Fresh from the lab · Edition 001 · First week publishing
The project developed by Neuro Spark directly addresses this problem by automating the extraction of information from DXF (Drawing Exchange Format) files, a format widely used in technical projects generated by CAD tools. The solution identifies legends, symbols, and markers present in the drawings, transforming visual and geometric data into structured information to support quantity takeoff generation.
Your company trained a model on terabytes of customer data. One of them exercises the right to be forgotten. Now what? Retraining from scratch takes days and costs a fortune. At Neuro, we implemented surgical unlearning techniques that make AI forget a specific data point in seconds — with 96.2% effectiveness and auditable evidence for LGPD and GDPR.
Shipping an LLM to draft contracts, score risk reports, or serve customers is the easy part. The hard part is statistical confidence at scale. At Neuro, we run automated quality pipelines based on G-Eval — turning qualitative judgment into continuous, auditable numbers.
Dense manuals, compliance packs, and long histories are table stakes for RAG and agents — but vanilla inference recomputes the same static tokens on every turn. At Neuro we built on advanced prompt caching to fix unit economics and slash time-to-first-token.
Friday afternoon we got together — no sprint review, no roadmap deck — just to pass context among ourselves. The people steering the projects said what was actually going on. We left questions hanging until somebody could replay the idea in their own words. Walking out of that session, I remembered again why I like building here: most of what sets us apart is that kind of persistent, ego-free knowledge transfer.
