LedgeIndex
NewLedgeIndex for AI agents
Knowledge infrastructure

Turn technical knowledge into agent-ready indexes.

LedgeIndex ingests docs, code, and APIs through one pipeline and serves grounded answers to your agents — model-agnostic, via SDK, API, or MCP. Built by developers, trusted by enterprises.

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SDK · API · MCP · BYO-LLM · Hybrid search

Platform

The knowledge engine behind your AI products.

Grounded agents start with a knowledge engine. LedgeIndex is yours to embed.

A production-grade pipeline from raw sources to grounded answers — via SDK, API, or MCP. Swap agent frameworks without rebuilding your knowledge layer.

01

Ingestion pipeline

Crawl docs, repos, PDFs, and API specs. Parsed, cleaned, and chunked automatically.

docsrepospdfsopenapi
02

Index engine

Hybrid vector + keyword search, tuned for technical content. Bring your own LLM — we handle retrieval.

vectorkeywordhybrid
03

Tracing & evals

Debug your knowledge base like you debug code — chunk-level tracing, retrieval evals, and gap detection.

tracingevalsgap detection
LedgeIndex Accelerator

Don't start from scratch.

A reference implementation — not a separate product. Drop in the embeddable widget or deploy the full UI on your index in minutes, prove the engine on your data, then keep it or build your own agent on the same backend.

The engine

Knowledge infrastructure

  • Ingestion, indexing, and hybrid retrieval
  • SDK, API, and MCP — model-agnostic
  • Tracing, evals, and governance controls
Explore the engine

Reference implementation

LedgeIndex Accelerator

  • Embeddable widget — ready to wire into your website
  • Production-grade UI wired to your index
  • Deploy on your data in ~30 minutes
  • Use as-is or replace the frontend entirely
Deploy the starter kitBuild custom instead

Instant proof of value. Teams that only need a working solution ship the Accelerator. Teams that need full control use it as a baseline — same index, your own interface.

Engineering workflows

Outcomes engineers measure — not tickets deflected

Engineering teams get infrastructure — grounding, pipelines, observability, retrieval. Need speed first? Deploy the Accelerator on the same engine, then customize when you're ready.

Technical agent grounding

Reliable context for agents — not a black-box chat layer

Wire version-aware, cited retrieval into coding agents, copilots, and internal tools. Your models get grounded facts before they reason — so plans and answers stay tied to what your product actually ships.

MCPCitationsVersion-aware

“We stopped duct-taping RAG into every agent. One index, SDK and MCP — grounding just works.”

Alex ChenStaff Engineer
Workflow

Connect. Index. Govern. Ship.

Traced at every step. Swap agent frameworks — keep your index.

  1. 01 · Connect

    Point at your sources

    Docs, repos, OpenAPI, PDFs — preview extraction first.

  2. 02 · Index

    Build the index

    Chunked, embedded, re-crawled. Hybrid search with citations.

  3. 03 · Govern

    Review before ship

    Audit quality, flag gaps, control what agents retrieve.

  4. 04 · Ship

    Wire your agents

    SDK, API, or MCP. Same index, any stack.

FAQ

Questions, answered.

Everything else — architecture, security reviews, custom deployments — we cover in a demo call.

What do I build with it?

Powerful agents that use grounded knowledge to plan, build, and answer. Point LedgeIndex at your docs and code, then wire the index into coding agents, support bots, or internal copilots — they retrieve cited facts before they reason, so plans and answers stay tied to your actual product.

What exactly is LedgeIndex — another chatbot?

No. LedgeIndex is the knowledge engine underneath: ingestion, indexing, and retrieval APIs. The LedgeIndex Accelerator is a reference implementation — a production-ready assistant UI wired to your index so you can prove value in minutes. Use it as-is, or strip the frontend and build your own agents on the same backend.

Which sources can I connect?

Documentation sites, GitHub repositories, OpenAPI specs, PDFs, and internal knowledge bases like Confluence or Notion. Sources are re-crawled on a schedule, so the index stays in sync without manual re-uploads.

How do my agents query the index?

Three ways: the TypeScript/Python SDK, a REST API, or the built-in MCP server — so tools like Claude, Cursor, and custom agents can use your knowledge base natively. LedgeIndex is model-agnostic: bring your own LLM (BYO-LLM). We handle retrieval and citations; you pick the model.

How do you keep answers grounded?

Every answer carries citations back to the exact chunk and source page it came from. Tracing shows which content produced which response, and content gap reports flag questions your docs can't answer yet. Governance controls let you review index quality and restrict what agents retrieve before anything reaches production.

Where does my data live?

In our managed cloud or your own — LedgeIndex can run in your VPC. Either way, your content stays queryable and exportable; nothing gets locked into a black box.

How is pricing structured?

Usage-based: you pay for indexed content and query throughput, not seats. Start free while you evaluate; enterprise plans add SSO, RBAC, audit logs, and support SLAs.

For developers

Build your first index in minutes.

Point LedgeIndex at your docs, get a queryable knowledge index, and wire it into your app or agent — SDK, API, or MCP.

$ npx ledgeindex crawl https://docs.example.com

Start building

For enterprises

Production-grade from day one.

  • SSO & role-based access control
  • Usage-based pricing — pay for throughput, not seats
  • Your stack, your data: nothing locked in a black box
Book a demo