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RAG · Semantic Search · Team-Wide Access

AI Knowledge Base — Built on Your Business Data

Your SOPs, docs, and support history turned into an AI brain your entire team can query in plain language.

Your company's knowledge is buried in folders, wikis, and email threads nobody searches. We build a retrieval-augmented AI system that surfaces the right answer from your actual documents — instantly, accurately, and without a cloud AI seeing your data.

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The Problem

A new support agent asks the same question a senior employee answered six months ago in a document nobody can find. A sales rep quotes the wrong price because the pricing doc was updated and nobody sent the memo. Your onboarding takes 3 weeks because the SOPs are scattered across four tools.

What We Build

We build a RAG (retrieval-augmented generation) system on your documents. Your team queries it in plain language. It searches semantically, retrieves the relevant chunks, and answers with source citations. Connects to Telegram, Slack, or a web interface. Uses Claude or GPT-4 as the reasoning layer — or a local model if data residency matters.

What You Get
✓Document ingestion pipeline (PDF, Word, Excel, Notion, Confluence)
✓Vector database setup (pgvector or Qdrant)
✓Semantic search + hybrid retrieval (vector + full-text)
✓LLM integration with source citation in every answer
✓Chat interface: Telegram bot, Slack bot, or web UI
✓Admin panel: add documents, view queries, flag bad answers
✓30-day post-launch support
How It Works
01
Knowledge audit

We map what documents exist, where they live, and what types of queries the system needs to handle well.

02
Build and index

We ingest your documents, chunk and embed them, set up the vector store, and wire the retrieval pipeline to your LLM.

03
Test and tune

You run real queries — things your team actually asks. We tune chunking strategy and retrieval until accuracy is production-ready.

Real Use Cases
Support team KB: reduces ticket handling time by answering from past resolutions and SOPs
Sales KB: reps query product specs, pricing, and competitor comparisons in one place
HR bot: employees ask policy questions without emailing HR every time
Legal review: paralegal queries contract database for precedents and clause templates
Onboarding accelerator: new hires get accurate answers from SOPs on day one
Pricing
$2,000–$5,000
one-time

Scoped during the free assessment. You get a fixed quote before any commitment.

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Frequently Asked Questions
How many documents can it handle?+
Typical deployments range from 50 to 10,000+ documents. Performance depends on the vector database and retrieval strategy — we tune for your volume.
Does it stay accurate when documents are updated?+
Yes. We build a re-ingestion pipeline so you can update the knowledge base when documents change. New or updated docs can be re-indexed without rebuilding everything.
Can it cite where an answer came from?+
Yes. Every answer includes source citations — which document, which section. This is built into the retrieval architecture, not a post-hoc feature.
What's the difference between this and Private AI?+
The AI Knowledge Base uses cloud LLMs (Claude, GPT-4) as the reasoning layer — queries pass through their APIs. Private AI runs the LLM entirely on your server. Choose Private AI when data residency is a hard legal requirement. Choose Knowledge Base when cloud APIs are acceptable and cost matters.
Can multiple teams use the same knowledge base?+
Yes. We can build access controls so different teams or roles see different document sets, or everyone shares the same base. Depends on your org structure.
See It In Action
→Case study: AI consultant with corporate memory — RAG on n8n→Case study: SEO audit on autopilot — knowledge retrieval pipeline
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