# 24/7 AI Call Center: 200k Calls/Month, 60 HVAC Brands | Super Cat > AI voice agents answering 200k calls a month for 60 US HVAC brands, booking 67% of calls against 77% for human agents: real telephony, live booking, traced. Source: https://www.super-cat.tech/portfolio/ai-agent-call-center Last modified: 2026-10-22 [← All case studies](https://www.super-cat.tech/portfolio)200k calls/month · 67% booking rate vs 77% human # 24/7 AI Agent Call Center: 200k Calls a Month Across 60 US HVAC Brands Voice agents orchestrating 200,000 calls a month across 60 US HVAC brands for a 5,000-person enterprise, booking 67% of calls against 77% for human agents — within 10 points of a trained team, around the clock. Built as a product, an AI front desk that answers, books and routes, not a pilot. ## The problem For an HVAC brand the front desk is the business: a missed call is a missed booking, and after-hours and peak-hour calls went to voicemail that most callers never leave. Across 60 brands under one 5,000-employee US enterprise, staffing every shift for that volume was hard to justify, and leaving it unanswered cost real business. ## What we built An AI front desk that runs the call centre 24/7: real telephony with barge-in handling so callers can interrupt naturally, booking, rescheduling and cancelling against live availability, answers only from what each brand has approved, and tool-calling into live systems so the agent can look things up and write back rather than just talk. Before: Night and weekend calls unanswered; Staffing every shift hard to justify; A demo bot is not a call centre. After: 200k calls/month · 67% booking rate vs 77% human.Before - Night and weekend calls unanswered - Staffing every shift hard to justify - A demo bot is not a call centre How it works - 01 Answer on real telephony. Callers reach the agent on the phone line and can interrupt it mid-sentence, as they would a person. - 02 Book against real availability. It checks the live calendar, then books, reschedules or cancels directly in the scheduling system instead of taking messages. - 03 Say only what is approved. Questions are answered from what each brand has approved, never improvised. - 04 Hand over with context. When confidence drops, the call moves to a person with the context already written down. - 05 Test on real transcripts. Every release is regression-tested against real call transcripts before it ships. - 06 Trace every call. Each call is traced end to end, so a 3am failure is findable the next morning. After 200k calls/month · 67% booking rate vs 77% human Stack Voice AI AI Agent AI Receptionist Scheduling Telephony Enterprise ## Why it holds up Production is a different problem from a demo. This one ships with defined handoff to a human when confidence drops, regression tests over real transcripts before every release, and tracing on every call so a 3am failure is findable the next morning. It is live inside the business, not piloted in a sandbox: 200,000 calls a month across 60 HVAC brands, with a 67% booking rate against 77% for the human team. Related service: [Forward-deployed AI engineers](https://www.super-cat.tech/forward-deployed-ai-engineer) ## More case studies Big 4 client · fully on-premise ### Enterprise RAG Chatbot for a Big 4 Accounting Firm Multimodal retrieval over a Big 4 firm’s full compliance library, tuned for citation instead of summary — every answer arrives with its source paragraph attached. - Juniors reading thousands of pages per question - Seniors acting as search engines Read the case → US$1M collected in the first 3 months ### AI Payment Collection and Reminders The companion product to the AI front desk: it chases unpaid invoices with polite, timed reminders and a payment link, and stops the moment the customer pays or replies. It collected over US$1 million for the client in its first three months. - Chasing payment is awkward and late - Billable time lost to collections Read the case → Ask the EDI in plain English ### Natural-Language EDI for a US EDI Solutions Vendor Business users ask questions of raw X12 — “what are the net terms on invoice 4471?” — and get answers in plain English, from a Llama 3 model fine-tuned on EDI and grounded in the parsed transactions. - Business and EDI teams speak different languages - Rejected transactions decoded by hand Read the case → Get started ## Talk to the engineer who will build it. You will speak with me directly. Bring the use case, the data and the constraints, and I will give you a straight view on feasibility, effort and the first thing to ship. [Book a call](https://calendly.com/super-chain/catyung)Hong Kong & London time zones