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title: How to Build a Context Layer and a Company Brain | daily.dev
description: Building a production-grade &#x27;company brain&#x27; — a context layer that grounds LLMs in a company&#x27;s internal knowledge — is far more complex than the typical demo...
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# How to Build a Context Layer and a Company Brain

**[Towards Data Science](https://daily.dev/sources/tds)** · 11 min read · 1 upvotes · 0 comments

## Summary

Building a production-grade 'company brain' — a context layer that grounds LLMs in a company's internal knowledge — is far more complex than the typical demo suggests. The post covers seven major engineering challenges: continuous ingestion via reconciliation loops (not batch jobs), multi-index architectures (relational ground truth, BM25, vector, knowledge graph), retrieval as a dynamic orchestration graph rather than top-k similarity, teaching the brain through curation and usage feedback, agent execution with separate credentials and interrupt/approve flows, an LLM gateway for reliability and cost attribution, and evaluation harnesses with golden datasets. Key principles: treat all indexes as rebuildable projections from a relational source of truth, make identity deterministic and operations idempotent, and build evaluation infrastructure early. The author draws from a production system serving multiple tenants across dozens of data sources.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/how-to-build-a-context-layer-and-a-company-brain>

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