Demo: https://www.youtube.com/watch?v=no2LY2gRh-c
Typical production voice agent is an ensemble of three models: STT, an LLM, and TTS.
Each of those layers offers a dozen credible vendors, and each month there are new models on the market. Almost everyone evaluates once, picks a stack of their choice, and never rechecks because switching from a vendor to another involves yet another integration and arguments about the numbers.
The result is that you use voice agents running last quarter's models while better and cheaper options are available.
Before founding Speko, I spent four years as cofounder and CTO building voice agents for enterprises across Asia in 10+ languages. Each time a new speech model would arrive, we repeated the same ritual: hire native-speaking raters, benchmark it against our existing stack, and update production if it improved. Speko turns this process into an API. A team running thousands of calls a day told us: "we can literally go to this dashboard, switch the model, and it will do it for us."
How it works: you send a request with your optimization criteria (accuracy, latency, cost or balanced), language and region. The router filters to models which we measured for the given combination of constraints, benchmarks them, selects the winner, and returns a response with headers containing provider, model names, and the scores. The gateway prefetches signed session plans, so a new session dials the provider straight from memory; no control-plane round trip while a caller waits.
Failover happens only during connection setup stage: if the provider refuses the connection attempt, we start connecting to the runners-up.
Some of the customer stories: one founder came to us not knowing what to pick at all: he gave us his use case and now routes everything through the platform. A property management AI runs LiveKit in Python and had not updated STT or TTS since launch: they did not know their STT had high error rates on their calls, better options existed, and swapping always looked like an R&D project. One team did not know which models to pick for Spanish. A medical team did not know which STT handles medical vocabulary best. In every case we helped find the right stack from the benchmarks, and now they route through us.
The measuring part is public: we pass the same inputs to every model in one region in different dated runs and we publish the boards, including those where our selections perform worse than alternatives. A launch demo answers which 30-second clip sounds better; production asks which model survives minute eight, so we test spontaneous speech, money and dates, ten-minute takes, and the rankings change. We trained an automatic scorer for TTS naturalness on our blind head-to-head listening votes; on providers it has never seen a vote for, it picks the same winner our raters do about as often as raters agree with each other.
We don't train or sell models ourselves, that's precisely how we keep our rankings impartial.
We also open sourced the gateway for teams who want to avoid an extra network hop on the audio path and don't want to share keys with our cloud (https://github.com/SpekoAI/gateway, MIT): one Go binary, which is running as a sidecar in your agent's container, speaks one local protocol over Unix socket, pins provider hosts and attaches your keys. In BYOK mode it doesn't communicate with us at all.
Notice that the anonymous, content-free telemetry is enabled by default, and one env var disables it.
Cost: the gateway and BYOK setup will be free forever, we charge for the hosted router and managed keys with consolidated billing. Since we started the batch in late June, external usage has grown about 25 percent per week on average, front-loaded toward the launch weeks.
I would love feedback from the community: how do you pick speech models now, and what makes you trust the third-party benchmark?
https://speko.ai/"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["openrouter"],"value":"Launch HN: Speko (YC S26) \u2013 OpenRouter for Voice AI"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://speko.ai/"}},"_tags":["story","author_abdik","story_49332751","launch_hn"],"author":"abdik","children":[49333218,49333279,49333353,49333588,49333760,49333830,49333835,49334259,49334290,49334453,49334769,49335012,49335174,49335320,49336512,49337578,49340337,49340627,49341187,49342326,49345850,49353640,49356537,49363247,49370038,49406203],"created_at":"2026-08-17T15:36:18Z","created_at_i":1786980978,"num_comments":69,"objectID":"49332751","points":118,"story_id":49332751,"story_text":"Hi HN! I'm Bek, founder of Speko, a platform that finds an optimal combination of speech-to-text, LLM, and text-to-speech models, given your constraints, among all our public benchmarked options, and tells you why.
Demo: https://www.youtube.com/watch?v=no2LY2gRh-c
Typical production voice agent is an ensemble of three models: STT, an LLM, and TTS.
Each of those layers offers a dozen credible vendors, and each month there are new models on the market. Almost everyone evaluates once, picks a stack of their choice, and never rechecks because switching from a vendor to another involves yet another integration and arguments about the numbers.
The result is that you use voice agents running last quarter's models while better and cheaper options are available.
Before founding Speko, I spent four years as cofounder and CTO building voice agents for enterprises across Asia in 10+ languages. Each time a new speech model would arrive, we repeated the same ritual: hire native-speaking raters, benchmark it against our existing stack, and update production if it improved. Speko turns this process into an API. A team running thousands of calls a day told us: "we can literally go to this dashboard, switch the model, and it will do it for us."
How it works: you send a request with your optimization criteria (accuracy, latency, cost or balanced), language and region. The router filters to models which we measured for the given combination of constraints, benchmarks them, selects the winner, and returns a response with headers containing provider, model names, and the scores. The gateway prefetches signed session plans, so a new session dials the provider straight from memory; no control-plane round trip while a caller waits.
Failover happens only during connection setup stage: if the provider refuses the connection attempt, we start connecting to the runners-up.
Some of the customer stories: one founder came to us not knowing what to pick at all: he gave us his use case and now routes everything through the platform. A property management AI runs LiveKit in Python and had not updated STT or TTS since launch: they did not know their STT had high error rates on their calls, better options existed, and swapping always looked like an R&D project. One team did not know which models to pick for Spanish. A medical team did not know which STT handles medical vocabulary best. In every case we helped find the right stack from the benchmarks, and now they route through us.
The measuring part is public: we pass the same inputs to every model in one region in different dated runs and we publish the boards, including those where our selections perform worse than alternatives. A launch demo answers which 30-second clip sounds better; production asks which model survives minute eight, so we test spontaneous speech, money and dates, ten-minute takes, and the rankings change. We trained an automatic scorer for TTS naturalness on our blind head-to-head listening votes; on providers it has never seen a vote for, it picks the same winner our raters do about as often as raters agree with each other.
We don't train or sell models ourselves, that's precisely how we keep our rankings impartial.
We also open sourced the gateway for teams who want to avoid an extra network hop on the audio path and don't want to share keys with our cloud (https://github.com/SpekoAI/gateway, MIT): one Go binary, which is running as a sidecar in your agent's container, speaks one local protocol over Unix socket, pins provider hosts and attaches your keys. In BYOK mode it doesn't communicate with us at all.
Notice that the anonymous, content-free telemetry is enabled by default, and one env var disables it.
Cost: the gateway and BYOK setup will be free forever, we charge for the hosted router and managed keys with consolidated billing. Since we started the batch in late June, external usage has grown about 25 percent per week on average, front-loaded toward the launch weeks.
I would love feedback from the community: how do you pick speech models now, and what makes you trust the third-party benchmark?
https://speko.ai/","title":"Launch HN: Speko (YC S26) \u2013 OpenRouter for Voice AI","updated_at":"2026-08-23T05:12:58Z","url":"https://speko.ai/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"FinnLobsien"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["openrouter"],"value":"Stripe to Buy OpenRouter for $7B"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["openrouter"],"value":"https://www.bloomberg.com/news/articles/2026-08-16/stripe-nears-deal-to-buy-ai-firm-openrouter-for-over-7-billion"}},"_tags":["story","author_FinnLobsien","story_49330139"],"author":"FinnLobsien","children":[49330459,49330487,49330499,49330560,49330754,49330919,49331269,49331278,49331285,49331436,49331698,49331743,49331769,49331806,49331996,49332251],"created_at":"2026-08-17T13:00:44Z","created_at_i":1786971644,"num_comments":47,"objectID":"49330139","points":97,"story_id":49330139,"title":"Stripe to Buy OpenRouter for 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For anything that needs more accuracy \u2014 Google Vision, Gemini, or any OpenRouter model \u2014 you bring your own API key. No subscription, no markup on your usage.\nExport as TXT, JSON, XML or PDF. AI engines support custom prompts so you can translate, extract form fields, or get structured output in one step.\nApp: https://onlineocrfree.qzz.io\nSource: https://github.com/naimurhasan/online-ocr-free"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["openrouter"],"value":"Show HN: Online OCR Free \u2013 Batch OCR UI for Tesseract, Gemini and OpenRouter"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://onlineocrfree.qzz.io"}},"_tags":["story","author_naimurhasanrwd","story_47238255","show_hn"],"author":"naimurhasanrwd","children":[47239329,47239602,47247126],"created_at":"2026-03-03T20:12:55Z","created_at_i":1772568775,"num_comments":6,"objectID":"47238255","points":14,"story_id":47238255,"story_text":"Built this because people working with large document sets had no free tool that handled batch processing cleanly.\nTesseract is free and runs locally. For anything that needs more accuracy \u2014 Google Vision, Gemini, or any OpenRouter model \u2014 you bring your own API key. No subscription, no markup on your usage.\nExport as TXT, JSON, XML or PDF. 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DocsRouter is that layer as a product: one stable API that talks to multiple OCR engines and vision LLMs, lets you route per document based on cost/quality/latency, and gives you normalized outputs (text, tables, fields) so your app doesn\u2019t care which provider was used.
It\u2019s meant for teams doing serious stuff with documents: invoices/receipts, contracts, payroll, medical/admin forms, logistics docs, etc., who are either stuck on \u201cthe OCR we picked years ago\u201d or are overwhelmed by the churn of new vision models.
Right now you get a REST API, simple SDKs (coming soon), a few pluggable backends (classic OCR + newer vision models), some basic routing policies, and a playground where you can upload a doc and compare outputs side by side.
I\u2019d love feedback from HN on two things:
1- If you already juggle multiple OCR/vision providers, what does your homegrown router look like, and what would you need to trust an external one?
2 - Would you prefer this or use the LLM/OCR providers directly, with the possibility of changing the provider every so often?
Demo and docs are here: https://docsrouter.com"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["openrouter"],"value":"Show HN: DocsRouter \u2013 The OpenRouter for OCR and Vision Models"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://docsrouter.com"}},"_tags":["story","author_misbahsy","story_46310013","show_hn"],"author":"misbahsy","created_at":"2025-12-18T07:55:18Z","created_at_i":1766044518,"num_comments":0,"objectID":"46310013","points":14,"story_id":46310013,"story_text":"Most products that touch PDFs or images quietly rebuild the same thing: a hacked-together \u201crouter\u201d that picks which OCR/vision API to call, normalizes the responses, and prays the bill is sane at the end of the month.
DocsRouter is that layer as a product: one stable API that talks to multiple OCR engines and vision LLMs, lets you route per document based on cost/quality/latency, and gives you normalized outputs (text, tables, fields) so your app doesn\u2019t care which provider was used.
It\u2019s meant for teams doing serious stuff with documents: invoices/receipts, contracts, payroll, medical/admin forms, logistics docs, etc., who are either stuck on \u201cthe OCR we picked years ago\u201d or are overwhelmed by the churn of new vision models.
Right now you get a REST API, simple SDKs (coming soon), a few pluggable backends (classic OCR + newer vision models), some basic routing policies, and a playground where you can upload a doc and compare outputs side by side.
I\u2019d love feedback from HN on two things:
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2 - Would you prefer this or use the LLM/OCR providers directly, with the possibility of changing the provider every so often?
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