Dmytro Shein

How a Company Brain Works: The Technology Behind Organizational AI Memory

A Company Brain connects to the systems where institutional knowledge already lives, indexes that knowledge by meaning rather than by filename, and returns sourced answers with the company's permission boundaries intact. The architecture is the same five-layer pattern Sphere has shipped into production — this article walks through each layer with the operating evidence attached.

RAG Evaluation: How to Measure Accuracy Before Going to Production

Most enterprise RAG projects are evaluated on vibes. Four metrics split by layer — context precision, context recall, faithfulness, answer relevance — turn accuracy from an opinion into a number you can track, defend, and improve before and after production.

RAG Chunking Strategies: How to Split Documents Without Destroying Context

The least glamorous decision in RAG is the one that quietly determines retrieval quality: how you split documents. Fixed-size chunking cuts blind through structure. Semantic and hierarchical chunking preserve context — and chunk metadata is what makes citations, permissions, and filtering possible.

Hybrid Search in Enterprise RAG: Why Vector-Only Is Leaving Accuracy on the Table

Vector-only retrieval quietly fails on the exact-match queries enterprise users depend on. Hybrid search — BM25 + vector retrieval fused with reciprocal-rank fusion and a reranking step — is the production baseline that makes enterprise RAG reliable.

Multi-Model Enterprise AI: Why Model Flexibility Is a Governance Requirement, Not a Feature

Locking enterprise AI to a single vendor's model is not a commercial preference — it is a governance failure. Commercial risk, data residency constraints, model quality evolution, and regulatory exposure all require the ability to switch or add models without rebuilding your compliance layer.

AI in Financial Services: What FINRA, MiFID II, DORA, and the EU AI Act Require

Financial services organisations face more AI-specific regulation than any other sector. Seven frameworks, applied simultaneously, to the same employees on the same platform. Here is what each requires, where they overlap, and how to enforce all of them without a separate compliance programme for each one.

CSRD AI Emissions Reporting: A Practical Step-by-Step Guide for Sustainability Teams

You need to report the carbon footprint of your organisation's AI usage under ESRS E1. Your AI vendors provide none of the data. Here is exactly how to gather it, calculate it, and produce an auditable disclosure — with or without automated tracking.

EU AI Act Risk Classification: A Step-by-Step Guide for Compliance Teams

How to classify every AI system your organisation uses across all five risk levels — the questions to ask, the eight Annex III domains that determine High-Risk status, fine thresholds at each tier, and what each classification requires you to do next.

100 OpenClaw Use Cases You Can Try Today