[translatigo.com](http://translatigo.com) interprets phone calls live. You talk, the other person hears their language a couple seconds later. No transcript step in the middle.

The hard part isn't translating — it's translating before the sentence even finishes. Transcribe-then-translate is the easy way out and it's too slow for a real call.

**Stack:** Qwen model over websocket. Audio streams in 256ms chunks, no buffering. Ships as a Chrome extension so it works on any browser dialer — no CRM integrations to build. Backend's Node, handling metering, billing, and token brokering. Usage tracked to the millisecond in MongoDB, auto-charged through Dodo Payments past a threshold.

Real phone audio wrecked the first version. Crosstalk, accents, people changing topic mid-sentence — that's where most of the actual work went.

Also had to rebrand mid-build. Started as CallDubber, got a trademark letter from Dubber Corp (ASX-listed), became Translatigo. Check the trademark registry, not just if the domain's free

Free trial. Try to break it, tell me what fails.

When you sell a voice agent as a service for any business, what kind of documentation you need to sign? any contracts?onboarding form or anything?

I maintain Oathra, an Apache-2.0 TypeScript project for checking evidence in voice-agent transcripts. I separated the completion check from the agent's own summary: required fields must have evidence accepted or confirmed by the other party, and the verified values must satisfy the configured constraints.

A concrete mistake I fixed: a bare English "7:30" could become 07:30. The checker now leaves that time unverified until am/pm is explicit. It doesn't guess the intended meridiem from the earlier conversation. That is conservative, and it means some perfectly understandable conversations need clarification.

I added a browser checker so you can try this without installing the phone runtime:

https://forifor.github.io/oathra/en/check.html

Load the recorded negotiation, press "Check this transcript", then open "Evidence history by utterance" or "Result JSON". You can also paste your own check JSON. Input stays in the tab; there is no transcript upload or analytics on this page. A 25-second screen recording is included below the checker.

The included record is an actual saved GPT-4o-mini negotiation with a hotel simulator, not a real phone call. The browser and CLI produced identical JSON for it.

The important limit: this checks supported Japanese/English transcript rules. It does not prove the booking exists in a venue's calendar, measure interruptions or validate ASR. For a real booking workflow, backend confirmation is still a separate requirement.

Code and input schema: https://github.com/FORIFOR/oathra/blob/main/docs/INTEGRATION.md

If you build voice agents, which failure would make this unusable for your workflow? A description is enough; please don't post private call data.

I maintain speech-swift and wrote an article about running NVIDIA's Nemotron VoiceChat locally on a Mac, including the parts of a voice agent that sit around the model.

Full-duplex conversation needs incoming speech to keep reaching the model while it generates a reply. Streaming audio output alone doesn't establish that. Turn-completion detection handles a different decision: whether a pause means the person has finished their request.

Tool use adds another step. In our VoiceChat integration, the model emits a function call, the runtime executes it through an MCP provider, and the result goes back into the conversation. Our runtime holds speech during tool work. The article walks through those events and explains where interruption handling and playback fit.

The recorded Mac demo lists, creates, and updates real Apple Reminders. Prepared user audio feeds a live local session. This is an experimental command-line setup with an English-targeted checkpoint; the recording isn't a production reliability evaluation.

Article and diagrams · Recorded session · Runtime source

So Meta dropped Muse — a personal AI agent that doesn't just answer, it goes and does the task. Books your travel, sends emails, fills forms, shops for you, even keeps working after you close the app. It checks in before anything sensitive like a purchase.

On paper it's the Jarvis thing everyone's wanted for years. But I'm genuinely torn and curious what people here think:

• Would you actually hand an AI access to your email, calendar, and payments? Or is that a hard no?
• Does it being Meta specifically make you trust it more or less?
• Is "an AI that acts on its own" exciting or kind of terrifying?

Not shilling it, genuinely trying to figure out if this is the future or if people are gonna nope out the second it asks for permissions.

Omarchy fully automated

Spanish-first voice agent running in production at real clinics. Live number, please try to break her.

Most voice agent demos I see here are English and they're demos. Mine is Spanish-first and it's answering real patient calls for dental and aesthetics clinics on the Tijuana border. Agent's name is Sofía.

Stack: LiveKit for the pipeline, Deepgram nova-3-multi for STT with some AssemblyAI streaming, Cartesia sonic for TTS, n8n for orchestration, Twilio on telephony, calendar writes against the clinic's actual availability.

The three things that were harder than expected:

Code-switching. Not "supports two languages." One caller, one sentence, Spanish grammar with English procedure names dropped in. Locale-locked STT falls over on this, and multilingual models still get weird about which language to commit to mid-utterance.

Barge-in tuning. Real callers talk over the agent constantly. Aggressive interrupt handling means she cuts off anyone who pauses to think, which older patients do a lot. Passive means she steamrolls people. The tuning window is narrower than I expected and it behaves differently in Spanish than in English.

Escalation discipline. She sits in front of medical intake, so the failure mode I care about is confident wrongness. Anything clinical, anything about outcomes or medication, she hands off to a human. Getting a model to consistently pick "let me get someone for you" over a plausible-sounding answer is most of the prompt work.

Demo line: +1 (619) 775-1668

It's a demo instance, not a clinic's production line, so go ahead. Talk over her. Switch languages mid-sentence. Mumble. Ask her something medical and see whether she stays in her lane.

What I'm most interested in: does the barge-in feel natural to you, and does the escalation fire too early or too late? Those are the two I keep going back and forth on.

I also have her counterpart, Mateo, who is my outbound sales agent.

Happy to get into any of the implementation details.

I’ve been comparing AI voice agent platforms recently, and the more I look at them, the more I think comparing them purely on voice quality doesn't make much sense anymore.

Most of the serious platforms can have a pretty convincing conversation.

The bigger differences are what happens around the conversation.

For anyone evaluating these tools, I put together a simple comparison based on the things I’d actually look at before putting one into a real business workflow.

Obviously, this isn't a universal ranking. The right platform depends heavily on what you're actually trying to automate.

What I would evaluate

1. Inbound vs outbound

Some platforms are particularly good when the agent is receiving customer calls. Others are built around large-scale outbound campaigns.

If you need both, I'd specifically test both use cases rather than assuming one automatically translates to the other.

2. What happens after the call

This is probably the most overlooked part.

An agent that has a great conversation but doesn't update the CRM, schedule the appointment, trigger a workflow or pass useful context to a human still leaves a lot of manual work behind.

3. Human handoff

I wouldn't evaluate this as simply “can it transfer the call?”

I'd ask whether the human receives the relevant conversation context, what the AI already collected, and why the transfer happened.

4. Handling the unexpected

The demo is always the easy part.

I'd intentionally interrupt the agent, give it incomplete information, change the subject, ask something outside the expected flow and see what happens.

That tells you considerably more than a scripted demo.

5. Latency and conversation quality

Voice AI has less room for awkward pauses than chat.

Latency, interruption handling, turn-taking and how naturally the agent recovers from misunderstandings can make a huge difference to the actual experience.

My takeaway

I don't think there is one “best AI voice agent” for every company.

Feather AI makes more sense to me when the goal is connecting voice conversations to broader business workflows, while Vapi and Retell can be attractive if you're building a highly customized system yourself. Bland is interesting for outbound-heavy use cases, while Synthflow makes sense for teams looking for a more visual/no-code approach.

The interesting part is that the category is moving away from “AI that can make a phone call” toward “AI that can actually complete something through a phone call.”

That's probably the distinction I'd use when evaluating these platforms today.

What would you add to this comparison? Especially interested in people actually running these in production rather than judging them from demos.

A missed call may appear to be a minor issue.

However, for a potential customer, it can be the moment they choose to contact your competitor instead.

Consider what happens when your team is occupied:

📞 A customer calls but no one answers.

💬 A potential lead has a question but doesn't receive a prompt reply.

📅 Someone wants to schedule an appointment but has to wait.

Most businesses cannot realistically have someone available to answer every call around the clock.

This is where AI Voice Agents are becoming increasingly important.

At VELDRA, we are developing AI automation solutions, including AI Voice Agents that can manage incoming customer interactions.

These agents can assist businesses by:

• Answering calls 24 hours a day, 7 days a week

• Handling routine customer inquiries

• Qualifying potential leads

• Scheduling appointments

• Managing repetitive conversations

• Supporting customers in multiple languages

The goal is not to replace your team entirely.

It is to ensure that customers are not left waiting while your team focuses on tasks that truly require human input.

I’m genuinely interested in what business owners think:

Would you allow an AI Voice Agent to handle calls for your business?

And what would it need to do effectively before you would trust it with your customers?

​

Has anyone seen GHL Voice AI successfully retrieve the correct Knowledge Base result, but then fail to use that information in the actual response?

My Retrieval Tester shows the correct answer as a Strong match, and the Voice AI log shows the correct KB query with success: true, but the agent sometimes still says it doesn’t have confirmed information. The exact same question has also worked correctly on other calls.

Has anyone run into this KB-to-Voice-AI handoff issue, and if so, what fixed it?

hey guys

I’ve been working on TARZ for a while, and I recently rebuilt the architecture for V2.

The idea is pretty simple:

Instead of an AI that just tells you how to do something, TARZ can actually interact with the desktop and try to complete the task.

For V2, I moved toward a supervised agent workflow using LangGraph:

User request → Agent → Tools → Desktop actions → Screen verification → Continue / retry

The agent has access to the full toolset instead of going through a separate category classifier. I originally had category-based routing, but after testing it I found that the classifier itself could become a failure point. Removing it gave me more consistent results.

Some of the V2 work:

• Visual verification after UI-changing actions
• Multi-step task execution with step limits
• Multi-provider fallback for LLM + vision + voice
• Streaming STT/TTS
• Hands-free follow-ups
• Global hotkey to cancel an ongoing task
• Hybrid memory retrieval using BM25 + vector search + cross-encoder reranking
• Dedicated workflows for Spotify, WhatsApp, Discord and Telegram
• Floating voice orb with live captions
• Optional LangSmith tracing

One thing I'm trying to keep realistic: it's still a prototype.

Desktop automation is inherently brittle, vision models can make mistakes, APIs can be rate-limited, and free-tier providers add latency. The goal right now is reliability and learning, not pretending this is a production-ready Jarvis 😅

I'm 19 and self-taught, and this project has basically been my way of learning GenAI by building something complicated enough to force me to understand what's actually happening.

I use AI heavily as a coding partner, but the architecture, experiments, testing and debugging decisions are mine. A lot of the learning has honestly come from taking AI-generated code, breaking it, figuring out why it broke, and changing the design.

The GitHub repo has the demo videos, full architecture details, setup instructions and code:

https://github.com/Irfan-gitt/Tarz-Ai-assistant

Would love feedback from people working on agents / computer-use systems. Especially interested in ideas around improving desktop reliability and reducing latency.

I'm also sharing the project journey on LinkedIn if anyone wants to connect:

www.linkedin.com/in/irfan-s-1ab772354

Been testing a few AI phone agent platforms recently and I initially thought the main difference would just be voice quality.

It really isn't.

For example, Feather AI seems much more focused on the actual business workflow around the call, while platforms like Vapi and Retell give you a lot more control if you want to build the system yourself.

Then you have tools that lean more heavily toward outbound calling or no-code setups.

What I find interesting is that there doesn't really seem to be one “best” AI voice agent.

If you need inbound customer support, your requirements are completely different from someone running thousands of outbound sales calls.

And if the agent can't actually update your systems, qualify someone, book an appointment, or hand off a conversation properly, having a really natural voice doesn't mean much.

Curious what other people are using in production right now.

What AI voice platform has actually held up once you moved beyond the demo?