In short
Mirai Minds builds voice AI agents that answer and place phone calls for support, sales and follow-ups. We build on Pipecat and LiveKit with Deepgram speech recognition, including Hindi, Cartesia or ElevenLabs voices, and Plivo or Twilio numbers. A caller can reach a live person mid-call, and every call is recorded, transcribed and turned into structured data.
What we build
ProblemCallers who need a person get stuck in a loop with a bot.
SystemThe agent raises a handoff that appears on the team's dashboard. The first person to accept joins the same LiveKit room; the claim is atomic, so two people can't take one caller.
ResultIf nobody accepts within 60 seconds, the agent says so and offers a callback instead of leaving the caller waiting.
ProblemDialing a contact list with a script that ignores how many calls are live, or what time it is.
SystemA queue-driven campaign worker with per-campaign and global caps on live calls, daily dial windows, international number validation and a voicemail status per contact.
ResultCampaigns run unattended inside the limits the operator sets.
ProblemOutbound agents start their script into ringback, call screening or a voicemail greeting.
SystemThe agent stays silent until a person speaks. A detector recognizes voicemail and carrier messages in English and Spanish and ends those calls with a short goodbye.
ResultThe greeting reaches a person, and voicemail outcomes are recorded per contact.
ProblemCall outcomes sit in recordings nobody listens to.
SystemAfter each call, a model reads the recording and fills fields: did a person answer, did they engage, was a callback requested and for when, was a meeting booked, and the exact words of any conflict.
ResultCallbacks, meetings and WhatsApp follow-ups are driven by data instead of memory.
ProblemMany Indian customers would rather speak Hindi than English on the phone.
SystemDeepgram Nova-3 recognition configured for Hindi, Cartesia voices and Indian phone numbers.
ResultAgents that hold Hindi calls for Indian businesses on our own platform.
Reference architecture
How it flows
The long version
Published
A call arrives on a Plivo or Twilio number and joins a LiveKit room over SIP. A Pipecat pipeline takes it from there: Deepgram turns speech into text, a language model decides what to say and which tools to call, and Cartesia or ElevenLabs speaks the reply while the caller can still interrupt. When the call ends, the recording and transcript are stored, and a model fills structured fields such as whether a callback was requested and when.
Turn-taking is where most voice agents feel wrong. Cut in too early and the agent talks over people; wait too long and it feels slow. In the outbound calling platform we use three layers: voice activity detection so short answers like "yes" register, end-of-turn detection from the speech model (Deepgram Flux where it fits), and a watchdog for turns that end with nothing usable. On outbound calls the agent also waits for a real person before its first line, instead of greeting a voicemail box.
| Consideration | Hosted platform (Vapi, Retell) | Custom build (Pipecat + LiveKit) |
|---|---|---|
| Time to a first call | Fastest: configure in a dashboard | Slower: pipeline, hosting and telephony to set up |
| Cost per minute | Provider costs plus a platform fee | Provider costs plus your hosting |
| Turn-taking and interruptions | The platform's settings | Full control, tuned per use case |
| Speech and voice providers | The platform's supported list | Any, including self-hosted models |
| Where audio and transcripts live | The vendor's cloud | Your cloud or ours |
| Who runs it | The vendor | You, or us on your behalf |
| Good fit | Pilots, standard flows, lower volume | High volume, Indian languages, strict data rules, custom handoff |
Our own Voice Agents platform supports both, per assistant: a hosted runtime such as Vapi, or our Pipecat pipeline. A pilot on a hosted runtime is often the right first step. We move to a custom build when per-minute cost, language quality or data rules make the case, and we say so when they don't.
We watch real calls. Each call has a recording, a transcript and structured fields, and CallAuditAI can score recorded calls against a rubric. The fixes are usually small: a prompt line, a turn-taking threshold, a pronunciation. We ship them like code, tested on past calls first, and a person stays reachable through handoff the whole time.
Shipped
iKoMatch
6,717Unified investor records
AI agents and automation · WhatsApp AI agents · Voice AI agents
CallAuditAI
Scorecards for an admissions counseling team's recorded calls
Custom LLM and document AI
Ethnic-wear e-commerce brand (India)
Visual search live for store staff since July 2026
Computer vision · Voice AI agents
AI outbound-calling startup
Live for outbound campaigns
Voice AI agents
Voice Agents
Live at voice-agents.miraiminds.co
Voice AI agents · WhatsApp AI agents
Myva.ai and CallPaaS
Both products built and extended across 2023–2025
Custom LLM and document AI · Voice AI agents
Stack
Chosen per project. We'll tell you when something simpler will do.
Process
Week 01
Discovery call
Thirty minutes with an engineer. You describe the job; we say whether AI is the right tool and what it would take.
Week 12
Scope and evals
We agree what done looks like and build a test set from your real data before writing the system.
Weeks 2–63
Build
Working software every week, measured against the test set, with your team trying it early.
Launch4
With human review
The system goes live with a person checking the risky steps and a clear way to reach a human.
Ongoing5
Run and improve
We watch real traffic, fix what breaks and tune on real cases, or hand over with documentation.
FAQ
On our platform the agent raises a handoff request, which appears at once on the team's dashboard. The first person to accept joins the same live call; the claim is atomic, so two people can't take one caller. If nobody accepts within 60 seconds, the agent tells the caller no one is free and offers a callback.
We measure it on every turn: the gap between the end of the caller's speech and the agent's first audio. The levers are how the end of a turn is detected, which model answers, how fast the voice starts streaming and how far the servers sit from the phone network. We tune those on your real calls rather than quote a lab figure.
Yes. We run Hindi calls on Deepgram Nova-3 recognition with Cartesia voices. For other languages we first check speech-recognition and voice support, then test on recordings from your own callers before anyone relies on it.
Either can be right. A hosted platform is the fastest way to a pilot. A custom Pipecat and LiveKit build costs more up front but gives control over turn-taking, providers, data location and per-minute cost at volume. We have shipped both, and a pilot on one can move to the other.
Your call scripts or a few recorded calls, access to the systems the agent should read or update (CRM, calendar, store), phone numbers or permission to provision them, and a person or team to take handoffs. For outbound work, the contact lists and the hours you are allowed to call.
Where you decide. On our platform they are scoped to your workspace. For one client we wrote each call's recording straight to their own Azure Blob storage using workload identity, so the app held no storage keys. Retention is agreed per deployment.
Each minute pays for telephony, speech-to-text, the language model and text-to-speech, plus hosting; hosted platforms add their own fee. Call volume, call length and model choice move the total most. We model it on your expected minutes before you commit.
Related
A 30-minute call with an engineer who has shipped this before. You leave with a plan, a rough timeline and what it would take — whether or not we build it.
