Milenow AI
AI Voice Bot for Logistics ERP — milenow.com
Company overview
Milenow runs logistics operations in the US where every shipment needs confirmation calls to vendors and drivers. As volume grew, a growing team of agents spent hours a day on repetitive calls and manual data entry — the bottleneck between more shipments and more headcount.
The business challenge
What we were trusted to solve.
Manual data entry and repetitive phone calls were slowing down operations as shipment volume grew. Order confirmations required hours of human effort and introduced significant accuracy errors.
Our solution
Integrated an AI-driven calling engine directly into the existing ERP, enabling real-time order confirmations, automated follow-ups with vendors and drivers, and infinite scalability without added headcount.
Technical constraints
The guardrails we designed within.
Live inside the ERP
The AI had to read and write the existing Symfony/MySQL system, not a parallel database.
Accuracy over cleverness
A wrong order confirmation is worse than a slow one — the bar was zero data errors.
Scale without headcount
Call volume spikes couldn't require hiring; the system had to absorb them.
Discovery process
Mapping the real system before touching it.
- 01
Call taxonomy
We recorded and categorized real agent calls to map every branch a confirmation could take.
- 02
Data contract
Defined exactly what the bot reads from and writes back to the ERP, field by field.
- 03
Fallback design
Planned graceful hand-off to a human for any call the model wasn't confident on.
Architecture decisions
How it fits together.
An AI calling engine sits beside the existing ERP: it places calls, understands responses, extracts structured order data, and writes confirmations straight back — with Redis queues absorbing spikes.
Voice pipeline, not a chatbot — Speech-to-text, an LLM reasoning layer, and text-to-speech tuned for phone-quality audio.
Queue-backed scaling — Redis-backed job queues mean hundreds of concurrent calls without touching the ERP's throughput.
Human-in-the-loop — Low-confidence calls route to an agent, so accuracy never trades against automation.
Technology stack
What it runs on.
Implementation timeline
From discovery to production.
Weeks 1–2
Call analysis & data contract
Mapped conversation flows and ERP integration points.
Weeks 3–7
Voice engine build
STT/LLM/TTS pipeline with confidence scoring and fallback.
Weeks 8–10
ERP integration & pilot
Live on a slice of shipments, measured against agent baselines.
Key features
What shipped.
Autonomous confirmation calls
Places and completes vendor and driver calls end to end.
Real-time ERP sync
Confirmed data lands in the ERP the moment a call ends.
Results & performance
The outcome, measured.
Order confirmation dropped from hours of manual calling to minutes of autonomous operation — with data accuracy the team could finally trust at 100%.
Auricorium's AI solution streamlined our logistics, reduced manual work, and boosted accuracy to levels we didn't think were possible.
Lessons learned
What we'd tell the next team.
Model the conversation, not just the prompt. Mapping real call branches upfront is what made accuracy possible.
Queues are the scaling story. The voice model gets attention; the queue architecture is what let it scale.
Related work
More platforms we shipped.
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