Search inside Indian-language podcasts by what was said, then share the clip
TL;DR Cueseek turns a publisher's back catalogue of Hindi and regional-language podcasts into searchable text. Listeners type a question and land on the right minute, and editors find quotes and cut clips for social posts. Podcast networks and audio apps pay a monthly fee based on hours indexed.
Indian podcasting is large and mostly not in English. IBEF estimates an audience of about 200 million and says up to 85% of Indian podcasts are made in regional languages[1]. A market-research firm puts monthly listeners at around 215 million in 2025, with vernacular content taking 68% of daily streaming[2]. Yet search in most apps reads only titles and show notes.
So a two-hour Marathi interview where a guest explains crop insurance is invisible to the person asking that exact question. Editors hit the same wall when hunting for a quote across 400 old episodes.
The seed pitched natural-language search for music and audio in general. Music discovery by mood or description is already built into the big streaming apps, and in India one music-licensing startup matches music to creators' videos with AI[3]. Spoken word is where the gap is.
Cueseek ingests a publisher's audio, transcribes it with open Indian-language speech models, and indexes it by meaning as well as keywords. A listener can type "what did she say about home loans" in Hinglish and jump to 47:12. An editor gets a search screen across the whole archive with one-click clip export and a caption file. Publishers embed the search in their own app or site, and Cueseek stays in the background. It sells search and clips, not a listener app, and does not host or distribute anyone's shows.
The payer is a podcast network, audio-streaming app or news publisher with a deep archive. Users are their listeners and in-house editors. Comparable tools are priced in dollars - Listen Notes charges a US$200 (about ₹19,300) base for its pro API[4]. Cueseek would price per indexed hour plus a monthly platform fee, with a lower tier for single-show studios.
Open Indian speech models have changed the cost, and by a lot. One public research lab's open speech model covers 22 scheduled languages under an MIT licence, and its newer model covers 25[5]. Two years ago a startup would have paid per minute to a foreign API with weak accuracy in Bhojpuri or Odia. Video podcasts are also growing, so the demand for short clips keeps rising[2].
Nobody publishes a figure for spending on podcast search in India. The closest neighbour is the podcast market itself, where one firm estimates US$288M (about ₹2,800 Cr) in 2025, mostly advertising[2]. These vendor estimates vary widely, so treat them as context. The narrower anchor is the share in regional languages, which IBEF puts at up to 85%[1].
One global podcast search engine indexes about 3.8 million podcasts and 196 million episodes and sells transcripts on paid plans, but it searches the whole web of shows, not a publisher's own archive, and is English-first[4]. The Indian music-matching startup raised US$1.5M (about ₹14.5 Cr) in 2021 for AI music matching for video creators[6], which is music, not speech.
The big Indian audio apps may build in-house search, but I found no Indian vendor selling it to publishers.
| Difficulty | Moderate (42/100) |
| Starting capital | ₹65–125 L |
| Time to launch | 6–9 months |
Transcription accuracy on code-mixed speech, crosstalk and cheap microphones is the core technical job. Selling is the other hard part - there are only a few dozen networks with big archives, so each sale matters and each loss hurts. Expect long pilots before anyone signs a yearly contract.
No sector licence. Searching a client's own audio needs no permit, so the work is in contracts and data rules.
Building this outside India? The categories above usually have a local equivalent, so check with your own regulator.
AI is the product here, so the uses are about doing the core job well.
Don't trust transcripts blindly on names, numbers or anything a guest might dispute. A human should check any clip before it's published.
Want to work out the AI layer for Cueseek? Talk to Yash →
I think this suits you if you have speech-AI skills and contacts in Indian audio publishing. If you're hoping to build a consumer listening app then it's the wrong idea. First prove that two networks will pay to index at least 1,000 hours each, and that listeners who search come back.
Plan for roughly ₹65–125 L, much of it on speech-AI engineering and long pilots with publishers.
No sector licence. You need DPDP compliance for personal data, contracts that leave copyright with the publisher, and GST.
Yes, but the gap is widest for code-mixed and regional languages. In English-heavy markets you'd face more existing search tools.
The free Cueseek validation kit has the line-by-line capital breakdown, the effort scoring, the data and copyright checklist, a 14-day validation sprint with pass/fail targets, a 90-day launch plan, 10 interview questions for publishers, and 5 assumptions to test. Download the kit →
Thinking about building Cueseek or something close to it? The make-or-break work is transcription that holds up on code-mixed, noisy Indian speech, and I can help you plan a pilot that tests it honestly. If you need a hand, you know where to find me. Talk to Yash →
For illustration only. Names are fictional and ideas hypothetical; sourced facts are real but may be out of date. Nothing here is legal, financial or professional advice, and Yash Khatri is not personally liable for any use of it. Read the full disclaimer.