Build a CallSphere-Style Multi-Agent for HVAC Dispatch
By Sagar Shankaran, Founder of CallSphere
HVAC companies miss 40–60% of inbound. Build a 4-agent dispatch (intake, scheduling, parts, emergency) that integrates with ServiceTitan in 600 lines.
Key takeaways
TL;DR — At $2,500–$5,000 average ticket, missing 5 calls/week is $50–100k/year in lost revenue. Build a 4-specialist HVAC dispatcher (intake, scheduling, parts/quote, emergency) on OpenAI Realtime + ServiceTitan API. Per-minute cost: ~$0.07 vs. 0.30–0.80 for vendor stacks.
What you'll build
A 4-agent HVAC dispatcher: triage → (new-job intake | reschedule | parts/quote | emergency). Books to live ServiceTitan capacity, SMSes confirmations with tech name + ETA window, and pages the on-call for after-hours emergencies.
Prerequisites
- ServiceTitan API access (Marketplace integration).
- OpenAI Realtime + Twilio Voice + SMS.
- Python 3.11+,
openai-agents[voice],fastapi. - SMS opt-in language reviewed for your state.
- On-call rotation in Postgres.
Architecture
flowchart TB
C[Caller] --> TR[Triage]
TR --> NEW[New Job]
TR --> RES[Reschedule]
TR --> PQ[Parts/Quote]
TR --> EM[Emergency]
NEW --> ST[(ServiceTitan)]
RES --> ST
PQ --> ST
EM --> PAGE[On-call page]
Step 1 — Triage
```python triage = RealtimeAgent( name="triage", instructions="""You're dispatch for ABC Heating. Identify intent in 2 exchanges: new-job, reschedule, parts/quote, emergency. Emergencies (no heat in winter, gas smell, water leak) hand off to emergency immediately.""", handoffs=[new_job, reschedule, parts_quote, emergency], ) ```
Step 2 — New-job specialist
```python @function_tool async def find_open_slots(zip_code: str, day: str, urgency: str) -> list[dict]: techs = await st.techs.list_by_zip(zip_code, skill_set=["hvac"]) slots = await st.dispatch.aggregate_slots(techs, day, urgency_score=URGENCY_MAP[urgency]) return slots[:5]
@function_tool async def create_job(name: str, phone: str, address: str, slot_id: str, problem: str, system_type: str) -> dict: j = await st.jobs.create(customer={"name": name, "phone": phone, "address": address}, slot_id=slot_id, summary=problem, tags=[system_type]) ref = f"HV-{datetime.now():%Y%m%d}-{j.id:03d}" await sms.send(phone, f"Tech {j.tech_name} arriving {j.eta_window}. Ref {ref}") return {"ref": ref, "job_id": j.id, "eta": j.eta_window} ```
Hear it before you finish reading
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Step 3 — Reschedule specialist
```python @function_tool async def lookup_existing_job(phone: str) -> dict: j = await st.jobs.find_active(phone) if not j: return {"found": False} return {"found": True, "job_id": j.id, "current_slot": j.slot_iso}
@function_tool async def reschedule_job(job_id: str, new_slot_id: str) -> dict: j = await st.jobs.reschedule(job_id, new_slot_id) await sms.send(j.customer.phone, f"Updated to {j.eta_window}. Ref HV-{j.id}") return {"ok": True} ```
Step 4 — Parts / quote
```python @function_tool async def get_diagnostic_quote(zip_code: str, after_hours: bool) -> dict: z = await st.pricing.zone(zip_code) return {"diagnostic_fee": z.diag_fee + (z.after_hours_premium if after_hours else 0)}
@function_tool async def check_part_in_stock(model_number: str, part: str) -> dict: return await st.inventory.find(model_number, part) ```
Step 5 — Emergency
```python @function_tool async def page_oncall(summary: str, address: str, phone: str) -> dict: oncall = await rotation.who_is_oncall() twilio.calls.create(to=oncall.phone, from_=BUSINESS_NUMBER, url=f"https://you/page?summary={summary}") j = await st.jobs.create_emergency(summary=summary, address=address, phone=phone) return {"job_id": j.id, "paged": oncall.name} ```
Step 6 — Twilio bridge
Standard Realtime bridge. Add CSAT IVR ("press 1 if booked correctly") at end-of-call to populate quality metrics.
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Step 7 — Reporting
Track: book-rate (booked / total), AHT, average revenue per booking, after-hours emergencies/night, missed-call rate (always 0 with AI). Replace your old IVR dashboard.
Common pitfalls
- Static schedule copies. Always read live capacity — never overbook.
- Generic emergency thresholds. Per-region tuning matters (snowstorm = no heat is critical).
- Tech name privacy. Some companies prefer "your technician" vs. names — config flag.
How CallSphere does this in production
Explore a live demo and compare current plans to find the right fit for your business.
FAQ
ServiceTitan tier needed? "Marketing Pro" or higher for full API.
TCPA? Inbound is fine; outbound recall to cell phones needs PEWC.
After-hours pricing? Tool returns the surcharge so the AI can quote correctly.
Multi-trade (HVAC + plumbing + electric)? One trade parameter on every tool.
Voice tone? Use ElevenLabs for warmer voice for residential calls.
Sources

Written by
Sagar Shankaran· Founder, CallSphere
LinkedInSagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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