The Challenge
A Fortune 100 technology company with 80,000 employees had accumulated decades of institutional knowledge across disparate systems: SharePoint sites, Confluence wikis, Google Drive folders, Slack channels, email archives, and legacy document management systems. Finding the right information had become a daily frustration.
Employee surveys revealed that engineers spent an average of 4.5 hours per week searching for information they knew existed somewhere. New hires took 6+ months to become productive because they couldn't navigate the knowledge landscape. Tribal knowledge walked out the door every time a senior employee left.
Previous attempts to solve this with enterprise search had failed. Keyword search couldn't understand context or intent, returning hundreds of marginally relevant results that required manual review. Employees had given up on official tools and relied on asking colleagues, creating bottlenecks around senior team members.
The Solution
Jasnova built a conversational AI assistant powered by Retrieval-Augmented Generation (RAG) that understands natural language questions, retrieves relevant information from across all knowledge sources, and synthesizes accurate answers with citations.
Technical Architecture
- Unified Knowledge Graph: Connected 15+ data sources through a unified connector framework, maintaining real-time synchronization as documents are created and updated
- Advanced Chunking: Developed intelligent document chunking that preserves semantic meaning, handles tables and code blocks, and maintains document hierarchy
- Hybrid Search: Combined dense vector embeddings with sparse keyword matching (BM25) for optimal retrieval across different query types
- Multi-Stage Retrieval: Implemented a re-ranking pipeline that uses a cross-encoder model to identify the most relevant passages from initial retrieval results
- Grounded Generation: Fine-tuned generation model that produces accurate, cited responses while refusing to answer when information isn't available
Key Capabilities
- Natural Language Q&A: Ask questions in plain English and receive synthesized answers with source citations
- Multi-Turn Conversations: Follow-up questions maintain context for deeper exploration of topics
- Document Summarization: Summarize lengthy documents, meeting notes, or entire project folders
- Expert Finder: Identify subject matter experts based on their contributions to relevant topics
- Knowledge Gap Detection: Surface topics where documentation is missing or outdated
Implementation
Phase 1: Data Infrastructure (Weeks 1-8)
Built connectors for all data sources with appropriate authentication and access control. Implemented incremental indexing to handle the 10M+ document corpus without disruption. Established data governance workflows to exclude sensitive content.
Phase 2: Retrieval Optimization (Weeks 6-14)
Experimented with embedding models, chunk sizes, and retrieval strategies using a curated evaluation dataset of 500 question-answer pairs created with domain experts. Achieved 85% accuracy on the evaluation set, up from 52% with baseline approaches.
Phase 3: Generation Tuning (Weeks 12-18)
Fine-tuned the generation model on company-specific terminology and writing style. Implemented guardrails to prevent hallucination and ensure all claims are grounded in retrieved documents. Added citation formatting that links directly to source documents.
Phase 4: User Experience & Rollout (Weeks 16-24)
Deployed integrations across Slack, Microsoft Teams, web portal, and IDE plugins. Ran a pilot with 2,000 users to gather feedback and refine the experience. Full rollout with training and change management support.
Results
Six months after full deployment:
- 50% Reduction in Search Time: Average time to find information dropped from 4.5 hours to 2.2 hours per week per employee
- 40,000 Daily Active Users: Half the company uses the assistant daily, with 92% user satisfaction
- 85% Query Accuracy: Users rate 85% of responses as accurate and helpful
- 30% Faster Onboarding: New hire time-to-productivity reduced from 6 months to 4 months
- $15M Annual Productivity Gains: Calculated from time savings across the organization
Advanced Use Cases
Code Documentation Assistant
Engineers can ask questions about the codebase and receive answers synthesized from code comments, architecture documents, and Slack discussions. "How does the authentication service handle token refresh?" returns a comprehensive answer with links to relevant code files and design docs.
Policy & Compliance Helper
HR and legal teams use the assistant to answer employee questions about policies. "What's the travel expense policy for international trips?" provides an accurate summary with citations to the official policy documents.
Incident Response
During outages, on-call engineers query the assistant for similar past incidents, runbooks, and resolution steps. This has reduced mean-time-to-resolution by 25% for recurring issue types.
"The Knowledge Assistant has become indispensable. It's like having instant access to the collective memory of the entire company. I can't imagine going back to the old way of searching through countless documents."
- Senior Engineering Manager, Technology Company
Security & Governance
Enterprise deployment required robust security measures:
- Access Control: Responses only include information the querying user has permission to access, enforced at retrieval time
- Audit Logging: All queries and responses are logged for compliance and debugging
- Data Residency: All processing occurs within the company's cloud environment with no data sent to external services
- PII Detection: Automatic detection and masking of personally identifiable information in responses
