Ask your knowledge base in plain language, get answers with sources

The Problem

Every organization has too much knowledge in too many places. Wiki pages, FAQ docs, policy PDFs, onboarding manuals, internal portals, archived ticket resolutions, scattered Notion / Confluence / SharePoint pages. The information exists. Finding the relevant piece when you need it is the problem.

Conventional search returns ten results and asks you to pick. Keyword search misses semantic matches (“vacation policy” ≠ “annual leave entitlement”). Most users give up after one search and ask a colleague, or open a support ticket, instead. The knowledge base is technically comprehensive and practically useless.

A RAG (Retrieval-Augmented Generation) assistant changes the shape: the user asks a question in plain language, the assistant retrieves the relevant passages from your corpus, and the LLM writes a grounded answer with citations back to the source. Always current with your data, always traceable, never invented.

What the Solution Does

A chat interface (or API) on top of your knowledge base.

1. Ingest, load your wiki, docs, FAQ, manuals into a structured corpus. Updates flow through automatically.
2. Chunk and embed, split documents into searchable chunks; compute semantic embeddings for each.
3. Query understanding, when a user asks a question, parse intent and reformulate into one or more retrieval queries.
4. Retrieve, vector search returns the most relevant chunks.
5. Generate, the LLM uses the retrieved chunks and the original question to compose an answer, with explicit citations.
6. Surface answer and sources, the user sees the answer and can click through to verify.

Where It Fits

This makes sense if you…

• Have a knowledge base with > 100 pages of structured content (wiki, FAQ, documentation)
• See real cost from “user asks question that’s already in the docs but can’t find it”
• Want a unified search layer over multiple knowledge sources (Confluence, Notion, SharePoint and ticket archive, all together)
• Will keep the source content current (the assistant is only as good as the corpus)
• Are comfortable with LLM-grade answer quality (faster than human, more current than training-data, and not perfectly accurate)

This is probably not the right time if you…

• Have a tiny knowledge base where keyword search is fine
• Need 100% accurate answers. RAG runs 60-80% on free-form questions, well short of 100%
• Cannot maintain the source content, outdated docs produce outdated answers
• Need conversational AI that takes structured action (booking, transactions). That’s a different architecture, see voice-bot or customer-support-chatbot

Business Value

Time-to-answer. Users who would have spent 5-15 minutes searching (or given up and asked a colleague) get an answer in seconds. The compound effect across a team is substantial.

Ticket deflection. Tier-1 support tickets, the ones that have answers in the docs, drop 20-40% after deployment, depending on baseline ticket mix and assistant coverage. Higher-tier tickets (the ones requiring real human reasoning) don’t deflect, by design.

Better corpus utilization. Most knowledge bases have material that almost no one finds, buried two clicks deep, indexed under wrong keywords. RAG surfaces everything in natural language. Coverage of the corpus actually used by users jumps from 30-50% (typical keyword-search reality) to effectively 100%.

Citation-driven trust. Every answer ships with sources. Disputes about “what the policy says” become quick verifications against the cited document. This matters more than it sounds, especially in regulated or contractually-sensitive contexts.

How It Works

The architecture below is the standard RAG pattern, refined across multiple production deployments (YouTravel, Una-desk, Headcorn, RAIN, AGENYZ, and others).

1. Data ingestion

We connect to your sources, Confluence, Notion, SharePoint, Google Drive, file system, internal wiki, ticket archive. Documents flow into a normalized internal format. Updates sync on a schedule (typically daily; faster if needed).

2. Chunking strategy

Documents get split into chunks (paragraphs, sections, or semantic units). Chunking quality is one of the strongest predictors of answer quality. We tune chunking per corpus type and avoid defaults.

3. Embedding

Each chunk gets converted into a semantic vector. We use OpenAI / Anthropic embeddings by default; self-hosted alternatives (BGE, E5) for clients with data-residency constraints.

4. Query processing

When a user asks a question, the system:

• Optionally rewrites the question for better retrieval (“what’s the vacation policy?” becomes “vacation policy annual leave entitlement”).
• Generates one or more retrieval queries.
• Vector search returns top-N chunks.

5. Answer generation

The LLM receives the original question, the retrieved chunks, and a system prompt that says, in effect: “Answer the question using only the provided context. Cite your sources. If the context doesn’t answer the question, say so.”

This is the key behavioral difference from raw LLM: grounded in retrieved context, with explicit “I don’t know” when out of corpus, with citations back to the chunks used.

6. UI / API surface

Default deployment: a chat widget embedded in your product or portal. Alternative: REST API for custom integration. We also integrate with operator workspaces (Chatwoot, Intercom) so support agents see the AI’s answer alongside the conversation and can correct or escalate.

Stack

OpenAI / Anthropic / open-source LLMs (pluggable), vector store (Pinecone, Weaviate, pgvector, or Memgraph’s vector index), Datapipe for the data ingest pipeline, Python/FastAPI for the assistant service, PostgreSQL for conversation history, Chatwoot for the operator workplace (when used).

For knowledge bases where structured data matters as much as free text, product catalogues with attributes, legal corpora with explicit relationships, we extend to a graph-based architecture: Semantic RAG (codename Vedana), which adds Cypher-based structured retrieval on top of vector search.

What You Need to Make This Work

Data. Source documents (wiki, FAQ, docs, manuals). Most formats supported: Markdown, HTML, PDF, Word, Confluence / Notion / SharePoint exports. Examples of typical user questions help us tune retrieval quality.

Integrations. Read access to the source systems. The assistant service exposes a REST API; your frontend (chat widget, portal, app) consumes it. Optional: integration with operator workspace (Chatwoot, Intercom) for handoff to human agents.

Hardware. Cloud-only by default. On-prem deployment available for regulated clients (data stays in your infrastructure; LLM provider can also be self-hosted).

Team. A content owner who knows what’s in the corpus (and what’s missing). A support / operations lead for the deployment workflow. A frontend developer for the widget integration (~20-30 hours during pilot).

Implementation Roadmap

1. Test (2-4 weeks)

Connect to a slice of the corpus. Build the first version. Test against a curated question set with known good answers. Measure accuracy and identify gaps: questions the corpus can’t answer at all are content gaps, separate from assistant problems. Output: written report on accuracy, recommended content improvements, pilot scope.

2. Pilot (1-2 months)

Production deployment to a user group (sample of customers, internal team, etc.). Wire up the UI. Measure assistant accuracy and ticket-deflection rate. Output: working assistant in active use, documented business outcomes, go/no-go on full rollout.

3. Production (2-4 months)

Full deployment. Quarterly accuracy review. Continuous corpus refresh via Datapipe. Your content team owns the corpus; we stay on for embedder updates and edge cases.

Keep in Mind

The limits worth knowing:

• RAG runs 60-80% on free-form questions. This is the chunk-RAG baseline, measured on real deployments. The remaining 20-40% includes questions the corpus can’t answer (content gap), questions the model phrases wrong (retrieval gap), and questions the assistant won’t confidently answer (a feature, by design). For higher accuracy on structured-query domains, see Semantic RAG.
• Corpus quality is the ceiling. Outdated docs produce outdated answers. The assistant doesn’t fix bad source content; it surfaces it more efficiently. Sometimes the most useful first project finding is “your docs are wrong”.
• LLMs hallucinate inside the answer text. What changes vs raw LLM is that the retrieved evidence is grounded, citations match the source. The model can still phrase the answer in misleading ways. We tune prompts to minimize this, but it’s not zero.
• It’s not 100% deflection. The assistant deflects questions that have answers in the corpus. Questions requiring real human judgment (escalations, complaints, exceptions) need human agents. Handoff workflow is part of the design.
• Counting LLM API costs at scale. A high-volume deployment can produce real LLM bills. We use prompt caching, smaller models where they suffice, and Datapipe to dedupe, but plan for ongoing API spend as a real line item.
• Multi-language is real work. A multi-language corpus needs careful handling, chunk-level language detection, multilingual embeddings, per-language prompts. We’ve shipped multi-language deployments but they cost more upfront.

FAQ

How is this different from ChatGPT?

ChatGPT answers from its training data. It doesn’t know your company’s specific policies, your current pricing, or your product manual updates from last week. RAG answers from your data, always current, always cited, always grounded.

Can we self-host the LLM?

Yes. Self-hosted alternatives include Llama 3, Mistral, Qwen, and others. Accuracy varies by model; we benchmark during pilot. Cloud-hosted OpenAI/Anthropic is the cost-effective default; self-hosted is for data-residency-constrained clients.

How do we update the assistant when our docs change?

Datapipe handles incremental ingest, changed docs flow through automatically. Typical update cycle is daily; near-real-time for clients with that requirement.

Does this work in languages other than English?

Yes. We’ve shipped Russian, English, Spanish, Polish deployments. Multilingual support adds some complexity (per-language tuning) but works at scale.

How is this different from Microsoft Copilot for SharePoint / Notion AI / Confluence AI?

Vendor-specific AI integrations work great for organizations all-in on that vendor. We’re the right choice when you want: unified search across multiple knowledge sources, integration with non-vendor systems (custom CRMs, ticket archives, etc.), full on-prem deployment, or substantial customization of retrieval and prompt behavior.

What about questions the assistant can’t answer?

The assistant says “I don’t know” by design, via prompt engineering. It never guesses to fill the gap. Out-of-corpus questions can be routed to human agents via the operator-workplace handoff.

Ready to Discuss?

If you have a knowledge base with real content and real users who can’t find what they need, this is a fast, measurable-ROI deployment. We’ll walk through your corpus, your typical user questions, and your current support / search workflow, and tell you what to expect from a 2-week pilot.

Discuss your project