Dograh - The open source VAPI alternative

@natalia_iankovych - Yes. Thats correct. You can build voice agents on top of Dograh and make calls worldwide by connecting it with various telephony providers, like Twilio. You can definitely make reservations by phone.

Hi @natalia_iankovych - definitley. You can build voice agents using Dograh for global use cases and in 70+ languages

@natalia_iankovych Yes, you can build a voice agent for phone reservations and connect it to telephony for calls worldwide.

@natalia_iankovych That's correct.

You can connect your own telephony provider. Also, worth checking that provider's coverage for the countries you're calling into.

And you can use the MCP tools to build a reservation voice agent on Dograh. Here's how.

For reservations specifically, the part worth setting up early is getting the outcome back out like booked, not booked, call back later. That goes to your backend over a webhook, so you're not reading transcripts to find out what happened. More detail here

Would love to hear how the agent building and testing goes.

@drag0s - Thanks a lot for your kind words ❤️ Wishing you loads of luck with @Zaplingo

@drag0s Thanks for the support! Glad the local model support and hands-on help have been useful.

@drag0s the founder shoutout is so real, can confirm from the inside :) and it's not just the founders either - our Slack community's got contributors and users constantly trading configuration insights, figuring out the right tool setup for specific use cases, and helping each other debug.

Thanks @drag0s . lovely to see you root for us :)

@isroiljon - Thank you so much for your kind words.

We have given a lot of thought to creating the product and making it easy for developers and business owners alike to create and manage their voice agents. We are always hungry for more product feedback on how we can improve it and make it easier.

Wishing you all the best with @Premation

@isroiljon Appreciate you checking it out! Would love to hear what you build with Dograh.

thanks @isroiljon . do try our MCP's - we try to give a blowout experience to devs

@isroiljon Thanks a lot. The product development has been very deliberate and thus difficult at times. We owe the good mix of highly relevant capabilities it to the early adopters a lot! Looking forward to @Premation doing wonders.

@kate_ramakaieva - Thank you for your message.

Yes, observability and automatic evals creation is something that's on top of our head. We do integrate our basic observability using OTEL exporters on Langfuse, where you can already create data sets for your own use cases.

We are also trying to add these features natively on Dograh and MCP so that observability around latency, token uses, and model performance becomes a first-class citizen of the platform on both Cloud and your self-hosted environments.

Wishing you all the best with @FuseBase

@kate_ramakaieva Thanks for the feedback. We’re working toward native observability for latency, token usage, model performance, and call quality across both cloud and self-hosted deployments.

@kate_ramakaieva thanks for pushing us in this direction. its definitely a part of our pipeline - and a high priority release

@kate_ramakaieva thank you! Yes, this is very much on the way. Curious which of these is the bigger pain point for you, and worth doing first. Always good to hear it from someone actually running agents.
Good luck with @FuseBase

@dzaitzow - Thank you so much for the kind words.

We strongly believe that once Dograh is into consideration of every company in the world for their voice AI use case, revenue will be an organic side effect of that. The primary focus right now is to be able to create as much value as possible for our early believers and customers to cement our place in this industry and market.

Enterprise tickets are of course something thats super welcome and helps us bring the revenue numbers up, and some of very large enterprises (Bn $$+) have told us that they are self hosting Dograh for their use case.

great question   @dzaitzow  :D
We are believers in OSS and isn't that how the dev ecosystem should always be - open and accessible :)
For monetisation - we are walking the shoes of great oss project in the past. A bunch os companies use our cloud simply because they have the optionality to switch to Open source when required

@dzaitzow We monetize through managed cloud hosting, usage-based infrastructure, and enterprise support. Self-hosting remains fully open source, while larger teams can pay for convenience, scale, and hands-on help.

@dzaitzow thank you for your comment. The open source part is the bit we most want to get right, so good to hear it landing. Thanks for following along.

@kasaei - Thank you so much for your kind words. We are always hungry for feedback. All the very best with @Api Hunt

@kasaei That means a lot from a background like your's. Thank you. Wishing @Api Huntthe best!

@kasaei Thanks for the support and kind words. It means a lot!

Thanks @kasaei - glad to hear this from you :)

@devansh_pawan1 - Thank you so much for your support. You guys have done excellent work with @Smallest.ai and we are very happy and proud partners.

@devansh_pawan1 Thanks for the support! Great to be partnering with @Smallest.ai.

@devansh_pawan1 Thank you for your support. Wishing @Smallest.ai the best!

thanks @devansh_pawan1 . Anything specific you loved - we would be happy to double down on that further

@roopreddy - Thank you so much for your message.

Yes, for sure. We not only provided an inbuilt QA node, where you can customise the QA prompt for different industries, call types, and use cases, but we also integrate and play well with other vendors in the space, like Tuner and Noveum.

You can also expose those QA results in your post-call data sync (webhook nodes) so that your systems immediately get updated with how did the call go and how it can be improved.

Hook that with an MCP, and you have got a self-improving agent. All the best with @Documentation.AI

@roopreddy Yes, teams can customize the QA prompts and scoring criteria for different industries, call types, and use cases. Results can also be synced to external systems through post-call webhooks.

@roopreddy I thought its worth adding that you can define fully custom metrics and thresholds per industry/call type - not just one score, but your own rubric (compliance, script completion, sentiment, etc.) weighted however you want. Combined with the webhook sync above, teams tune scoring to exactly what "a good call" means for their vertical.

Yes @roopreddy  absolutely - metrics and scoring rules can be customized for different call types. Fullly customisable. We are also brining in persona simulations for AI to AI testing of voice agents

@rahul_aluri - Thank you for your message.

We are seeing good traction in Legal Intakes (inbound and outbound), Car Rentals (inbound), Restaurant Booking (inbound) and Medical Insurance (outbound) sectors.

The limitations are mostly around declaring about robo call for automated calls. Supporting regional languages are more of a capability concern and using the right set of models behind the orchestrator.

All the very best with @Spur.fit

@rahul_aluri Thanks a lot for your comment. Love what you're doing at @Spur.fit.

Adding on the languages/geography part, since you bring your own models, you can pick the best STT/TTS/LLM per language (70+ supported) rather than being capped by a single vendor's coverage. Geographic reach depends on your telephony provider, and for data-residency-sensitive regions you can deploy in-region or in your own VPC via self-hosting.

Congrats on everything you've been shipping for the fitness coaching space!

@rahul_aluri We’re seeing traction in legal, restaurant booking, and medical insurance use cases. Regional language support depends on the STT, TTS, and LLM models you choose, while geographic coverage depends on your telephony provider.

Thanks @rahul_aluri  . Early traction is spread across sales and support use cases; there are no major geography or regional-language limitations.

@bobaikato - Thank you for your comment.

Inbound volume needs to scale more dynamically and is not in operator's control while outbound volume can be controlled by the operator.

Example: If there is a big event in town, a car dealership might get many more calls than they usually do on a normal days.

So, when it comes to scaling, the inbound needs to be more elastic and responsive, while the scaling requirements for outbound can be preplanned and provisioned.

Thats an excellent question @bobaikato 
Its mostly around agent building.
In general more receptive about AI agents in inbound calls( e.g. Customer support etc) rather than outbound calls (e.g. Insurance reminder etc)
In outbound we have seen that obsession around getting the first 15 seconds right works best. While in inbound scoping and being abl to handle variety of queries and handoff ecomes critical.

@bobaikato Inbound requires elastic capacity because call volume can spike unexpectedly. Outbound is easier to forecast and provision since the operator controls the call volume.

@bobaikato I wanted to add that there's no training step when building a voice agent at Dograh. An agent is a prompt plus a workflow along with configurations, so customising it means editing nodes: seconds, and reversible. For your own specifics you'd use the knowledge base rather than training data.