Most eval/AI optimisation platforms/libraries I tried do not seem to be made for voicebot use cases. They either don’t cover nuances of multi-turn conversations or don’t cover nuances of voice stack (transcription errors, interruptions). Any recommendation on platforms for better observability, evaluation and optimisation for voice use cases?
I'm trying to build an agentic ai system which handles booking services and suggestions for a car dealership and service centers.
techstack:
I have already made backend but facing some latency issues
I also have to implement this like a calling system
Current call flow:
User speech → STT → text → LLM → response text → TTS → audio output
Latency :
Architecture:
Right now, the system is not streaming end-to-end — it’s more of a sequential pipeline.
[This is just a college project so free tools are much appreciated :)]
I also dont have much experience with these kinds of projects so I'm just vibe coding this right now :|
My current thesis in voice ai:
- receptionist roles have already hit PMF
- biggest challenge with widespread adoption is lack of assurance, especially in the US or regulatory space in general.
bit of context about me to judge my opinion:
>!(Disclosure: my team runs a small voice-agent practice - will not promote - no links, it shapes what I see in the market.)!<
>!- startup founder around voice ai - prior startup acquired by a big firm, deal sized at ~10% of their annual revenue!<
Voice vendors (infra, TTS):
- I see an aggressive push from TTS labs to sell voice agents direct to customers, cutting out middlemen (who are asking as high as 80% commissions - at this point who is giving commission to whom).
- Maybe they are right in saying they have the real IP and the rest are middlemen. But open-source TTS is catching up.
- if some is already running a call center, they sit on tons of voice recordings that can help get better performance than established brands out of the box.
voice ai buyers:
I recently benchmarked the same voice agent with the same (STT, LLM, TTS) combo and same prompts across vendors (publicly available endpoints only).
A few observations:
- Metrics like TTFB (time-to-first-byte) vary vastly. Best-vs-worst gap is >2x. Some don't even bother to enable streaming. One vendor I suspect used a US-based region for Europe (maybe for cost reasons). This alone adds ~250ms latency.
- tool calling is available with only a limited set of vendors (appointment booking, forwarding to humans)
- guardrails are best-effort. Simple jailbreak-style test cases like "describe when my grandma had X" break them.
- very few vendors understand the tone of the human voice. Most just transcribe.
- these are still unsure about how to choose among dozens of agencies
agency selling voice agents:
- I see three categories of people for evals:
- self testers: please stop. Anchoring bias + statistically meaningless.
- self-made evals: better than nothing. Try to get adversarially tested as well.
- external vendor users: these score QA. No regulatory support yet.
- either way, you should not be worrying about compliance and evals. Handling customers is hard enough.
Feel free to point where I'm wrong. Happy to learn. This stays public.
Why do you think we aren't seeing voice AI agents everywhere?
Interested to hear what everyone's conversion rates & marketing strategies are for their AI Receptionists and thought I'd share some insight into how we've achieved customers.
It's super interesting to see that there seems to be two sides:
- Half who focus on cold calling & outbound contact to try and initiate demos
- Other half who focus on inbound & gauging intent.
Marketing & SEO
For us we have a heavy push on long tail passive inbound acquisition. Since launch we've been steadily growing our SEO presence. We've put a lot of focus on SEO and we're currently at 500 impressions/day for our targeted keywords.
We have a 1.1% CTR so looking at around 165 visitors / mo from Google searches.
Our top keywords seem to be exactly what we are targeting for, covering "virtual receptionist", variations and some industry specific keywords starting to rank well.
Looking at some of our lower positions for industry specific terms, I can see some great opportunity for us to push rankings higher and get more high-intent traffic. Can see below that we're ranking well for "answering service for carpet cleaning", "ai receptionist for landscaping services", "cleaning company answering service" among others.
We will look to push into page 1 for these over the next month or so.
Paid Ads
We have been running various campaigns for trying to get high intent traffic. Initially I ran broad ads across a variety of interests and tried to direct to our site but realise the cost vs the CTRs & conversions was extremely bad. We refined our ads to be targeted specifically by industry, with custom approaches and landings for each.
You can see for example that the reddit ads we run achieve insanely good CTRs. 1.14% CTR on landscaping and a CPC of £0.68 is a level I never thought we'd achieve.
On Google we've been seeing really high CTRs (11%!!!) but the intent/conversions & engagement we measure just hasn't matched what I had hoped. I'm currently re-working the campaigns to try and get maximum conversions.
Solid High Impact Landing
Focused as much as I could on getting maximum conversions & trying to *force* the user to see how good our agents are. Went hard on ensuring that as soon as they see the homepage, they'd be presented with that option.
Way too many sites have "Book a demo". No one wants to book a demo. They want to see it now. You're not going to get HVAC folks visiting a homepage and then scheduling a call to listen to an agent that they probably already think will sound bad.
You need to be so confident in your agent that you want to shove it in front of people.
Analytics & Tracking
This is actually something I've had to start learning from scratch. GA4 is an absolute beast once you are able to learn how to visualize paths, see where visitors go and what they do.
One of the first things I did that really boosted how many people signed up & paid was the open homepage demo that anyone could click at any time and see how the receptionist sounded.
This instantly dispels the expectations of "It'll sound robotic!", "It'll make loads of mistakes!", "Its frustrating to talk to!".
Click button, speak to receptionist, be amazed.
I've recently created a custom event for demo starts so I can see which sources give us the most demo starts. It's still collecting data but it'll be extremely handy at some point.
Agency Outreach & Offers
At some point I had realized that:
A) Our technology is insanely good. Our AI Receptionist is sub-500 latency, sounds human, can integrate with a ton of tools straight out of the box. We use a custom stack. No Vapi, n8n or any of that garbage.
B) Outreach directly to trades can be hard to scale. Agencies do this well but agencies can't always build great tech and also don't want to spend time having to deal with tech problems and maintenance.
Combining these, I started offering (and still do) $1/mo voucher codes for subscriptions for 3 months to new agencies instead of the usual $29/mo.
They could build their agents in our platform, demo them to customers, and at worst, lose $3 over 3 months.
This creates both a fantastic sales force but also people who can help give feedback & help push the platform forward with improvements.
We've had a few agencies so far who have signed up and re-sold the agents for $200, $300, $500/mo to clients.
This also meant the agencies could ignore building, knowing they'll get great tech and instead focus on selling & generating revenues and cold outreach.
Development SDK & API
Given our tech is great, I also decided why not wrap it up and launch a development SDK. We've just launched it so unsure of uptake right now but allows anyone to code or "vibe code" an agent with our tech within seconds.
Hoping this will end up getting some reach with builders but still early days.
Actual Sales
So the important thing is really - How many sales have we actually achieved in the last month taking this into account?
| Total Visitors (Last 30 days) | 1,956 |
|---|---|
| Sign-Ups (Last 30 days) | 48 |
| Paid Conversions (Last 30 days) | 12 |
A lot of the sign-ups that don't convert I think are people just curious to see the platform and how it works. We started offering a totally free plan with free call time so that folks could also just login, see how to setup an agent and give it a test whirl.
They just don't have the ability to pick a phone number to assign if they don't shift onto a paid plan.
Learnings & Future
Overall, really happy with how things are going. Plan going forward is:
After spending the last few months vetting teams for an autonomous agent project, I’ve realized that 90% of the market is just "GPT-wrappers" with better marketing. They can build a demo in a week, but they have no idea how to handle latency, cost optimization, or SOC2 compliance in a real production environment.
If you’re looking to build something more complex than a basic chatbot, here’s a tip: stop looking for "AI specialists" and start looking for software engineering firms that happen to be experts in AI.
The big differences we noticed:
We eventually found Svіtlа Systеms, and they treated our AI agent like a core piece of infrastructure rather than a side-project. Because they are a high-level engineering firm first, they actually knew how to handle the "unsexy" stuff like security audits and scaling. It’s the only reason we actually made it to a live release this year.
Would love to hear from others - has anyone actually found a team that understands the data cleaning and infra side of AI, or is everyone still just playing with LLM APIs?
I’ve been going pretty deep into voice AI infra lately (agents, call flows, real-time pipelines, etc.), and i finally spent some time testing Telnyx as the underlying carrier + infra layer instead of just using the usual orchestration stacks.
Wanted to share a quick, non-marketing take since i don’t see many grounded reviews here.
Pros
Most platforms like wrappers around someone else’s telecom stack. Telnyx is different, they run their own private global IP network, which means fewer hops + more control over routing and latency.
They colocate compute (GPUs) with their telephony PoPs, so audio doesn’t bounce around between providers. If you care about barge-in, natural turn-taking, sub-300ms responses... this actually shows up in practice.
Cons
If you’re coming from Vapi / Retell / etc., this feels lower-level. You’re building more of the stack yourself.
Telnyx = telecom + transport layer
Your LLM / orchestration layer still matters separately. A lot of people mix these together when choosing tools.
curious what others here are doing tbh
7 months running a Voice AI agency taught me one thing fast.
You're not running one business. You're running two.
Job 1: Client work. Fulfillment, onboarding, retention, results. That's what you signed up for.
Job 2: Your own agency's ops. Lead generation. Marketing. Ads. Competitor tracking. Making sure you actually have clients to serve.
The problem: when you focus on one, the other slips.
Go heads-down on client work for 3 weeks - your pipeline dries up. Shift focus to lead gen and marketing - a client's results drop because you weren't watching. Hire a VA to plug the gap - there goes 20-30% of your margin.
I started breaking it down by where human judgment actually matters.
Lead gen has no creativity in it. It's structured work - scraping, filtering, qualifying. The same criteria, repeated every day. That's not where you add value.
Competitor tracking is the same. You need someone watching, not thinking. Did anything change? Did they launch something? Are they spending more on ads?
Ads monitoring. Same pattern. Is CPL where it should be? Did a creative die? You don't need to analyze it - you just need to catch it instantly before it becomes a lost week.
Marketing is the only place where real judgment is needed. And even there - most of the time you're not lacking ideas, you're lacking the signal. You'd know what to do if someone just told you where the gap was.
I started thinking: what if each of these had a dedicated operator running in the background every day? Not replacing you. Not making decisions for you. Just watching its domain, catching problems the moment they happen, and surfacing the right info to the right person.
Does this split feel familiar? Curious how others are handling it.
Most small and mid-sized companies don’t lose deals because they lack demand. They lose because they’re slow.
And the uncomfortable truth is… speed is no longer optional. It’s becoming the core competitive advantage.
Large companies solved this years ago. They have dedicated teams, structured pipelines, and systems that respond almost instantly. Meanwhile, smaller teams are still dependent on manual replies someone has to notice the lead, open it, think, and respond.
That gap, even if it’s just 5–15 minutes, is where most revenue quietly slips away.
Because today’s buyer doesn’t wait.
They submit a form, send a message, or make a call and if they don’t hear back quickly, they move on to the next option. Usually within minutes.
Now this is where AI lead response systems change the dynamic.
Instead of relying on human availability, the system ensures every lead is acknowledged instantly. No delays, no missed notifications, no dependency on business hours.
And that first response matters more than most businesses realize.
The company that responds first often controls the conversation. And the one controlling the conversation early usually has a higher chance of closing.
For smaller companies, this is a massive shift.
They no longer need to compete on budget alone. They can compete on responsiveness.
And responsiveness builds trust faster than branding ever can.
Another layer to this is consistency.
In most businesses, response quality and speed fluctuate. Busy days, weekends, team availability it’s never uniform. Some leads get great attention, others fall through the cracks.
AI removes that inconsistency.
Every lead gets handled the same way. Instantly. Predictably. Without variation.
Over time, that consistency compounds into something powerful: a reliable system that doesn’t depend on human mood or workload.
Then comes qualification.
Teams spend a surprising amount of time talking to leads that were never serious to begin with. No budget, no urgency, no real intent.
AI can handle the first layer of filtering.
Basic questions, simple intent checks, availability nothing complex, but enough to separate signal from noise. So when a human steps in, they’re not starting cold. They’re stepping into a warmer, more qualified conversation.
That shift alone improves efficiency more than most expect.
And then there’s follow-up the part almost everyone knows is important but rarely executes well.
Not because teams don’t care, but because they’re busy.
AI doesn’t forget.
It follows up consistently. It keeps conversations alive. It nudges leads at the right time without feeling intrusive. And in doing so, it recovers opportunities that would’ve otherwise been lost.
What all of this really does is redefine where humans add value.
Instead of handling repetitive first-touch interactions, they focus on closing, relationship-building, and high-impact conversations.
That’s where real growth happens.
Looking ahead, the companies that win won’t necessarily be the biggest spenders.
They’ll be the fastest responders.
The most consistent.
The ones that remove friction from the buyer journey.
And for the first time, smaller and mid-sized companies have access to tools that let them operate at that level.
That changes the game.
Curious how others here are approaching this.
Are you still handling lead responses manually, or have you started experimenting with automation or AI in your process?
I’ve been digging into something interesting lately fully automated AI lead-response systems and I’m trying to understand how useful they actually are in real-world businesses.
From what I’ve seen, the idea is pretty simple but powerful:
The moment a lead comes in (form fill, Facebook ad, website inquiry, missed call, etc.), the system instantly responds across multiple channels usually text, email, and even AI voice calls.
Instead of waiting 10–30 minutes (or worse, hours), the lead gets contacted within seconds.
But it doesn’t just stop at replying…
Some of these systems can:
So in theory, it’s replacing the “first 5–10 minutes” of human sales work — which is usually where most leads are lost anyway.
What I find interesting is which businesses this actually works best for.
From what I’ve observed, it seems strongest in:
Basically, anywhere:
- Leads come in unpredictably
- The owner/team can’t respond instantly
- And speed directly impacts conversion
But I’m also wondering where it doesn’t work well.
Like:
Another angle is lead quality vs automation.
If AI qualifies leads upfront:
Also curious about the customer side:
Do people actually notice they’re talking to AI in the first interaction?
And if they do… does it matter?
I feel like this space is moving fast, but most content around it is either too hype-driven or too technical.
Would love to hear from people who’ve:
What worked? What broke? What surprised you?
Everywhere I go, people are building artificial intelligence agents for sales, coding, outreach, voice assistants, etc., basically automating things that have a decent workflow.At the same time, one of the most annoying and real problems in my daily life is still completely manual: managing subscriptions.Between streaming media platforms (Netflix, Disney+,Prime Video), artificial intelligence tools and random SaaS, it is no longer just a question of money but continuous decision fatigue.
Do I still need this?
Should I cancel this month?
Do I have to pay for overlapping content again?
Somehow … there is no really good "agent" to do it? No system just understands usage patterns, predicts what you need, and automatically adjusts things.Instead, I still do this, taking notes in my mind and occasionally checking my bank statement in a panic, just like it is 2012.
Am I missing something, or does it not exist yet?
Konferenzen im Gesundheitswesen sind chaotisch.
Deshalb haben wir für die #DMEA etwas anderes entwickelt:
https://kolsetu.com/events/dmea26
→ Eine Seite, auf der Sie mit uns interagieren können
→ Fragen Sie uns alles zur Konferenz
→ Navigieren Sie per Sprachsteuerung
Kein Klicken. Kein langes Suchen.
Außerdem haben wir ein Video erstellt, das mit Vogelgezwitscher beginnt, denn… genau so sollte sich Gesundheitstechnologie anfühlen.
Hi Everyone, I have started learning to build voiceagents. But i'm not sure which stack to choose. Currently using deepgram for stt, openai as brain, elevenlabs for tts. But there are lag issues and latency. Would really love to hear from people who are experienced in building ai voice agents about there stack and tools they use for optimal output and low latency
Been digging into voice AI lately (especially real-time agents), and most content I find is still very “intro/demo” level.
So I started collecting some actual events/webinars/hackathons that seem more useful if you're building seriously.
Here’s a quick snapshot 👇
Voice Agent Events (Apr–May 2026)
What stood out to me:
Which is honestly where things break in real life.
Also noticing a pattern across all of these:
Curious:
👉 Are there any good deep-dive voice agent sessions I’m missing?
👉 Especially around enterprise deployments / outbound calling
Most stuff still feels too surface-level.
The best voice agent builder in 2026 depends on whether you want a demo-level bot or a production-ready system. From real usage + research, the top options include SimplAI, Vapi, Voiceflow, and Bland AI — but they’re built very differently.
What actually matters
Most people compare voice quality. That’s a mistake.
From both production use cases and community feedback, the real factors are:
Reddit builders highlight this gap clearly:
>
Platform Comparison (Based on Real Capabilities)
Key difference:
Not just voice — it’s an agent system that executes tasks, not just talks.
2. Vapi / Bland AI (Voice-first infra tools)
Limitation:
3. Voiceflow (Design-first platform)
Limitation:
4. DIY stacks (LLM + Twilio + custom logic)
Reality:
Real-World Insight (What People Miss)
From actual deployments + discussions:
>In simple terms:
Most tools help you build voice interfaces
SimplAI helps you run voice-driven business processes
TL;DR
Quick breakdown:
👉 If your goal is production use → orchestration matters more than voice quality
