Long-reads on building intelligent data systems, AI grounded in real company knowledge, and the architecture behind Archively.
What it means to ground an LLM in your own company data, why retrieval-augmented generation matters more than fine-tuning for most teams, and how Archively turns a stack of disconnected tools into a private AI you can actually trust.
Read the essay ↗Why grounding beats fine-tuning, what RAG really does under the hood, and how to build a private AI on your own company data.
The connector architecture behind Archively, how we handle scoped permissions across vendors, and why federated search beats migration every time.
A look at the documented half-life of internal docs, why search degrades faster than headcount grows, and what to do about it before it becomes a hiring problem.
