About the project (description, duration, stage)
The client is the largest US network of in-home veterinary hospice and end-of-life care. A major US private-equity sponsor drives the AI program and plans more projects across its portfolio.

We built a real-time voice copilot for their Veterinary Care Coordinators (VCCs). The copilot listens to live calls with pet families. It extracts appointment and clinical fields while the call runs. It fills the client's scheduling system through a Chrome extension. A second workstream, the

Vet Visit Copilot , sends each vet an AI pre-visit briefing by email (Amazon SES).

Next is the

production phase .

Stage:

production SOW in executive alignment; start expected September 2026.

Duration:

multi-month, with strong extension probability. 0.5 FTE minimum; ramp toward 1.0 FTE as production scales.

Why the role is open:

the current architect moves to another strategic build. He stays at

0.15-0.2 FTE for supervision and knowledge transfer

during ramp-up, so the new architect gets a structured handover.

Objective





Own the technical architecture and delivery of the voice copilot from validated PoC to production

Hit the bar this client tests against: latency, accuracy, concurrency, and cost

Keep expectations aligned: production polish is in scope now; protect the team from silent scope creep

Transfer knowledge continuously to the client's team and Neurons Lab engineers

Areas of Responsibility
Technical architecture & hands-on implementation

Own the full pipeline: streaming speech-to-text, LLM field extraction, Chrome-extension delivery, and AWS infrastructure

Drive latency work: cut P95 from ~6s toward ~2s; remove post-processing corner cases (occasional ~1min lag on one field type)

Run model A/B tests (current pair: Claude Haiku vs GPT Luna) with golden-set evaluation for phonetic name and email accuracy

Own evaluation and cost: Langfuse traces, accuracy dashboards, real per-call cost from live calls, and an optimizat

📌 AI Architect (Madrid)
🏢 Neurons Lab
📍 Madrid