AI-Powered Natural Language Interface for DevOps Monitoring

Overview

Telecom providers and financial institutions needed a solution to detect synthetic speech and protect against voice fraud in call centers. We built a real-time deepfake detection system for streaming audio using speaker recognition, diarization, ASR, Deep Fake detector model. Management needs monitoring data for decisions but has no SQL skills, so they depend on DevOps for every request and wait 1–2 hours while engineers spend 20–30% of their time on routine data pulls. An AI assistant in Slack lets users ask questions in plain English, turns them into read-only database queries with full audit trails, and escalates when unsure—cutting DevOps time on data requests by 60–80% and giving near-instant, self-service access to system performance, costs, and errors.

Achievements

The system was successfully deployed into production, reducing the DevOps team's workload on routine data requests by 60–80% and cutting response times from hours to under 30 seconds. By achieving an 80%+ self-service rate for management, the solution ensured 24/7 data access while maintaining 100% security compliance through automated audit trails and read-only enforcement. Additionally, the implementation of intelligent caching and multi-agent routing reduced AI operational costs by 40–60%.

Responsibilities

• Designed a multi-agent orchestration logic (Router, SQL, CSV, and Security agents) to handle complex user intents.
• Developed a security validation engine to prevent unauthorized database operations and mask sensitive information.
• Implemented context-aware conversation management, allowing users to ask follow-up questions naturally.
• Built a smart caching and retrieval system to reuse query results, optimizing both performance and API costs.
• Integrated the solution with Slack API and enterprise production databases, ensuring seamless adoption into existing workflows.