BiVelio Research

An open notebook on the AI systems we design to run companies: retrieval, agents, knowledge graphs and orchestration. No hype — the why, the how and the math.

Our mission

Knowledge that isn't shared doesn't compound.

We built BiVelio by running real companies. Every algorithm we design comes from a concrete problem, not a paper. We believe explaining it in the open is the most honest way to make progress.

This section is our research notebook: here we publish the ideas, models and technical decisions behind the product. We want anyone — customer, engineer or curious mind — to understand how the system they trust to automate their operation actually reasons.

In the open

We publish the reasoning, not just the result. Transparency is a requirement, not a marketing gesture.

With rigor

Math, code and real measurements. If we claim something, we show how we know.

That compounds

Each article builds on the last. Knowledge that accumulates is what creates long-term advantage.

To share

We write to teach. If an idea helps another team build better, it has done its job.

The library

Research articles

Browse by topic. Each piece is self-contained: context, intuition, formalization and results.

FeaturedAgents

The knowledge graph as ambient context for agents

An agent without context is just a model. Classic vector retrieval treats knowledge as a bag of independent chunks — which is why it fails on the questions that matter most to a company. We walk through the real problems RAG developers hit (with data), why graph structure wins where vectors fall short, how the landscape stacks up (Pinecone, LangChain, LlamaIndex, Neo4j, Microsoft GraphRAG), and how we plan to be the best at the context that actually counts: a company's operation.

BiVelio Research8 min read
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