ClaudeCopilotGleanLangChain isn't performing.
Ember makes it work.
Each Analyst runs different agents, across multiple different tenants. It’s a huge time sink, permissions are a mess, and the agents can’t find the right documents. Now they can.
Every file. Every tenant.
PDFs, decks, Notion, Confluence, GDrive, Sharepoint, S3, codebases, ticket history. Cross-tenant ACL preserved end-to-end.
Structure-aware chunking
Novel chunking that respects semantic and structural boundaries — sections, claims, tables, diagrams — not naïve token windows.
Multi-vector embedding
Each chunk indexed against three vector spaces — semantic, lexical, structural — and resolved at retrieval time per query intent.
Graph-RAG assembly
Entities, citations, ownership and version edges weave the chunks into a navigable graph — not a flat vector store.
Dynamic threads
Semantic spines emerge from the graph — a renegotiated path through institutional memory, generated per query.
Two files. Any agent stack.
Ship Ember as ember.md and skills.md. Drop them into Claude Code, Microsoft Copilot, or any in-house runtime — anywhere your agent reads instructions. No SDK to install. No retrieval code to write.
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Native to your stackStays on disk in your repo or runtime. Versioned alongside your agent definitions.
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Tenant-aware retrievalThe agent sees only what the calling user is allowed to see. Permissions enforced at the chunk.
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Citations by defaultEvery retrieved chunk arrives with file, version, span and a stable URI back to the source of truth.
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Zero migrationKeep your existing prompts, tools and runtime. Ember slots in below them.
Retrieval that understands
how your industry works.
Most RAG systems flatten knowledge. Ember preserves the lineage — citations, ownership, version history, the way teams actually reason — so retrieval traces the real shape of institutional memory.
Structure-preserving chunking
Tables stay tables. Sections keep their hierarchy. Claims keep their evidence. Retrieval honours the document, not a token window.
Citations as first-class edges
If document A cites document B, that's a graph edge — not a side note. Threads traverse them, agents follow them, answers prove them.
Threads, not flat results
Per-query semantic spines surface the path through the graph that best answers the question — with reasoning steps an agent can quote.
vs flat vector RAG
across 100k-doc corpora
across any agent stack
preserved at retrieval
Inspect the brain
that powers your agents.
A read-mostly SaaS console for the people who don't write prompts — Legal, Compliance, RevOps. Browse threads, audit retrievals, govern access. The console is optional; the company brain is the product.
We bolted on Ember and by EOD it was clear it had been night and day for our agents. Zero migration friction, and our team's output is just plain better now.