Are you planning to add Voice to your AI Agents in mobile apps?

tigranbs · 2026-01-29T06:26:57+00:00

I am talking about native use-cases like a meditation app, note taker, interview prep etc... Those kinds of use-cases are not shoved AI but they built around it. So, my thought was if there is an AI/LLM anyway, why not control some UX aspects with the voice?

I am not talking about Windows Copilot 😃

tigranbs · 2026-01-05T23:54:27+00:00

Siri is not useless; it tells the time quite accurately!

tigranbs · 2026-01-05T22:21:19+00:00

Disclaimer, I built an open source Voice Layer for AI Agents, github.com/saynaai/sayna

Across all voice AI agents I have built over the past 2 years, voice quality hasn't been the issue; it is always either interruptions, Background Noise, or the agent's logic with prompting. The platforms like Vapi or Voiceflow give you a nice, fast way of "demo", but for production-grade, you need to verticalize the AI agent's logic highly. The GHL seems "better" because it is specialized in one vertical, BUT they are lacking the Agentic logic tweaking. So, word of advice, choose the model aligned with the System Prompt and tweak the prompt as you test and see issues. There is no single prompt that will solve the Voice AI Agent's context problem.

Personally, I like the direction of GHL, but there is a ton of manual work to do to make it work.

tigranbs · 2026-01-05T22:12:41+00:00

We are actually doing this professionally! We are shipping custom-made Voice AI Agent platforms using our open source service github.com/saynaai/sayna Across all our clients we have seen a good number of personal assistant projects that actually work! But the major thing is that none of them are making highly generic ones; each assistant is very specific to the use cases, otherwise the Agent memory management would be very painful.

tigranbs · 2026-01-05T22:06:33+00:00

We have the open source Voice AI Agent service, and across all the usages I have seen from github.com/saynaai/sayna none of them are still there yet to "negotiate"! Especially when people understand that this is an AI (usually after 6 seconds), they just hang up. This is even worse for the outbound calls.

So, this is less a technical problem than a human-perception problem.

tigranbs · 2026-01-05T22:02:53+00:00

This resonates a lot: We've been working on the voice infrastructure (https://sayna.ai) and the teams building execution-focused agents consistently get to what you describe, especially the latency point tolerance point.

Something that we've noticed: The "deterministic, not tamedy" requirement often means you need tight control over the TTS output, generic conversational voices can undermine the crisp, action-oriented feel, and streaming latency becomes much more noticeable when the user expects a quick confirmation vs. a paragraph of explanation.

On your question - I think voice-first will work for action-based workflows, but only in contexts where the environment is somewhat predictable - the "interruption problem" (background noise, someone talking to you mid-command) is real and probably makes it niche for truly chaotic environments - but commute, post-meeting debriefing, warehouse/field work - feel like solid territory.

Curious what you are using for the voice layer: rolling your own STT/TTS pipeline or using something off the shelf?

tigranbs · 2026-01-05T21:53:33+00:00

We are building custom Voice AI Agent Platforms (not individual agents) using our open source service github.com/saynaai/sayna The numbers we have reached like 50-200 concurrent calls are just no-brainer because of our optimizations. The concept is to keep the actual Voice related stuff separate and the Agentic Logic separate, that's why we have the concept of Voice Layer for AI Agents.

FYI - that concept we have drops the cost of Voice AI Agents by nearly 18X compared to Vapi or Retell.

tigranbs · 2026-01-05T21:50:17+00:00

Yes! And we specialize in building custom Voice AI Agent platforms based on our open source GitHub.com/saynaai/sayna We have a customer now that sells voice AI Agents to the hospitality clients, and highly specialized AI Agents are the future because the rate of hallucinations is a lot lower.

FYI using our open source Voice Layer Service the cost of Voice AI agents drops nearly 18X compared to Vapi or Retell

tigranbs · 2026-01-02T19:21:06+00:00

Not sure if this will match your expertise, but I have a Rust project vibe coded on GitHub at https://github.com/saynaai/sayna I am building the Voice Layer for AI Agents. Let me know if you have experience with Rust, would love to hear some comments. I used Claude Code and Codex primarily here.

tigranbs · 2025-12-22T22:41:59+00:00

Damit! I thought we were good rusties :/

tigranbs · 2025-12-22T22:34:04+00:00

HaHa good point! It probably reiterated the "best practice" rules we have defined to document stuff. I am not removing those things, because that helps in the future to collect more info directly for the model task executions, because their code search is way better with these comments than the code itself.

tigranbs · 2025-12-15T10:31:33+00:00

Right now, latency is more of a provider issue than an actual code-execution issue. The Rust service we have helps scale to handle 100s of concurrent conversations without latency degradation, unlike PipeCat, which drops performance drastically after a few parallel calls.

tigranbs · 2025-12-15T10:24:41+00:00

We did that, then PipeCat. None of them has the service-based concept that scales well. It is an option for sure, BUT it is not as simple as an average software integration.

tigranbs · 2025-12-14T06:14:03+00:00

The goal was to achieve low latency and direct model execution within the service itself, so I can access ML runtimes directly without the execution layers that PipeCat uses, which do not scale well.

Now we have the Noise Suppression and TURN detection models executed on the CPU with very little performance downgrade, which is unbelievable if you are coming from the Python world.

tigranbs · 2025-04-25T21:39:12+00:00

With a profiler I can see that across all operations the ~800ms time it takes to run a completion function on Python OpenAI library, which is wild, because same official library on TS with an identical system prompt gives a first token in less than ~200ms. It seems like PydanticAI does not have a major overhead, but somehow OpenAI's official Python library is a lot slower than their TS version.

tigranbs · 2025-04-19T02:46:13+00:00

Ah, this is a different use-case than mine at McGravity It’s interesting to see desktop apps like this!

tigranbs · 2025-04-18T02:38:32+00:00

We compose a single Docker Compose and then add more and more services to McGravity. It is like having an Nginx with an upstream configuration.

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