Ultron
Resource Infographic
Infographic
Measuring time wasted searching for internal documentation... Average time employees spend searching for information: 1.8 hours per day. Percentage of searches that fail: 35 percent. With Company Docs MCP: Average search time drops to 12 seconds. Answer accuracy from indexed docs: 96 percent. Annual productivity recovered per employee: $23,000.

Your employees spend 1.8 hours per day searching for internal information. 35 percent of those searches fail completely. A Company Docs MCP server makes every answer findable in 12 seconds with 96 percent accuracy.

Your SOPs are in Google Docs. Your product specs are in Notion. Your engineering wiki is in Confluence. Your HR handbook is a PDF nobody has opened since onboarding. When someone needs to know the refund policy, the deployment process, or the brand voice guidelines, they either search for 20 minutes or ping a colleague on Slack and wait for a response.

You have tried putting everything in one place before. It lasted two weeks before the documentation diverged again. The problem is not organization. It is access. You need a system that reads documentation wherever it lives and surfaces the right answer instantly.

What this replaces

Knowledge Manager
$5,000/moCompany Docs MCP
Employee search time
$4,000/mo12-second answers

The Company Docs MCP server creates a unified search layer on top of all your documentation without requiring you to move or reorganize anything.

Setup has 3 phases. Phase 1 (Indexing): The MCP server connects to your documentation sources (Google Drive, Notion, Confluence, local markdown files, PDFs) and creates a vector index of every document. Phase 2 (Chunking): Documents are split into semantically meaningful chunks with metadata: source URL, last updated date, author, and category.

Phase 3 (Querying): When you or an employee asks Claude a question, the MCP server retrieves the most relevant chunks from the index and injects them into Claude's context. Claude answers using only the retrieved documentation and cites the specific source for every claim. If the answer is not in the docs, it says so instead of guessing.

The Stack

MCP ServerThe document bridge

Runs locally or on a server, connects to your documentation platforms via API, maintains the vector index, and responds to Claude's search queries with relevant document chunks.

ClaudeThe knowledge interface

Receives the retrieved document chunks as context and generates human-readable answers with source citations. Operates under strict rules: only answer from provided context, never fabricate information.

UltronThe access layer

Provides a Slack bot or web interface where any team member can ask questions without needing Claude Desktop. Routes questions to the MCP-connected Claude instance and returns answers in the channel.

ultron.sh/agents

System Architecture

indexer/
gdrive_connector.ts
notion_connector.ts
confluence_connector.ts
pdf_parser.js
server/
mcp_server.ts
vector_store.js
chunk_retriever.ts
citation_builder.js
stack_cost_audit
$ ultron audit --scope full_architecture
Monthly stack cost: $30/mo
Equivalent team cost: $9,000/mo
Cost reduction: 99.6%
✓ Audit complete. Architecture validated.

The difference between a useful internal knowledge bot and a useless one is citation. When Claude answers with 'According to your Refund Policy document (last updated March 2026), the standard refund window is 30 days,' employees trust it. When it answers with 'The refund window is probably 30 days,' they do not. Always force citation. It is the single biggest factor in adoption.

Your company documentation already has the answers. Your team just cannot find them. Fix the access layer and 1.8 hours per day comes back.

Included in this resource

Document source connectors
Vector index configuration
Index your first documentation sourceUnlock
Enterprise Knowledge Search Study
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