Topic
Retrieval (RAG)
Retrieval-augmented generation (RAG) answers questions with a language model by first retrieving the relevant pieces of your own material. These notes cover retrieval that works on structured material such as code, contracts and catalogues, and how to know whether a change made it better.
RAG and knowledge systemsOctober 8, 2026 · 3 min read
RAG for code and other structured material: why token-count chunking fails
Most retrieval systems split documents by token count, embed everything and hope. On code, contracts and catalogues that returns plausible fragments and misses the answer. What we built for DevGuardian AI instead, and how the same design applies beyond code.
October 8, 2026 · 3 min read
From public data to market intelligence: how Valco AI was built
Dubai publishes its property market as open data, updated daily. It is public, and it is not ready to use. The pipeline, the data model, the forecasting models and the conversational layer we built for Valco Properties, and the order we built them in.
October 8, 2026 · 4 min read
Documentation you can audit: observed, documented, inferred
AI-generated documentation reads beautifully, and nobody can tell which sentences came from the code and which the model dreamed up. Our founder compared seventeen tools that document a codebase, found the gap, and designed what he would build instead: facts with evidence and one of three labels.
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