven. How to hire generative AI developers who ship production apps—and avoid paying for impressive demos that never scale.

Approval gates and exception queues are only real if humans can review fast with context. This playbook covers routing policies, reviewer interface, decision capture, staffing, feedback loops, and metrics.

Hybrid search combines exact-term precision with semantic recall and consistently beats either alone on enterprise queries. This playbook covers dual indexes, query processing, fusion, reranking, filters, and evaluation.

A knowledge base chatbot is the most common first LLM project and the most commonly under-built. This playbook covers curation, retrieval, grounded answers, refusal, evaluation, and launch so it earns trust.

Lead scoring works when sales trusts the score and acts on it. This playbook covers outcome definition, fit and intent features, calibrated modeling, CRM delivery, feedback loops, and evaluation on pipeline outcomes.

A model registry is the system of record for what is in production, how it was evaluated, and who approved it. This playbook covers metadata, versioning, lineage, evaluation records, approval, deployment tracking, and access.

Multi-agent systems fail from handoffs, loops, and untraceable behavior. This playbook covers when to decompose, typed contracts, deterministic orchestration, shared state, budgets, trajectory evaluation, and observability.

Some data cannot leave the perimeter, and some workloads justify owning the model. This playbook covers building a private LLM deployment: model selection, serving, security, gateway integration, evaluation, and operations.

Procurement is policy-heavy, approval-heavy, and full of requests that arrive as free text. This playbook covers intake, policy checks, supplier and catalog grounding, approvals, ERP integration, and evaluation.

Buy now pay later depends on instant decisions, low friction, and increasingly on demonstrable affordability assessment. This guide covers where AI supports decisioning and operations, why affordability and adverse decisions carry regulatory weight, how disputes and chargebacks are handled, and what must stay under human control.

Claims adjusting is slowed by evidence gathering rather than by decision-making. This guide covers structuring first notice of loss, triaging severity to route cases correctly, assembling the evidence an adjuster needs, handling fraud signals responsibly, and why coverage and settlement decisions must remain human.

Equipment finance combines credit assessment with asset knowledge, and both are slowed by document handling. This guide covers structuring applications and financial statements, supporting residual value assessment, reviewing documentation, monitoring portfolio health, and why credit decisions and pricing stay with underwriters.

Factoring depends on verifying invoices and knowing debtor quality, both of which are document-heavy and time-sensitive. This guide covers automating verification, monitoring concentration and dilution, detecting fraud patterns responsibly, supporting collections, and why advance rate and funding decisions remain human.

Trade finance runs on documentary examination against letters of credit, done by hand under time pressure. This guide covers extracting and comparing document data, detecting discrepancies against terms, supporting sanctions and compliance screening, and why the decision to accept or refuse documents stays with the bank's examiners.

Treasury spends its mornings assembling a cash position from bank statements in incompatible formats. This guide covers consolidating positions automatically, explaining forecast variance from real drivers, reviewing bank fees against agreements, monitoring exposure, and why payment initiation and authority must stay firmly under human control.

When off-the-shelf AI does not fit, custom software wins. What custom AI development includes, when to build vs buy, and how to choose a partner that ships.

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.