AI partner to a Fortune 500 critical-infrastructure technology leader, delivering an offline-capable voice AI companion that turns hands-free voice into the primary interface for field crews across power and rail operations. A latency-tuned real-time pipeline (VAD → STT → wake-word → LLM → TTS) combines with a provider-agnostic LLM backbone — swap between quantized on-device models for edge inference and frontier multimodal cloud LLMs via configuration — and an agentic supervisor that delegates to domain tools for voice-driven form filling, voice-guided procedures, multimodal inspection, crew briefings, and shift summaries. Engineered to enterprise-grade production standards: multi-tenant isolation, MLOps governance, CI/CD on Infrastructure-as-Code, and validated disaster recovery.
The Challenge
Field crews for critical-infrastructure operators — power, transmission, rail — work in environments where every minute on paperwork is a minute off the line, where connectivity is unreliable, and where mistakes carry real safety consequences. Existing tooling either lives on the laptop back at the truck or assumes constant cloud access; neither survives a real shift. Our partner, a Fortune 500 leader in energy and critical-infrastructure technology, needed a voice-first AI companion that could work hands-free in noisy outdoor conditions, fall back gracefully when connectivity drops, capture structured data accurately enough for compliance-grade records, and ship into a multi-tenant production environment serving multiple regulated clients without compromising isolation or auditability.
Our Approach
We own the voice-AI stack end-to-end. A latency-tuned real-time pipeline — Audio Capture → VAD → STT → wake-word → LLM → TTS — runs as a single orchestrated control loop tuned to the noise floor of real outdoor environments. The LLM layer is provider-agnostic: swap between quantized on-device models for GPU-accelerated edge inference and frontier multimodal cloud LLMs via configuration, with no application logic changes. A central agentic supervisor delegates to a clean interface/logic tool layer covering voice-driven form filling with strict validation, voice-guided procedures backed by state machines, multimodal asset and defect inspection, location and weather intelligence, crew briefings, and shift summaries; RAG over domain knowledge keeps responses grounded, safety-critical answers carry verify-before-acting disclaimers by system prompt, and a streaming background mode passively captures activity without forcing dialogue. Production-grade enterprise footing is engineered in: multi-tenant isolation via Row-Level Security and Tenant-ID filtering with RBAC, MLOps governance (prompt versioning, audit history, security logging), full CI/CD on Infrastructure-as-Code, token-budget rate limiting, and validated disaster-recovery protocols. A parallel R&D track prototypes fully on-premise inference on quantized open-source LLM families for clients with offline or data-sovereignty constraints.
Results
The platform turns hands-free voice into the primary interface for field work that previously consumed paperwork hours — capturing structured inspection and defect records into compliance-grade workflows, guiding crews through procedures step by step, generating end-of-shift summaries automatically, and answering safety-critical questions ("is it safe to work on the line today?") with grounded, source-cited context. Engineered with offline-first edge inference alongside cloud LLM access, multi-tenant isolation alongside agentic flexibility, and MLOps discipline alongside venture velocity, the system meets the bar a Fortune 500 critical-infrastructure operator actually needs in production — and onboards new client domains without re-architecting the core.
