Imagine an employee spending 15–30 minutes searching for answers in Confluence, sending a ticket to IT, and the specialist spending another 5–10 minutes processing it. According to our data, 70% of IT requests mid-month are repetitive: password reset, ticket status. Each cycle costs the company an average of 8 minutes of two people's working time. We solve this with an enterprise AI chatbot for internal business processes that delivers answers in 1–2 minutes without human intervention.
Our bot is not just an interface to internal systems. It is a full RAG pipeline with access control, integration with corporate services, and support for complex queries. For example, in a company with 500 employees, the bot processes 200 requests per day, offloading the HR department by 40%.
What problems does an AI chatbot solve?
Internal requests drain time from both employees and specialists. The bot automates the routine: HR questions (vacation days, health insurance, office rules, travel arrangements), IT requests (password reset, system access, ticket status), finance queries (advance report status, purchase requests), navigation through the internal knowledge base, and new employee onboarding. As a result, support workload drops by 85%, and response time decreases from 20 minutes to 2.
How do we ensure data security?
The key difference from a public bot is authentication and access control. Each request is processed in the user's context: their role, department, permissions. The bot checks whether the employee has access to the requested information; otherwise, it returns a denial.
class InternalBotContext: user_id: str department: str role: str permissions: list[str] def process_request(message: str, context: InternalBotContext): if requires_finance_access(message) and "finance" not in context.permissions: return "You do not have access to financial data. Contact your manager." return bot_handler(message, context) RAG over internal documents
We use Retrieval-Augmented Generation — RAG. We index documents from Confluence (via API), SharePoint (Graph API), Google Drive. The index updates via webhook when documents change. Importantly, ACLs are indexed together with documents — employees see only what is permitted.
Why RAG over fine-tuning?
RAG is dynamic: when policies change or a new regulation is added, you just reindex the document without retraining the model. Fine-tuning is justified when the bot must strictly generate corporate templates — then we use LoRA adapters, which reduce training costs by 10x. According to our measurements, 95% of standard requests are covered by RAG without fine-tuning.
Integrations
- JIRA/ServiceNow: create tickets, check status
- 1C:ZUP: vacation balance, salary history
- Active Directory: employee information, organizational structure
- Slack/Teams: native integration as an app
Comparison of approaches to corporate chatbot
| Approach | Knowledge source | Accuracy | Implementation complexity |
|---|---|---|---|
| Rule-based FAQ | Static rules | Low when data changes | Low |
| Simple Q&A (vector search) | Only documents | Medium (70-80%) | Medium |
| RAG + LLM | Documents + model | High (up to 95%) | High |
| Fine-tuned LLM | Only training data | High (but static) | Very high |
RAG is better than fine-tuning for dynamic knowledge bases: it provides up-to-date answers without retraining and supports a context window up to 128,000 tokens.
How long does implementation take?
A typical project with Confluence and JIRA integration takes 4 to 8 weeks. The cost is calculated individually for your landscape. Time-to-Answer drops by 90% within the first month after launch.
| Metric | Before bot | After bot |
|---|---|---|
| Average response time | 20 min | 2 min |
| Resolved without human | 0% | 85% |
| Load on HR/IT | 100% of requests | 15% escalations |
What's included in the work
- Audit of current systems and knowledge base
- Architecture design: LLM selection, vector DB (Qdrant, pgvector), indexing pipeline
- Development of RAG layer with access control
- Integration with corporate services (JIRA, 1C, Confluence, etc.)
- CI/CD setup for deployment in your infrastructure
- Documentation and team training
- 3 months warranty support
Example Confluence indexing configuration
index: source: confluence api: Confluence Cloud REST API v2 webhook: true filters: - space: HR - space: IT - label: internal acl: inherit_from_space update: on_content_change Process workflow
- Analytics: identify pain points and frequent requests
- Design: select architecture and stack (LLM, vector DB, connectors)
- Implementation: develop RAG pipeline, integrate systems, test
- Testing: measure Time-to-Answer and answer accuracy on real queries
- Deployment: roll out in your environment (on-premise / cloud)
- Support: monitoring, fine-tuning, index updates
Efficiency
Implementing the bot allows a company to save significant FTE costs. Average savings for a department of 10 people can reach hundreds of thousands of rubles per year. Contact us for a preliminary audit — we will send an implementation plan within 2 days. Request a demo to see the bot in action on your documents.