Haystack is a real-time delivery analytics platform designed for engineering leaders like CTOs, VPs of Engineering, Directors of Software Engineering, and Engineering Managers. Haystack provides actionable insights that enable data-driven decision-making, aligning engineering performance with business objectives. Haystack platform integrates seamlessly with essential developer tools like GitHub and JIRA, offering a comprehensive view of team productivity and delivery efficiency.

Leading companies like AngelList, Shutterstock, Schneider Electric, and many more trust Haystack to optimize their development processes. By transforming historical Git data into objective insights, we help you identify bottlenecks and visualize trends, ensuring timely project delivery and sustained business growth. Our analytics dashboard allows you to monitor critical metrics such as cycle time, making it easier to spot inefficiencies before they escalate into costly delays.

Haystack helps engineering leaders to mitigate risks and improve workflow efficiency. With a unified view of the entire delivery lifecycle, you can track KPIs, compare performance trends, and make informed decisions that drive measurable outcomes. Our platform goes beyond merely measuring productivity; it equips you with the tools to foster continuous improvement and innovation within your teams.

Designed to scale with your organization, Haystack is the competitive advantage that data-driven engineering teams need to thrive. By leveraging analytics, you can transform your engineering operations, enhance collaboration, and accelerate your path to market success. Join top companies in harnessing the power of Haystack for a more efficient and effective engineering process.

mdstill is a document-ingestion tool purpose-built for LLM and RAG workflows. Where generic converters dump messy text, mdstill outputs clean, semantic markdown that preserves tables, headings, and document structure — the things LLMs actually need to understand context.

What you can do with it:

Prepare documents for RAG pipelines (chunk-ready, semantic boundaries preserved) Feed PDFs, Word files, or spreadsheets into ChatGPT, Claude, or Gemini without losing tables Build knowledge bases in Obsidian, Notion, or Logseq from existing document archives Extract structured context for AI agents and embeddings How it's different: Deep-conversion mode runs layout-aware parsing (tables, OCR, multi-column PDFs) — not just text dumping. Markdown output is ~40% more token-efficient than raw text, so your LLM costs drop. REST API available for pipeline automation.

Free tier, no signup required for basic use. Competes with markitdown, Unstructured.io, and LlamaParse — but with a zero-friction web UI.