I built this copilot to tackle the challenge of interfacing with massive, complex enterprise systems. Could AI empower non-technical users to control intricate supply chain platforms using just plain English in Slack? This multi-agent, RAG-powered system does exactly that – talking to APIs, writing SQL, automating workflows, and proving AI can handle real-world enterprise complexity. After I built the initial version as a solo developer, I brought in my team, and we built a full-fledged enterprise solution.
Challenge
Enabling non-technical users to interact effectively with the extraordinarily complex proprietary enterprise platform (featuring over 300,000 API endpoints and 3,000+ database tables) via natural language in Slack for configuration, troubleshooting, and workflow automation.
My Role & Contribution
Lead AI Systems Architect & Developer. Designed and implemented the complete end-to-end multi-agent system, including:
- Custom Multi-Agent Framework orchestrating specialized agents (API interaction, SQL generation, documentation RAG)
- Seamless integration with Slack’s conversational UI
- Advanced RAG system processing 300k+ API specs, documentation, and internal knowledge bases with hybrid search and low-latency updates
- Schema-aware, reliable Text-to-SQL generation for complex database operations
- Agentic API playground enabling validated API call execution through natural language
- Sophisticated self-healing mechanisms and session management for complex multi-turn interactions
Key Technologies
Custom Multi-Agent Framework (Python), Claude 3, GPT-4o/3.5, Voyage AI Embeddings, Supabase Vector DB, Slack SDK, AWS, Redis, Langfuse.
Impact & Scale
Successfully integrated with a vast enterprise ecosystem, enabling complex operations through conversational interfaces, dramatically reducing time required for configuration and troubleshooting tasks.
★★★★★ (5.0) “Words won’t do Klim justice. He’s probably the smartest person I’ve ever met. If you have any needs, hire him.”
Source: Upwork — Client