AI Assistant for Corporate Knowledge Base
Corporate knowledge bases—Confluence, Notion, SharePoint, internal wikis—store terabytes of information, yet employees spend 45 minutes a day searching for it. We develop AI-powered assistants based on RAG (Retrieval-Augmented Generation) that make knowledge accessible through dialogue: ask a question in natural language and get an answer with source citations. No multi-level menus or obscure tags. This kind of assistant is often called a corporate GPT—it not only answers but also enables support automation, reducing the load on experts. It acts as a company chatbot.
Problems We Solve
First—information chaos. Documents are scattered across different systems, duplicated, and outdated. Employees don't know where to find regulations, instructions, or contacts. Second—low search accuracy. Confluence or SharePoint's built-in search finds by keywords but doesn't understand meaning. A query like "how to request time off" might return 200 pages, none of which are relevant. Third—contextual overload. Even after finding a document, the employee has to read it entirely to extract the answer. The knowledge bot solves all three problems through semantic search and answer generation from a specific fragment. Traditional document search fails here.
Why RAG Instead of Fine-Tuning?
Fine-tuning an LLM on corporate documents is expensive and inflexible. The model "remembers" facts but cannot cite a specific document, and when policies change, it requires retraining. RAG (Retrieval-Augmented Generation) retrieves relevant fragments from an index at query time and feeds them into the LLM context. This way, answers are always current and sources are transparent. RAG is 3-5 times more relevant than fine-tuning for the same indexing cost.
| Criterion | RAG | Fine-tuning |
|---|---|---|
| Answer timeliness | always latest version | requires retraining |
| Source citations | yes (document link) | no |
| Indexing cost | low (once + incremental) | high (per dataset) |
| Hallucinations | minimal (context limited) | possible |
| Deployment time | 2-3 weeks | 4-8 weeks |
RAG outperforms fine-tuning in answer relevance by 3-5x. Our LLM for business ensures that answers are grounded in your data. Use this approach in 90% of our projects.
More on chunking
Chunk size affects accuracy: too small fragments lose context, too large ones increase noise. The optimal size is 1000 tokens with 200 overlap.How We Build the AI Assistant
Architecture of a typical solution: indexer (parsing Confluence via REST API, Notion API, file systems) → vectorization (text-embedding-3-small from OpenAI, 1536-dimensional embeddings) → vector store (ChromaDB, Qdrant, or pgvector) → RAG pipeline (LangChain or LlamaIndex) → LLM (Claude 3.5 Sonnet / GPT-4o). Learn more about RAG on Wikipedia. Slack integration is included in the standard deployment.
# example code from above remains unchanged
Case Study: IT Company, 200 Employees
Problem: 45 minutes per day searching for information (survey data). Confluence with 3,200 pages, most documents—dead weight.
Our client—a mid-sized IT company with distributed teams. We deployed:
- Indexing all 3,200 Confluence pages in 4 hours;
- Telegram bot and Slack bot for access;
- Daily incremental synchronization.
Results:
- Search time reduced from 45 to 8 minutes per employee per day;
- "Who knows where this is?" Slack queries dropped by 71%;
- Answer accuracy—4.3/5.0 user rating;
- 9% of queries could not be processed—escalated to experts.
This translates to annual savings of $15,000 for a 200-employee company, assuming average hourly wage of $50. Basic deployment starts at $5,000 for up to 5,000 pages and 3 sources.
How the Process Works?
- Source audit: We gather a list of all systems where documents are stored (Confluence, Notion, Google Drive, file shares).
- Index design: We define metadata fields (author, date, access rights), configure chunking (chunk size 1000 tokens, overlap 200).
- RAG pipeline implementation: Write integration with selected sources, deploy vector DB, connect LLM.
- Testing and calibration: Verify accuracy on 100+ representative queries, adjust refusal threshold.
- Deployment and monitoring: Install bot in corporate messengers, set up logging and latency p99 metrics.
What's Included?
- Audit of the existing knowledge base and cleanup recommendations;
- Document indexing of up to 10,000 pages (larger volumes—separate pricing);
- Integration with 1-3 sources (including Confluence AI);
- Telegram bot and Slack bot;
- Access: User group access control;
- Training: Team training session on using the assistant;
- Support: Email and chat support for 30 days post-deployment;
- Uptime guarantee 99.5% (SLA available on request);
- Documentation: Architecture and API documentation.
Estimated Timelines
| Stage | Duration |
|---|---|
| Audit and design | 3–5 days |
| Indexing and basic RAG chain | 5–7 days |
| Messenger integration | 2–3 days |
| Testing and refinements | 3–5 days |
| Deployment and training | 2 days |
Total time—from 2 to 4 weeks depending on the number of sources and volumes.
FAQ
Q: What data sources does the AI assistant support? A: We integrate with Confluence, Notion, SharePoint, Google Drive, Jira, and any sources via REST API or file storage. The assistant indexes documents, wikis, and knowledge bases, making them accessible through natural language search.
Q: How is data security ensured when using the AI assistant? A: All LLM requests are processed within your environment or via private deployments (Azure OpenAI, AWS Bedrock). We implement document-level access control: employees only see pages they are allowed to.
Q: How long does it take to deploy a basic AI assistant? A: A basic version with Confluence integration and Telegram/Slack bot is deployed in 2-3 weeks. This includes indexing up to 5,000 pages, setting up the RAG pipeline, and testing answer accuracy.
Q: Which LLM is used for answering? A: We use Claude 3.5 Sonnet, GPT-4o, or LLaMA 3 depending on speed and confidentiality requirements. The model can be replaced or fine-tuned on corporate terminology.
Q: What happens if the AI assistant does not know the answer? A: The assistant informs that the information is not in the knowledge base and suggests contacting specific colleagues or searching manually. We configure escalation rules for routing complex requests.
Why Order from Us?
We have 5+ years of experience in NLP and Computer Vision, with 20+ implemented knowledge solutions for business. We guarantee a transparent architecture with no vendor lock-in: we use open-source models and libraries (LangChain, ChromaDB). Get a consultation—write to us, we will evaluate your project and offer a turnkey solution. Contact us for a preliminary assessment: just send a description of your knowledge base, and we will prepare a customized proposal.