Speko (YC S26) – OpenRouter for Voice AI

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-cTypical 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

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FL score

72

out of 100

Verdict

VALIDATE

high confidence

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A routing layer that automatically selects the best combination of speech-to-text, LLM, and text-to-speech models based on your constraints, eliminating manual benchmarking and vendor lock-in.

The pain

Teams building voice agents benchmark their model stack once, then never revisit it. New models launch monthly that are faster, cheaper, or more accurate, but switching vendors requires re-integration work and internal debate about metrics. Production systems run on outdated stacks while better options exist.

The gap

No abstraction layer exists between voice AI applications and the fragmented vendor ecosystem. Companies either commit to one vendor's full stack (losing optionality) or manually manage integrations across multiple vendors (high operational friction). The benchmarking data needed to make informed decisions is proprietary and scattered.

Build angle

Speko solves this by maintaining public benchmarks across all major STT/LLM/TTS combinations for specific languages and regions, then exposing a simple API that routes requests to the optimal model based on stated constraints (accuracy, latency, cost, balanced). The technical execution matters: prefetching signed session plans eliminates control-plane latency, and failover during connection setup prevents user-facing delays.

Strengths

  • Founder has 4 years of hands-on experience building multilingual voice agents at scale, understands the actual pain deeply
  • Clear product-market fit signal: customers explicitly want this (the 'thousands of calls a day' team quote is concrete validation)
  • Defensible moat through continuous benchmarking data that becomes more valuable as the model ecosystem expands
  • Low switching cost for customers once integrated, creates sticky usage
  • YC S26 backing suggests strong investor conviction
  • Solves a real operational problem that compounds over time as model velocity increases

Risks

  • Benchmarking at scale is labor-intensive: maintaining accuracy across 12+ vendors, multiple languages, and monthly new releases requires significant ops overhead
  • Vendor relationships are fragile: providers could change pricing, deprecate APIs, or restrict benchmarking access; no contractual guarantee of stability
  • Market size uncertainty: how many teams actually rebuild their voice stacks frequently enough to justify a subscription? Early adopters exist, but mainstream adoption depends on model churn acceleration
  • Competitive response: major cloud providers (AWS, Google, Azure) could build this internally or integrate it into their platforms, commoditizing the value
  • Data quality risk: if benchmarks are perceived as inaccurate or biased toward certain vendors, trust collapses and the entire business fails
  • Chicken-and-egg with benchmarking: need enough customers to justify benchmarking effort, but need benchmarks to attract customers

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