AirCaps AI GTM brain

category

market profile

• text: Multi-speaker, noisy or distant audio creates transcription and attribution errors that propagate into downstream applications.
• text: Speech API buyers compare real-world accuracy, latency, speaker labeling and total audio-hour cost across providers.

personas

• title: Voice AI engineering lead
  pains:

  • Overlapping voices and noise degrade transcripts and agent behavior
  • Needs an API with predictable cost and a way to benchmark on production audio
• title: Product manager for meeting intelligence or captions
  pains:

  • Incorrect speaker labels undermine trust in summaries and searchable transcripts
  • Audio comes from distant microphones and real meetings, not clean studio recordings
• title: Enterprise ML infrastructure lead
  pains:

  • High audio volume makes per-hour unit economics material
  • Deployment and concurrency requirements may require private infrastructure

icp

• text: Software companies building meeting assistants, transcription, captions or voice agents with substantial multi-speaker audio.
• text: High-volume organizations processing thousands of hours monthly that need private deployments or custom limits.

message pillars

• text: Handle difficult real-world audio.
• text: Provide speaker-aware transcription.
• text: Make evaluation transparent.

objections

• objection: Is this more accurate on our audio?
  response: Run a blind comparison on representative noisy and overlapping recordings; vendor benchmark results may not generalize.
• objection: Can we deploy in our own environment?
  response: Discuss the advertised private cloud, on-premise or on-device options and availability for the specific model.
• objection: What does speaker labeling cost?
  response: A5S Async includes speaker labels in its audio-hour rate; confirm volume terms and required features.

competitors

• name: AssemblyAI
  website: https://www.assemblyai.com/products/speech-to-text
  why: Speech-to-text API with speaker diarization and noisy audio support.
• name: Deepgram
  why: Speech API competitor offering transcription and diarization.
• name: Gladia
  website: https://www.gladia.io/competitors/benchmarks
  why: Competes in speech transcription and diarization for challenging audio.

keywords

• speaker diarization API
• speech to text noisy audio
• multi speaker transcription API
• speaker identification API
• far field speech recognition
• speech to text per audio hour
• meeting transcription API

voice

• Technical and benchmark driven
• Direct
• Developer oriented

signals

• text: A team is building meeting transcription or voice agents and reports errors on overlapping speech.
• text: A buyer processes 10,000 or more audio hours monthly and seeks volume pricing or private deployment.

pricing

motion

channels

• developer documentation
• benchmarks
• X
• website

confidence

Founders

• name: Madhav Lavakare
  title: Founder/CEO
  role: CEO
  bio: I've been obsessed with audio for ages - I built my first voice assistant on a Raspberry Pi at age 13. I studied C.S. at Yale, where my senior thesis focused on extracting clean speech transcripts from noisy multi-speaker audio. In the past, I've researched audio AI at the MIT Media Lab where I was part of the team that built the world's first collaborative human-AI live musical concert.
• name: Nirbhay Narang
  title: Founder/CTO
  role: CTO
  bio: I studied C.S. at Cornell and graduated Summa Cum Laude. I built my first voice apps for AR glasses in high school (an emotional support tool for people with autism). In college, I spent summers as the first engineer at YC startups, building conversational and voice AI platforms for therapy. I've done HCI research at Cornell and have been supported by several fellowships (8VC Fellowship, Kessler Fellowship).