A custom RAG conversational intelligence platform trained on 50,000+ internal documents with sub-second retrieval.
Client
Project Type
Region
Duration
The Problem to Solve
Atlas had over 50,000 internal documents, technical manuals, and SOPs scattered across Confluence, Google Drive, and Notion. Engineering and support teams spent hours each week searching for answers, frequently acting on outdated documentation.
Engineering & Strategy
We designed and deployed a custom RAG (Retrieval-Augmented Generation) assistant using hybrid vector search, chunk reranking, and enterprise RBAC access controls. The bot verifies citations before answering, eliminating hallucinations.
Delivered Results
Internal query resolution time plummeted from 45 minutes to under 5 seconds. Support teams and engineers now query the assistant across Slack and their internal portal with 99.4% factual accuracy.
Delivered Scope & Key Capabilities
Vector embedding pipeline- Hybrid semantic search
- Slack & web bot interfaces
- Enterprise RBAC permissions
- Automated document re-indexing
- Citation & hallucination guardrails
Real Results Delivered for Atlas
Key operational milestones and performance indicators achieved following deployment.
Productivity Multiplied
Atlas team members reported saving an average of 4.5 hours per week on document search and SOP verification.
Key Takeaways & What the Client Can Now Do
Connect fragmented company knowledge into a unified, secure conversational interface.
Guarantee accurate answers with automated source citations and verification guardrails.
Deploy seamlessly across Slack, Teams, and web-based portals.
Enforce granular user permissions so confidential data is never leaked.
Automatically keep knowledge bases updated when source documents change.
