Industry-Vertical Conversational Agent for an Industrial Operator

The Challenge

The operations team was drowning in alerts. Across hundreds of sites, dashboards generated thousands of notifications per day, but only a small fraction were truly actionable. Operators spent 60%+ of their time triaging noise instead of fixing problems. Standard BI tools couldn't help because the real questions were ambiguous: 'Why is this string under-performing today? Weather, soiling, a faulty inverter, or wiring?' Off-the-shelf chat-with-your-data tools hallucinated over operational data — unacceptable in a regulated industrial setting.

The Solution

An agent-native reasoning system that grounds every recommendation in real time-series data, asset metadata, and physics-based calculations. The LLM accesses these via typed tool calls — not vector search over text. Every answer cites its sources, every interaction is audited, and sensitive data never leaves the customer's control via a single-inference-and-destroy pattern.

How We Built It

• 1Tool-calling architecture: typed wrappers around time-series DB, asset registry, weather APIs, and physics calculators
• 2Hybrid routing: small local model handles routine queries, larger model for complex reasoning — 10x cost reduction
• 3Citation pipeline: every numeric claim in an answer links back to a specific row in the operational data
• 4Single-inference-and-destroy: raw sensitive data stays out of LLM context retention; only redacted summaries persist in audit logs
• 5Tiered confidence scoring: each suggested action ranked so operators can audit before executing
• 6Auto-generated work orders for confirmed faults; humans review-and-send

Tech Stack