All field notes

Fraud detection combines rules, models, and analysts in a loop that adapts as fraud does. This playbook covers data, features, model and rule design, real-time scoring, review, evaluation, and governance.

Graph RAG answers questions that span many documents and hops of relationships, where chunk retrieval falls short. This playbook covers when it fits, extraction, graph construction, combined retrieval, generation, and evaluation.

A practical playbook for hiring an FDE — the 5-step process, the traits that separate a real forward deployed engineer from a relabeled contractor, and a ready-to-use checklist.

Reinsurance runs on submissions that arrive as spreadsheets in a hundred formats and treaty wordings nobody can search. This guide covers where AI genuinely helps — submission structuring, exposure aggregation, wording comparison, and claims bordereaux processing — and why pricing and capacity decisions stay firmly with underwriters.

An AI MVP should prove the bet, not boil the ocean. How to scope, build, and ship an AI MVP fast—protecting runway and keeping the capability you build.

A churn model is only valuable if it changes what the retention team does. This playbook covers label definition, features, training and calibration, connecting scores to actions, evaluation, and monitoring.

Shipping models and LLM applications safely means pipelines that test data, evaluate behavior, gate promotion, deploy progressively, and roll back fast. This playbook covers the build for classical ML and LLM systems.

Claims triage decides who handles what and how fast. This playbook covers intake, extraction, severity and complexity scoring, fraud indicators, routing rules, adjuster decision rights, evaluation, and controls.

A context budget is an explicit allocation of the model's window between system instructions, retrieved evidence, conversation history, tool output, and the response. This explainer covers why unbudgeted systems fail silently through truncation, how to size each component, and what to drop first when the budget is exceeded.

An AI regression suite is a fixed set of cases run on every change to catch quality that has silently degraded. This explainer covers why probabilistic outputs make regression testing different, what belongs in the suite, how cases should be added from incidents, and how to judge results that are not pass or fail.

A scratchpad is the working space where an agent records intermediate reasoning and findings across steps. This explainer covers why structured scratchpads outperform free text, how they interact with the context budget, what they contribute to debugging, and when a scratchpad should persist beyond a single run.

A simulation environment lets agents be tested against realistic replicas of the systems they will act on, without real consequences. This explainer covers what fidelity is actually needed, how to generate representative scenarios including failures, what simulation cannot capture, and how it fits alongside production evaluation.

Continuous evaluation measures AI quality on an ongoing basis rather than once before launch, because models, data, and traffic all change. This explainer covers what can be measured automatically from production, how sampling and human review fit in, and why evaluation sets must be refreshed from real traffic.

Prompt compression shortens what is sent to a model while preserving the information that matters. This explainer covers the main techniques from summarisation to token pruning, what each loses, why better retrieval usually beats compressing bad retrieval, and how to measure whether it is working.

Multi-agent systems promise specialization and scale but multiply failure modes. This whitepaper catalogs the orchestration patterns that work in production, when to use each, and the controls that keep them reliable.

Tokenizing real-world assets puts ownership records and transfer logic on programmable ledgers. This whitepaper explains the architecture, the legal and compliance scaffolding, and the engineering decisions institutions face.

Most organizations have responsible AI principles. Few have turned them into controls that engineers apply. This whitepaper maps each principle to lifecycle practices, artifacts, and measures, so responsible AI becomes verifiable.

Prompts are not specifications. This whitepaper explains how spec-driven development turns AI features from demos into verifiable systems, what a good AI spec contains, and how it changes delivery, testing, and governance.

Pakistan has become a serious source of senior AI and software engineering for US companies. This whitepaper examines the corridor: talent, economics, overlap, legal structure, security, and the operating model that makes it work.

ML talent is scarce and uneven. How to hire machine learning engineers who ship production models—not notebooks—whether in-house or through a partner.

A confused deputy attack persuades a privileged system to use its authority on behalf of someone who does not have it. This explainer covers why AI agents have made this old vulnerability newly common, how it differs from ordinary injection, and why acting on the user's identity rather than the agent's is the fix.

A sovereign AI cloud provides infrastructure operated under a specific jurisdiction's legal control, not merely located there. This explainer covers why location alone does not satisfy sovereignty, what these offerings actually provide, the capability and cost trade-offs, and how to judge whether the requirement applies to you.

An AI model licence governs what you may do with the model and sometimes with its outputs, and open weights do not mean open licence. This explainer covers the common restriction types, why output ownership is less settled than assumed, what changes when a licence is revised, and what to verify before building.

An air-gapped AI deployment runs entirely within an isolated environment with no external connectivity. This explainer covers what that forecloses, why model updates and evaluation become substantially harder, which sectors genuinely require it, and what a realistic air-gapped architecture looks like.