Replacing a prototype backend with a production-grade AI call pipeline in 12 weeks
An AI voice platform for home service businesses had paying customers and a Series A four months out — on infrastructure that could not survive either.
The prototype stalled past 100 calls a day, analysed calls on a five-minute batch delay, silently lost 30% of bookings to a retry-less CRM client, and produced no analytics — so every enterprise demo answered “how do I know the AI works?” with “trust us”. Hiring the three senior engineers needed would have taken 12 to 18 months.
Four overlapping phases, each shipping a production increment rather than internal work in progress: an event-driven real-time call pipeline, an idempotent CRM sync engine, a call scoring and coaching layer, then optimisation and documented handover. Beta customers were on the new infrastructure by week five.
- —Event-driven real-time call ingestion and transcription pipeline
- —Emergency detection with escalation inside fifteen seconds
- —Idempotent CRM sync with exponential backoff and conflict resolution
- —Five-dimension call scoring with manager and CSR dashboards
- —Query tuning, model routing and full documentation handover
Capacity moved from 100 to 25,000 calls a day and CRM latency from five minutes to under five seconds, with sync success at 99.5% and AI spend down 60%. Customers grew from three to twenty-five within three months, and the client closed an $18M Series A on the resulting production metrics.
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