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AI Knowledge Management Services
AI knowledge management that answers from your own documents.
Knowledge that lives in documents, wikis, and inboxes is not accessible when people need it. AI knowledge management systems make your organisation's knowledge queryable, retrievable, and useful, at the moment someone needs an answer.
We build AI knowledge bases, internal search systems, and knowledge retrieval infrastructure that surface the right information to the right person at the right time.
RAG-powered knowledge bases that answer questions from your documents
Semantic search across wikis, PDFs, emails, and structured data
Automated knowledge extraction and organisation from existing content
Integration with Confluence, Notion, SharePoint, Google Drive, and Slack
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
4.9
on ClutchThe problem
Sound familiar?
Teams spending hours searching for information that exists somewhere in your systems?
New employees taking months to become effective because knowledge is buried and unstructured?
Short answer
RaftLabs builds AI knowledge management systems, RAG knowledge bases and semantic search, for companies across the US, UK, Europe, Canada, and the UAE. A focused knowledge base starts at $15,000 and returns cited answers from your own documents in under 2 seconds. Multi-source systems with permission-aware access control run $30,000-$60,000.
Key takeaways
- A focused RAG knowledge base runs $15,000-$35,000 and returns answers in under 2 seconds.
- Multi-source systems with access control and custom UI run $30,000-$60,000.
- Integrates with Confluence, Notion, SharePoint, Google Drive, and Slack with permission-aware retrieval.
- OCR and extraction pipelines can process 20,000+ documents per day with structured output.
Trusted by
The answer existed. It just took twenty minutes to find.
Someone asks a question that has already been answered. The answer is written down, somewhere: a Confluence page nobody reads, a Slack thread that scrolled away, one of three Google Drive folders with almost the same name. So they ask a colleague, who checks two places, guesses, and gets it slightly wrong.
The knowledge was never missing. It was unreachable at the moment someone needed it.
Ask a question, get the answer from your own documented content, with a citation to the source. That is the entire job.
Most organisations have more documented knowledge than they use. It sits in Confluence pages that nobody reads, in PDFs that aren't searchable, in Slack threads that disappear, and in the heads of people who have been there longest.
The average knowledge worker spends close to a fifth of the workweek, nearly one day in five, hunting for internal information or chasing the colleague who happens to know the answer (McKinsey Global Institute, The Social Economy). For teams running on Confluence, SharePoint, and Google Drive, that lost day is where AI knowledge management earns its return.
AI knowledge management closes the gap. A RAG-powered system indexes Confluence, Slack, and Google Drive and returns a cited answer in seconds, instead of the twenty-minute hunt across three systems. RaftLabs has shipped production software since 2015 for clients including Vodafone, T-Mobile, Aldi, Cisco, and Lockheed Martin, with GDPR, HIPAA, and SOC 2 requirements scoped in week one, not retrofitted before launch. The retrieval layer underneath rests on RAG pipeline development and vector database development.
This pays off when the knowledge is already written down, just scattered.
Everything on the left should already be true for your team. Even one thing on the right, and a plain search bar is the smarter first step.
A fit
01
Knowledge already documented across Confluence, SharePoint, Google Drive, Slack, or a wiki, just scattered and hard to search.
02
Teams losing real hours each week hunting for answers, or new hires taking months to get productive.
03
Budget for a build from $15,000, and someone who can point us at the content sources that matter.
Not a fit
- Knowledge that lives only in people's heads, with almost nothing written down yet.
- A single small wiki that a plain keyword search already covers well.
- You need answers invented, not retrieved from your own documented content.
What we build
The knowledge systems we build
- 01RAG-powered knowledge basesQuestion-answering systems built on retrieval-augmented generation that pull answers from your own documents rather than general model training, indexed at paragraph level with answers that cite the source. Hybrid retrieval and re-ranking hold latency under 2 seconds on a 500K-document corpus, and incremental indexing keeps it current without manual curation. Connected to Confluence, Notion, SharePoint, and Google Drive.
- 02Enterprise semantic searchA single query interface across every knowledge source, ranked by semantic relevance rather than keyword frequency. Permission-aware retrieval filters by the user's identity-provider groups in Okta or Azure AD so no one sees documents they cannot access, and zero-result queries are logged to reveal content gaps where knowledge does not yet exist. Spans Confluence, Google Drive, SharePoint, and Slack.
- 03Knowledge extraction pipelinesAutomated pipelines that extract structured knowledge from unstructured content at scale: specs from datasheets, decisions from transcripts, policies from compliance documents, and terms from contracts. The extraction approach matches the content, low-confidence values are flagged for human review, and every document carries an audit log of what was extracted and why. Built on Azure Document Intelligence, AWS Textract, and NLP.
- 04Customer-facing knowledge basesAI help centre and self-service systems that answer customer questions from your documentation and resolved-ticket history, returning a cited answer rather than a list of articles to read. Low-confidence queries escalate to a human agent with full context, and ticket deflection typically runs 30-50% for well-documented products. Deployed across Zendesk, Intercom, and Freshdesk.
- 05Knowledge graph constructionFor domains where relationships between entities matter as much as the entities, we build knowledge graphs that represent those connections explicitly rather than relying on vector similarity alone. Graph traversal answers multi-hop questions that vector retrieval cannot, and graph-augmented RAG combines both in a single query. Built on Neo4j or Amazon Neptune with GPT-4o and NLP.
- 06Slack and Teams knowledge botsAI assistants embedded in Slack or Microsoft Teams that answer questions from your knowledge base where work happens, with cited, threaded replies in about 3 seconds. One RAG backend serves both platforms, absorbing the HR, IT, and onboarding questions that consume hours of senior time each week.
What separates production RAG from a demo
A retrieval demo works on ten clean documents. It falls apart on ten thousand messy ones: near-duplicate policies, a 2019 doc that flatly contradicts the 2024 one, a table split across two PDF pages. The demo answers confidently and wrong. A production system earns trust by knowing when to refuse.
Grounding is not automatic. A Stanford study of leading legal AI research tools, all built on retrieval, found they still produced incorrect or misgrounded information 17% to 33% of the time (Stanford RegLab, 2024). Retrieval reduces hallucination. It does not remove it. The build decisions below are what close the remaining gap.
Naive RAG vs production RAG
| Naive RAG | Production RAG | |
|---|---|---|
| Chunking | Fixed token windows that split sentences and tables | Structure-aware chunks that keep tables, clauses, and context intact |
| Retrieval | Vector similarity only | Hybrid vector plus keyword, then re-ranked |
| Wrong answers | Answers confidently when it should not | Abstains and says 'not found in your documents' |
| Citations | A summary you have to trust | Every claim linked to its source document |
| Freshness | Re-indexed by hand, drifts from the source | Incremental sync keeps the index current |
| Access control | One index everyone queries | Permission-aware retrieval per user's identity groups |
| Quality bar | Ships when the demo looks good | Ships when it passes a measured retrieval scorecard |
Our method
The production-RAG pipeline we build to
- 01Structure-aware ingestion and chunkingWe chunk on document structure, headings, tables, and clauses, not a fixed token count that slices a sentence in half. Tables and code blocks stay intact. Every chunk keeps its source, section, and last-modified date, so a retrieved answer can be traced and dated.
- 02Hybrid retrieval and re-rankingVector search for meaning plus keyword search for the exact terms knowledge bases live on: a part number, an error code, a policy ID. A re-ranker then orders the shortlist. Pure vector search misses exact-match queries, which is where most real questions start.
- 03The retrieval scorecardBefore launch, we measure recall, groundedness, and citation accuracy against a labelled question set written with your team. A system ships when it passes that scorecard, not when the demo happens to look convincing. Most RAG builds skip this step entirely.
- 04Grounded generation with citationsThe model answers only from retrieved content and links each claim to its source, so a reader can open the document and check. No source, no sentence. Grounding is enforced in the prompt and verified in evaluation, not left to chance.
- 05Abstain by defaultWhen retrieval confidence is low, the system returns 'not found in your documents' rather than a fluent guess. In every workflow that matters, an honest no beats a confident wrong answer. Confidence thresholds are tuned per use case, higher for compliance, looser for exploratory search.
- 06Feedback and freshness loopZero-result and thumbs-down queries feed a review queue that surfaces missing content and bad chunks. Incremental indexing re-embeds changed documents on a sync window, so the index never quietly drifts away from what your source of truth actually says.
Pitfalls we plan around
Most knowledge systems fail in predictable ways. We design for each one from week one.
- Stale index
- When a policy changes, the old answer lingers until re-indexing. We run incremental sync on a schedule matched to how fast your knowledge changes, and stamp every answer with the source's last-modified date.
- Retrieval misses
- The answer exists but the retriever never surfaces it. The retrieval scorecard catches this before launch; zero-result logging catches the rest after.
- Prompt injection
- A malicious or careless document can carry text that hijacks the model. We treat retrieved content as data, not instructions, and constrain the model to answering from it.
- Document-level access control
- A retrieval system can leak a document to someone who should never see it. We filter by the user's identity-provider groups at query time, so retrieval never returns content the reader cannot already open.
Key Insight
Where this is heading is agentic retrieval and MCP. Single-shot retrieval is giving way to systems that run several searches, judge whether they have enough to answer, and retrieve again when they do not. The Model Context Protocol (MCP) is becoming the standard way to expose knowledge sources to those agents and to assistants like Claude and ChatGPT. We build retrieval layers an MCP client can query directly, so your knowledge base is ready for agent-driven access, not just a chat box.
What's the question your team keeps re-answering?
Walk us through where your knowledge lives and how people search for it today. We'll tell you what a retrieval system would return and what it costs to build.
How it works
From scope to shipped
Every project follows the same four phases. Scope is locked and price is fixed before development starts.
- Week 101
Discovery and scope
We map the knowledge sources, access patterns, and query types. You leave week 1 with a written scope document and a fixed-price quote. No development starts without your sign-off.
- Weeks 2-302
Design and architecture
We define the retrieval architecture, connector stack, and embedding strategy before writing production code. Design decisions made here cost ten times less than the same decisions made in week 8. The spec is locked before the build starts.
- Weeks 4-1203
Build, integrate, and QA
A working v1 on your first knowledge source at a staging URL by the end of sprint one, then we expand to the rest. Bi-weekly demos. QA runs in parallel with every sprint, not as a phase at the end, and the retrieval scorecard gates the launch.
- Weeks 12+04
Launch and post-launch support
Production deployment with monitoring activated on launch day. 8 weeks of post-launch support included in every project.
Where you land in that range depends on scope, not negotiation:
- Focused knowledge base, $15,000-$35,000
- A single content source, one Confluence space or one Google Drive folder, with a query interface that returns answers in under 2 seconds.
- Multi-source system, $30,000-$60,000
- A unified knowledge system across sources with access control, a custom UI, and ongoing sync infrastructure.
- Enterprise deployment, $100,000-$160,000
- Knowledge graphs, workflow integrations, and advanced analytics on top of multi-source retrieval.
What it costs
Fixed scope. Starting at $15,000.
A focused knowledge base or a multi-source system with access control, sync, and a custom UI, scoped and priced before development starts.
Starts at $15,000
A focused knowledge base starts at $15,000. Ongoing infrastructure runs $300 to $2,000 a month depending on document volume and query load.
Connect one source system first, usually a support wiki or a compliance archive, and prove accuracy before adding the rest of your knowledge base.
No hourly billing
Once we scope your first knowledge source, the price is locked in writing. No hourly billing, no surprise invoices, no change fees added after the fact.
Team continuity
The team that scopes your knowledge system is the team that ships it. The people you meet in week 1 hand it over in production, with no offshore handoff after the contract is signed.
Stay on topic
More on RAG & knowledge management
Article
Why RAG Systems Fail (and How to Tell if Yours Will)
Most RAG projects that stall don't fail because the model is weak. They fail in retrieval, data quality, and evaluation. Here are the failure modes we see most, what each one costs, and how to fix them before launch.
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Article
LLM Fine-Tuning vs RAG vs Prompt Engineering: When to Use Each
Most businesses default to prompt engineering because it is free. Most get disappointed because it cannot teach an LLM new knowledge. RAG and fine-tuning fix different problems. Choosing the wrong one wastes months. Here is the decision framework.
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Article
RAG Architecture Diagram: Naive vs. Advanced RAG Explained
The most-searched question about RAG is not 'what is it?' - it's 'what does it look like?' This guide describes the architecture at every stage, from the simplest naive RAG pipeline to a production advanced RAG system with hybrid search and reranking.
Read moreFrequently asked questions
AI knowledge management is the use of AI, primarily retrieval-augmented generation (RAG) and semantic search, to make an organisation's existing knowledge accessible on demand. Instead of someone spending 20 minutes searching through Confluence, a Slack conversation, and three different Google Drive folders, they ask a question and the system retrieves the relevant answer from your documented knowledge. The AI does not generate answers from general training, it retrieves from your specific content and cites its sources.
Traditional keyword search finds pages that contain the words you searched for. AI knowledge retrieval finds content that answers the question you asked, even when the exact words do not match. A traditional search for 'expense approval process' misses a page titled 'how to get reimbursed'. A semantic search finds it because it understands intent. The more important difference: AI knowledge management can synthesise across multiple documents and return a direct answer with citations, rather than a list of pages you still have to read.
We integrate with Confluence (Atlassian), Notion, SharePoint, Google Drive and Google Docs, Slack (conversations and files), Jira (tickets and documentation), GitHub (README files, wikis), Zendesk (knowledge base articles), PDF document libraries, and SQL databases with structured knowledge. We build custom connectors for proprietary content systems. Multiple sources can be unified in a single search interface, with access control enforced so users can only retrieve content they have permission to see.
We build incremental indexing pipelines that monitor your content sources for changes. When a document is updated in Confluence or Google Drive, the old vectors are deleted and the updated content is re-embedded within a configured sync window, typically hourly or daily, depending on how frequently your knowledge changes. New documents added to indexed folders are automatically ingested. Deleted documents are removed from the index. The result is a knowledge base that stays current without manual curation, beyond the initial setup of what sources to include.
Source-grounded retrieval is the primary safeguard: the AI answers based on retrieved documents and cites its sources, so users can verify the answer against the original content. Confidence thresholds can be configured to return 'no answer found' rather than a low-confidence response. We prompt the model to say when retrieved content does not contain enough information to answer the question. For regulated industries, we can require a human review step for high-stakes queries. No system eliminates errors, but a well-built knowledge retrieval system gives wrong answers far less often than general models and cites its sources so errors are detectable.
A focused knowledge base for a single content source, one Confluence space or one Google Drive folder, with a query interface runs $15,000-$35,000. A multi-source unified knowledge system with access control, custom UI, and ongoing sync infrastructure runs $30,000-$60,000. Enterprise deployments with knowledge graphs, workflow integrations, and advanced analytics run $100,000-$160,000. Ongoing infrastructure cost depends on document volume and query load, and most systems run on $300-$2,000 per month in cloud and API costs.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope AI Knowledge Management Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.