EKOS - Evidence-backed knowledge, compiled from your stack
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EKOS compiles heterogeneous enterprise sources — code, SQL schemas, ETL pipelines, git history, CI/CD, GitHub issues, Confluence, docs — into a single append-only, evidence-backed ledger. Every fact traces back to where it came from. Conflicts between sources are surfaced with citations, not silently resolved. When EKOS doesn't know something, it says "unknown" instead of guessing. Open source, Rust-based — built for teams tired of AI tools that answer confidently and wrongly.
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Maker update — distributed storage + a real query engine landed this week 🚀
Two big pieces shipped since launch, both proven against real workloads, not just unit tests:
1. Distributed storage & query is feature-complete (v1). EKOS can now split a knowledge base across partitions and serve reads/writes from independent worker nodes backed by S3/Azure/local object storage — a coordinator hands out write leases, query workers cache and serve partitions, and cross-shard search does a real BM25 top-k merge instead of a naive concat.
We didn't just call it done and move on — we ran two full autonomous end-to-end tests against real 90+ partition workloads (MinIO, live LLM provider, the works). They found 8 real defects unit tests never caught: a runtime panic on the object-store path, a query worker that took down the whole cluster when killed, a heartbeat interval that silently expired write leases mid-pipeline. All 8 are fixed with regression tests, and the reports are public in the repo. If a demo only shows the happy path, it's marketing, not evidence — so we publish the failures too.
2. A real compiled-knowledge query engine, not just "dump some JSON at the LLM." Natural-language questions now compile into a typed query plan, execute against structured facts + graph + search, and hand the model a flat set of atomic, source-cited claims — file, line, extractor, confidence — instead of whole objects for it to re-derive structure from. This is the same "compile once, trust the artifact" philosophy applied to retrieval itself.
Both are on main today. As always — open source, evidence-backed, gaps documented rather than hidden. Happy to answer questions about either piece below 👇