You just built a voice AI agent, it works, then the bill shows up and you're paying for the LLM, the voice, the phone call, and then another platform fee on top of that. That's not even the worst part. The worst part, you still don't really even own the system. Today, I'll show you Dobre and an open source VAPI alternative you can self host, inspect, and control.
Voice AI nowadays can look somewhat simple from the outside. Take a phone call,
turn speech into text, send it to the LLM,
turn the answer back into speech, it's done. That's easy. Right? Well, as any of us know who've tried this, not really because real calls are messy. People interrupt, people go silent, they're gonna change topics,
they can ask really weird questions.
Your agent needs to call APIs and when it breaks, you need to know why.
That is where most voice AI projects become more of a pain. A voice agent is not just chat GPT with a phone number, it is a live system with a bunch of moving parts, right? That's speech to text, LLM, text to speech, state, tool calls, a boatload of other things, you get it. There's a lot of moving parts that we don't actually see happening. And when the call fails, the bot gave a bad answer is not enough. Was it the prompt? Was it the model? What was it? Why did it fail? And this is where Dogebra comes in. If you enjoy coding tools that speed up your workflow, be sure to subscribe. We have videos coming out all the time. Alright. Now, let's look at this in practice. I'm gonna start locally because if a tool says it's built for devs,
I wanna see Docker before anything else, this was super easy to spin up. I'm gonna clone it from GitHub, I'm gonna c d into the folder and then I just have to run Docker compose up. That's simple enough, easy enough for us. Once the containers are running, we can jump into the Dobre UI.
Now I'll build a simple lead qualification agent. So what do I mean by that? Someone's gonna call in, the agent asks what they wanna build, then it asks about the company, the size, the budget,
small things like this. It'll then call an API tool to create or update a CRM lead if we embed that. And maybe I could even say if the lead was qualified, it transfers to a human.
So I add a prompt node,
then a qualification step,
then an API tool call
and then I can add a branch and a transfer.
There's no custom orchestration code yet and that's kind of the point here. This looks like a no code canvas but for devs and the value is not no code. The value is not wasting code trying to tie everything together.
Now, let's try to run a test call here.
Hi. This is Sarah from inbound calls.
Are you still there?
We're looking for an AI phone agent for inbound demo requests.
That's great. I can definitely help you with that. To make sure I connect you with the right solution,
could you tell me a little more about what you're looking to achieve with an AI phone agent for your inbound demo requests?
Let's say around twenty thousand minutes. Thanks for sharing that. And what is your company size and industry?
Now we can see the transcript here. We can see the trace.
We can see the tool call that actually happened and we can see the state changes.
Plus here's the recording which I wanted in the first place and that is what I want as a dev, not just the bot worked, I wanna know why it worked, when it fails, I want evidence of this actually happening.
So what is Doga? Doga appears to give us three different things out of all this. We get a voice agent, a visual workflow builder and the platform layer you usually have to build yourself.
The voice engine is the part that connects the caller, the phone provider, speech to text, the LLM, and text to speech.
That is what makes the call actually happen.
The workflow builder is where you design the logic of this whole system, so instead of hard coding every prompt, branch, API call and transfer,
you can map out the flow visually. So huge win here, I like these kind of maps. Ask this question,
wait for the answer. That's kind of what we're mapping out here. I can call this API branch here, transfer there, that kind of logic should be easy to change.
Then to all this, there's the platform layer, testing, tracing, recordings, analytics,
that is the boring stuff every series voice project eventually
needs.
With all this, you can bring your own providers, your own LLM and your own TTS.
Because Doga is open source, you can inspect the code, change how it works and self host it. As of this recording, GitHub stars are low. So this is a super new find that I found but it's honestly a rather cool one. Now let's compare Doga to other things we already have out here. You have three main ways to build voice agents. First is hosted platforms, VAPI, Bland, Retail.
These are good when you wanna move fast and you don't wanna run infrastructure.
You get clean dashboards, APIs, transcript, testing tools, all that's really useful, but you start to lose control right there. If the platform changes pricings,
you deal with it. If the platform changes limits,
deal with it. Right? If you need custom deployment,
anything like that, again, you might hit a wall. Hosted tools are fast though, so I guess that's a win. You have some of these raw frameworks like I came across PipeCap,
Vocode,
LiveKit I think is one of them. These give you a lot more control,
you can build almost anything.
But now, you're building everything around this framework,
off UI workflow editor, so that's a big trade off using things like that. Now, Doga is still way too new but it's here, so I think their bet is kinda simple. What if you could use a visual voice agent builder without giving up the self hosting, choosing a provider, tracing and control?
That's what this appears to be. Write code where code matters, use the builder where your flow matters, inspect the run time when things break, and swap providers when costs change.
Self hosting gives us a lot of control which is huge.
VAPI bland retail are best for fast hosted deployment, but the trade off cost lock in and less control. If you enjoy coding tools like this, be sure to subscribe to the BetterStack channel. We'll see you in another video.