Self-Hosted · Private · MCP-Native

Your Knowledge.
Your Rules.

RagGo brings AI-powered semantic search to your documents — fully private, self-hosted, and zero cloud dependency. For universities, teams, and anyone who can't compromise on data privacy.

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Windows · macOS · Linux No Cloud Required MCP Compatible
raggo — terminal
The Problem

The Privacy Dilemma

Professionals and Organizations that need private AI search the most, are the ones least served by current options.

Option A

Cloud RAG Services

OpenAI Assistants, Pinecone, Weaviate Cloud. Powerful, but your documents leave your building. FERPA, HIPAA, and most institutional data policies prohibit it.

Privacy risk
Option B

DIY Stacks

LangChain + Chroma + custom glue code. Private, but weeks of engineering — vector databases, embedding pipelines, custom interfaces. Inaccessible for most teams.

Too complex
Option C

Enterprise Platforms

Glean, Coveo, and similar. They solve both problems — but at $30k+ per year. Built for Fortune 500, priced for Fortune 500. Research labs and small firms can't justify it.

Wrong price point

RagGo: Turnkey, private, and accessible.

Install in minutes. Ingest your documents. Search with AI. All on your hardware. Starting at $0.

Get Started Free →
Capabilities

Everything you need,
nothing you don't.

A complete, production-grade RAG stack — not a demo, not a proof of concept.

Document RAG

Ingest PDFs, Markdown, code files, and plain text. Semantic search returns the most relevant chunks ranked by meaning — not just keywords.

Graph RAG

Explore class, function, and module relationships through an API knowledge graph. Understand your codebase at the architecture level, not just file-by-file.

MCP-Native

Drop-in backend for any MCP-compatible LLM client — Claude, Cursor, and more. Document search, graph queries, and system metrics exposed as standard MCP tools.

Web UI

Full browser-based interface for ingestion, semantic search, service monitoring, and configuration. No command line required for everyday use.

Vector Database

The vector database downloads and starts automatically. No Docker, no separate installs, no infrastructure knowledge needed. Just run RagGo.

Local-First Security

Binds to localhost by default. AES-256-GCM data-at-rest encryption on paid tiers. Self-signed TLS for internal gRPC. Your data is never routed through a third-party server — by architecture, not just policy.

Data Sovereignty

Your data never
leaves your hardware.

Not a marketing claim — a technical guarantee. RagGo's architecture makes external data transmission structurally impossible in default mode.

0
External API calls during document processing
100%
Local — embeddings, vector search, and storage
1
Executable to install. No cloud accounts, no containers.
Use Cases

Built for privacy-first teams.

From solo practitioners to enterprise IT — RagGo adapts to your context.

Client data stays with you.

Lawyers, accountants, and consultants handle documents bound by attorney-client privilege, CPA confidentiality rules, and strict regulatory frameworks. RagGo gives you AI-powered search across client files without ever sending data to the cloud.

  • Lawyers: instantly find relevant clauses across thousands of contracts, briefs, and case files
  • Accountants: search across tax filings, audits, and client financials — fully compliant with IRS and SOX requirements
  • Semantic search surfaces related documents you'd miss with keyword search alone
  • Data-at-rest encryption (AES-256-GCM) on paid tiers for additional security
  • No vendor lock-in — your data stays in your format on your hardware
Web UI search at /ui/search
Query: "indemnification clause merger agreement"
Found 5 results · 24ms

[1] Acme Corp MSA 2024 — 94%
    /clients/acme/contracts/msa.pdf · p.8

[2] Johnson Estate Trust Rev. — 91%
    /clients/johnson/trust-rev-2024.pdf · p.3

[3] Standard Merger Template — 88%
    /templates/merger-agreement.pdf · p.14

Private RAG for your MCP toolchain.

RagGo is MCP-native from the ground up. Drop it into your LLM client as a backend, query your codebase with GraphRAG, or use it as a private knowledge base for your AI assistant — all with a standardized API.

  • MCP endpoint works with Claude, Cursor, and any MCP-compatible client
  • GraphRAG for exploring class/function relationships in your codebase
  • gRPC microservice for programmatic ingestion and search
  • Runs entirely on CPU — no OpenAI API key required
MCP config (claude_desktop_config.json)
{
  "mcpServers": {
    "raggo": {
      "url": "http://127.0.0.1:3235/mcp"
    }
  }
}

Enterprise search without the enterprise price tag.

Small businesses accumulate years of proposals, invoices, HR documents, and internal wikis that become impossible to search. RagGo turns that scattered knowledge into an instantly searchable private library — no IT department required.

  • Search across contracts, SOPs, employee handbooks, and vendor agreements in seconds
  • Onboard new hires faster — let them search institutional knowledge instead of asking around
  • Team pricing ($19.99/seat) built for 5–50 person companies
  • Install once on a shared office machine or NAS — no cloud subscription needed
Web UI search at /ui/search
Query: "return policy updated shipping terms"
Found 4 results · 19ms

[1] Shipping & Returns SOP v3 — 95%
    /ops/sops/shipping-returns-v3.pdf · p.2

[2] Customer Service Playbook — 89%
    /ops/cs-playbook.pdf · p.11

[3] Vendor Agreement — FedEx 2024 — 84%
    /vendors/fedex/agreement-2024.pdf · p.6

Research without compliance risk.

Universities and research labs handle IP, student records (FERPA), and grant-funded data that institutional policy often prohibits from leaving campus networks. RagGo gives you AI-powered search that runs entirely on your own infrastructure.

  • Ingest research papers, datasets, and lab notes in a private collection
  • Semantic search across interdisciplinary archives without cloud exposure
  • Library-managed deployment for course materials and institutional documents
  • Data-at-rest encryption (AES-256-GCM) on paid tiers for additional security
Web UI search at /ui/search
Query: "quantum error correction superconducting"
Found 7 results · 38ms

[1] Chen et al. 2024 — 96%
    /papers/chen-qec-2024.pdf · p.12

[2] MIT Lab Preprint — 91%
    /papers/mit-qc-preprint.pdf · p.3

[3] Noise-Resilient Gates Survey — 87%
    /papers/patel-2023-gates.pdf · p.8
Pricing

Start free.
Scale when ready.

No credit card required to get started. Upgrade when your use case demands it.

Free
$0
Forever
  • Limited document ingestion
  • Text & Markdown files
  • Single user
  • Web UI access
  • MCP endpoint
Download Free
Individual
$9.99
per month
  • Unlimited ingestion
  • All file types incl. PDF
  • Single user
  • GraphRAG access
  • Priority support
Get Individual →
Teams
$19.99
per seat / month
  • Everything in Individual
  • Shared collections
  • Usage dashboard
  • Team admin controls
  • Volume discounts
Contact Sales
FAQ

Common Questions

No. In default mode, RagGo binds to 127.0.0.1 and all processing — chunking, embedding, and vector search — happens on your hardware. The vector database is bundled in the installer, so no external downloads are needed.
RagGo bundles a local embedding model that runs on CPU — no GPU required, no OpenAI API key needed. It is natively multilingual with 100+ languages supported, so you can query and ingest documents in different languages out of the box.
The free tier supports plain text (.txt) and Markdown (.md). Paid tiers add PDF, DOCX, XLSX, PPTX, CSV, HTML, and 25+ code file formats including Go, Python, JavaScript, TypeScript, Rust, Java, C/C++, and more. Individual and Teams plans also include OCR for scanned PDFs.
MCP (Model Context Protocol) is a standard for connecting LLM clients to external tools and data sources. If you use Claude, Cursor, or a similar AI assistant, MCP lets RagGo appear as a native search backend. It is entirely optional — you can use RagGo exclusively through the Web UI if you prefer.
RagGo runs on Windows, Linux, and macOS. We support x86_64 and ARM architectures, including Apple Silicon (M1/M2/M3/M4) Macs as a universal binary.
Your data is yours — it lives entirely on your hardware. If you downgrade or stop subscribing you retain access to everything you've ingested; you may lose access to premium features like GraphRAG or unlimited ingestion. Nothing is ever deleted by us because we don't have access to it in the first place.

Start for free.
Own your search.

No credit card. No cloud account. No compromise.

↓ Download RagGo