NeuralOps
A multi-agent orchestration platform for an enterprise logistics company. Replaced a team of 12 manual operators with AI agents handling 40,000+ decisions per day.
98.4% decision accuracy · 12× throughput · $2.1M annual savings
The client's operations team was processing 40,000+ route-planning, load-balancing, and exception-handling decisions per day. Each required a trained operator to review data from three systems, apply business rules, and make a judgement call. The team of 12 operators was at capacity, error rates were climbing, and scaling headcount further was economically unsustainable. They needed a system that could make these decisions autonomously — without sacrificing accuracy.
Week 1–2: Decision mapping
We embedded with the operations team for two weeks, shadowing operators and documenting every decision type, the data inputs required, the business rules applied, and the edge cases that required human judgement. We identified 23 distinct decision types, categorised them by complexity and frequency, and defined which could be fully automated versus which required a human-in-the-loop.
Week 3–5: Architecture design
We designed a multi-agent system using LangGraph for orchestration. A routing agent triaged incoming decision requests and dispatched them to specialist sub-agents — one for route optimisation, one for load balancing, one for exception handling. A supervisor agent monitored outputs and escalated to human review when confidence dropped below threshold. All agents shared a structured working memory via PostgreSQL.
Week 6–11: Build and integration
We built each specialist agent with a dedicated tool set: the route optimisation agent had access to maps APIs, historical route performance, and real-time traffic. The load balancing agent had access to warehouse inventory, vehicle capacity, and driver availability. All tool calls were structured with strict typing and logged in full. We integrated with the client's existing ERP via a FastAPI middleware layer.
Week 12–14: Eval, hardening, and handoff
We ran the system in shadow mode for two weeks — making decisions in parallel with human operators without acting on them. We compared outputs, identified disagreements, and tuned the agents. When accuracy exceeded 97% across all decision types, we began a phased rollout: AI-first with human review on exceptions, then full autonomy with random auditing. We built a monitoring dashboard so the operations manager could see decision volume, accuracy rates, and escalation patterns in real time.
“We were sceptical that AI could handle the edge cases our best operators deal with. Hostwire proved us wrong. The system makes better decisions than most of our team, and the two operators who remain are focused on the genuinely hard problems.”
