Pull-quote: “We don’t open-source everything. We open-source the things that should belong to the community.”
Why this matters
A lot of AI startups treat “open source” as a marketing posture. We treat it as a deliberate decision per project. Some projects belong in the commons because the community is better served by everyone using and improving them. Others stay proprietary because the R&D investment is significant and the value flows back to customers through the product.
This year we open-sourced four projects.
MarkForge
What it is. Bi-directional Markdown ↔ PDF / Office / HTML conversion. Built on Microsoft’s MarkItDown with extensions for PDF rendering, page sizes, and a WordPress plugin.
Why open source. Document conversion is plumbing. Plumbing should be free. Every team — internal docs, technical writers, AI ingestion engineers — needs it, and there is no defensible business advantage in hoarding it.
Status. Production. Used inside several of our platforms as an ingestion stage for RAG.
Weaviate Local UI
What it is. A local desktop interface for the Weaviate vector database. Schema browsing, object inspection, vector search, RAG chat with multi-provider LLM support, document upload with chunking and embedding.
Why open source. Vector databases are an active part of the agentic AI stack. Tooling that makes them accessible benefits the entire community. Weaviate is excellent and deserves a great local UX.
Status. Production. Used inside our development workflow for any RAG system in early design.
DevOps Monitor
What it is. A complete Docker-based monitoring stack: Grafana, Prometheus, Loki, Alertmanager, cAdvisor, Node Exporter. Configuration-driven target management. HTTP health checks. Per-application dashboards.
Why open source. Every multi-service deployment needs this. Most teams either rebuild it from scratch (slow) or adopt a vendor SaaS (expensive and exfiltrating). The reference stack is a community good.
Status. Production. Runs in front of every internal Zorost service.
Sigma Axion (selected components)
What it is. Components of our quantitative research framework — indicator chains, walk-forward backtesting infrastructure, transaction-cost modeling — published under MIT.
Why open source for these components. The plumbing of a quant stack should be a community good. The actual edges (the strategies themselves) are not open-sourced, because that is where the R&D investment lives.
Status. Production. Live at sigmaaxion.com.
What we don’t open-source
We don’t open-source the platforms with significant proprietary R&D investment: AeroFarr (causal AI for aviation), EvidAI (pharma evidence synthesis), FreightCortex (freight intelligence), Aquil (geopolitical intelligence), SPCio (co-developed with a manufacturing intelligence partner), or ComplyGrid. The investment is real and the value flows back to customers through the product.
Closing
Open source is a deliberate decision, not a posture. Some things belong to the community; some things belong to customers. We try to draw the line clearly.
