We build production voice AI agents on Vapi — inbound receptionists, outbound SDRs, and appointment setters. BYOK stack, sub-400ms latency, and a fixed-scope deploy in 2–3 weeks.
What We Build on Vapi
We've built each of these in production. Each has its own prompt architecture, integration pattern, and latency budget — and we ship them as fixed-scope builds.
24/7 inbound call handling — answers FAQs, qualifies callers, routes to the right department, and books appointments directly into your calendar. Zero hold time, zero missed calls.
Common in: Medical, dental, legal, real estate, home services
55–75% call containment rate
Automated first-touch outbound calls at scale — qualification questions, CRM field population, and meeting booking with your sales team. Warm handoffs only; the agent qualifies before it transfers.
Common in: SaaS, insurance, mortgage, staffing
3–5× more first-touch coverage per SDR
Outbound confirmation and re-engagement calls — schedule new appointments, confirm existing ones, handle cancellations and rescheduling with real calendar availability. Connects to Google Calendar, Calendly, and most EHR systems.
Common in: Healthcare, medspas, automotive, home services
40–60% reduction in no-shows
Handles tier-1 inbound support calls — account status, order tracking, billing questions, basic troubleshooting. Escalates to a human only when the issue requires it, with full context passed on.
Common in: E-commerce, SaaS, financial services, utilities
60–70% fewer escalations to human agents
BYOK Economics
Vapi is BYOK-first — you bring your own LLM, STT, and TTS keys and pay provider rates directly. We go a step further: we build a custom LLM backend that sits between Vapi and your models, giving you prompt routing, context management, and knowledge base integration that Vapi's native setup cannot do.
Typical all-in cost per minute
| Component | Cost |
|---|---|
Vapi platform fee Required on all plans | $0.05 / min |
STT — Deepgram Nova-2 (BYOK) Fast, accurate transcription | ~$0.008 / min |
LLM — GPT-4o mini (BYOK) Ideal for structured call flows | ~$0.02–0.05 / min |
TTS — ElevenLabs Flash (BYOK) Sub-200ms generation | ~$0.10–0.15 / min |
Telephony — Twilio inbound | ~$0.009 / min |
| Total all-in | $0.23–0.33 / min |
Prompt routing by turn complexity
Simple FAQ turns use GPT-4o mini. Complex reasoning or multi-step decisions escalate to GPT-4o. Same quality, 60–70% lower LLM cost on average.
RAG on your knowledge base
Before every LLM call, we retrieve relevant chunks from your product docs, FAQs, or pricing — injected as context. The agent always has the right information without bloating the system prompt.
Conversation summarisation
Every N turns, we summarise the conversation history and replace it with a compact summary. Long calls stay fast — response latency doesn't degrade at minute 10.
Tool call handling
CRM lookups, calendar availability, account status — all routed through your webhook server, not Vapi's native tool layer. You own the integration logic.
Sub-400ms
Response latency
end-to-end target
2–3 wks
Deploy timeline
fixed scope, firm quote
55–75%
Call containment
without human agents
$0.23–0.33
Per minute
all-in BYOK cost
Common Vapi Pitfalls
Most Vapi builds hit the same five walls. We know where they are and how to get around them before they become your users' problem.
Pitfall
Latency creep
Impact
Default Vapi setups often sit at 800ms–1.5s response latency. Conversations feel robotic and users start interrupting.
Our fix
We profile the full STT → LLM → TTS chain. We use GPT-4o mini with streaming for low-latency turns, reduce system prompt size, and choose ElevenLabs Flash for sub-200ms TTS generation. Target: sub-400ms end-to-end.
Pitfall
Interrupt collision
Impact
Wrong interruptionThreshold settings cause the agent to cut off users mid-sentence or talk over them — the two most common reasons users hang up.
Our fix
We calibrate interruptionThreshold through real call testing across speaker types and environments, not just against synthetic test data. The result sounds like a natural human conversation.
Pitfall
Voicemail misdetection
Impact
Outbound agents that cannot reliably distinguish a live answer from a voicemail waste calls, leave confused partial messages, and burn through your contact list.
Our fix
We configure Vapi's voicemail detection model and write separate voicemail scripts optimised for message completeness — the agent leaves a clear, professional message and logs the attempt in your CRM.
Pitfall
Context window bloat
Impact
Long conversations accumulate tokens. At 10+ minutes, LLM response time degrades noticeably — the exact calls where conversion is highest.
Our fix
We implement rolling conversation summarisation — the agent compresses earlier turns into a compact summary before they exceed the budget, keeping the active context tight without losing conversational continuity.
Pitfall
Failed SIP transfers
Impact
Misconfigured SIP transfer settings drop calls or leave the human agent without context. This destroys the handoff experience.
Our fix
We build structured transfer payloads — the SIP REFER carries caller name, reason for transfer, and a JSON summary of the conversation — so your human agent picks up already briefed.
How We Work
Fixed scope, firm quote before work starts. Here is exactly what happens during a Vapi engagement.
Deliverable at the end
A fully deployed Vapi voice agent on your phone number, integrated with your systems, with monitoring and runbook documentation. Not a prototype — production-ready.
Book a 30-minute call. We'll scope your use case, define the agent architecture, and give you a fixed price and timeline before any work starts.