Knowledge AI
When retrieval-augmented generation works, and when it does not
RAG succeeds or fails on retrieval quality. A guide to where it fits, the common failure points and how to measure each stage separately.
2 min read
Insights / Knowledge hub
Research-led writing for people building with AI
Articles on how AI systems behave in production, written by engineers for buyers and builders. Technical claims link to their sources.
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Latest insights
Document AI
How to evaluate document extraction before you automate
Field-level precision and recall, confidence calibration and straight-through thresholds: a practical method for judging document AI on your own files.
2 min read
Architecture
Multi-agent systems or workflow automation? Choosing the simplest thing that works
A decision guide for choosing between single model calls, workflows with AI steps, single agents and multi-agent designs.
2 min read
Governance
Human approval patterns for AI systems that act
Practical patterns for keeping people in control of consequential AI actions without turning review into a rubber stamp.
2 min read
Strategy
Estimating the cost and ROI of an AI initiative honestly
A structured way to estimate AI value and total cost, including review effort, exceptions, running costs and the scenarios that make a case fail.
2 min read
Security & Deployment
Hosted API, private cloud or on-premises: deciding where your models run
A comparison of deployment options for language models, covering data protection, quality, cost, latency and the operational burden of each.
2 min read
Operations
AI observability: what to log, trace and measure
A practical observability model for LLM applications: traces, quality signals, cost and latency, drift, and what to do with sensitive data in logs.
2 min read