Speko functions as an intelligent router for the entire voice AI technology stack.
Speko transforms the voice AI workflow by acting as a dynamic switchboard for the entire voice pipeline.
Building a reliable voice agent usually requires piecing together three specialized components: a speech-to-text engine, a large language model, and a text-to-speech system. The challenge lies in their rapid obsolescence—within months of deployment, a cheaper alternative or faster model may emerge, yet replacing providers often demands exhaustive testing and extensive code changes that deter most teams from switching.
Speko solves this by acting as an intelligent router. Instead of embedding fixed vendor choices, users submit their priorities—such as latency, cost, or accuracy—alongside language and region preferences. The platform then leverages its own benchmarked dataset to select the best-performing model combination for each request, eliminating the need for manual vendor lock-in.
How the system ensures seamless performance
The architecture is designed to remove delays in real-time interactions by minimizing control-plane bottlenecks. Users can fine-tune their requests with specific optimization goals:
- Define priorities: Specify whether the highest priority is lowest latency, maximum accuracy, or a balanced trade-off, while targeting a particular region.
- Automated selection: The router queries its public benchmarks to pinpoint the top-performing model for that exact configuration.
- Preloaded sessions: To avoid caller delays, the gateway pre-fetches signed session plans in memory. This allows direct connection to the provider without an additional network round trip.
- Instant failover: If the primary provider connection fails during setup, the system automatically switches to the next-best model without interrupting the user.
For teams concerned about network overhead or exposing API keys, Speko’s gateway is open-sourced as a Go binary. It can be deployed alongside existing services as a sidecar, using Unix sockets to securely manage provider connections and credentials locally.
# Example gateway setup (conceptual)
# Deploy the Go binary as a sidecar to handle local routing and credential attachment.The foundation of Speko’s effectiveness lies in its rigorous benchmarking methodology. Unlike fleeting demonstrations or short audio clips, their evaluations use continuous, 10-minute recordings of spontaneous speech to assess performance in extended conversations. For text-to-speech naturalness, they’ve developed an automated scoring system based on blind human tests, achieving alignment with professional evaluator ratings.
Because Speko does not develop or sell proprietary models, its rankings remain objective. This independence ensures developers can confidently upgrade components—such as replacing a low-accuracy speech-to-text model with a high-performance alternative—without overhauling their infrastructure. The result is a voice AI stack that adapts to new advancements through a simple API call, reducing the complexity of model management to a few lines of code.
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Confused about the actual difference from LiveKit Gateway. Why wouldn't someone just use Vapi to avoid the setup headache?
Speko differs because it acts as an intelligent router: instead of locking you into a single STT, LLM, and TTS vendor, you submit your constraints (latency, cost, accuracy) and region, and the platform routes each request to the current best-performing combination using its own benchmarked data, so you're not stuck rewriting your pipeline every time a cheaper or faster model emerges.
This looks promising! Is the setup easy for a complete beginner or is there a steep learning curve? One concrete step that helps is specifying your optimization criteria, such as lowest latency or highest accuracy, along with the target region, which the router uses to select the best model combination from its benchmarks.
The missing link is annoying! Is the speko.ai site actually working for you guys right now? I’m curious if the intelligent routing actually helps avoid the usual headache of manually benchmarking and rewriting code every time a better model comes out.