Introduction
Building a Voice AI assistant for calls no longer requires a giant vendor contract or a closed platform. With the right open source tools and APIs, you can wire together telephony, speech, intelligence, and automation into a real Voice AI call assistant that picks up calls, talks to customers, and hands clean outcomes back to your systems. For product and engineering teams, this approach also avoids lock in and keeps you close to the underlying tech, which makes it easier to experiment and ship features faster.
This guide walks through a practical Voice AI assistant stack you can assemble from open source components. It covers Asterisk and FreePBX for telephony, LiveKit for real time audio, ASR and TTS choices, NLU and LLM layers, and how to stitch everything into one event driven workflow using an open source voice framework. Along the way, it shows where a managed platform like SuperU.ai can sit on top of the open source ecosystem when you are ready to scale from side project to production call volumes.
What your Voice AI assistant needs to do
Before picking tools, it helps to be clear about what a Voice AI assistant for phone calls should actually do. At minimum, it needs to answer or place calls, understand what the caller is saying, respond naturally in real time, and take actions in your systems, such as creating tickets, updating CRMs, or booking meetings. It also needs to handle handoffs gracefully when automation is not enough, for example transferring to a live agent or voicemail.
From an architecture point of view, that breaks down into a few core capabilities. You need telephony and media transport, streaming ASR, low latency TTS, NLU or LLM based reasoning, dialog management, and integration with the rest of your stack through HTTP APIs. Open source Voice AI tools can cover each of these layers if you are willing to do some glue work with code and configuration.
Telephony and media with Asterisk, FreePBX, and LiveKit
On the telephony side, many teams use open source PBX projects such as Asterisk or FreePBX to terminate SIP trunks and expose phone numbers. These systems can route calls to internal extensions, IVRs, or custom applications, and they give you full control over dial plans, queues, and call routing logic for your Voice AI assistant.
For real time audio transport into your Voice AI stack, you can combine this PBX layer with a media stack like LiveKit, which provides open source building blocks for low latency voice over WebRTC and WebSockets. This gives you a way to stream raw audio from the call into your ASR service and play TTS audio back to the caller with minimal delay, which is critical for a natural Voice AI assistant experience.
Orchestrating components with an open source voice framework
Managing all of these audio streams and AI services is where an open source voice framework helps. Projects like Pipecat or Bolna provide pipelines that plug together ASR, TTS, LLMs, and external tools into one real time loop. Instead of hand wiring every WebRTC and HTTP call yourself, you define a pipeline with components for recognition, generation, and side effects, then let the framework handle buffering, timing, and reconnection.
These open source voice frameworks are designed for Voice AI assistants. They integrate with multiple ASR and TTS providers, support transports like WebRTC, and expose hooks where you can inject business logic, tool calling, and dialog management. This lets you focus on what your Voice AI assistant should do for customers instead of reimplementing audio plumbing.
Adding ASR and TTS to your open source stack
For ASR, there are several open source and open model options that you can self host or run via APIs, including Whisper based models and dedicated speech to text platforms. The key is to choose an ASR that supports your languages, works well with narrowband phone audio, and can stream partial transcripts so your Voice AI assistant can respond quickly.
On the TTS side, you can pick neural TTS engines that run locally or through APIs and support multiple voices. Some open source projects bundle basic TTS, while others let you plug in providers through a simple interface. Whichever you choose, aim for a voice that sounds natural on phone calls and offers controls for rate, pitch, and pauses so your assistant does not sound rushed or robotic.
NLU, LLMs, and dialog management with Rasa
Once you have transcripts coming in and TTS going out, the next layer is understanding and decision making for your Voice AI assistant. Frameworks like Rasa provide open source conversational AI that covers NLU and dialog management in one package. You define intents, entities, and stories that describe how the assistant should respond in different situations, and Rasa handles classification, slot filling, and dialog state.
If you want more flexible language understanding, you can pair this with LLMs accessed through open APIs or self hosted models. Many teams use LLMs for generation while still relying on a rules based dialog manager or state machine for critical flows such as payments or identity verification, which keeps your Voice AI assistant creative where it is safe and strictly guided where it is not.
Wiring actions through HTTP APIs and tool calling
A Voice AI assistant that only talks is just a demo. To be useful, it needs to perform actions in your tools by calling APIs, for example creating a support ticket, updating a CRM record, checking order status, or scheduling an appointment.
Your open source Voice AI stack can expose these tools as HTTP APIs or internal services that the dialog manager or LLM calls at the right time. Many frameworks provide built in actions or tool calling abstractions so your call flows can invoke code, handle errors, and return structured data back into the conversation without you manually wiring every request.
Observability and QA for your open source Voice AI assistant
Because you own more of the stack when you build with open source, you also own observability. You will want logs and metrics on call volumes, ASR and TTS latency, NLU accuracy, and how often calls are resolved versus escalated to a human agent. Storing transcripts and events in a central logging or analytics system helps you debug misheard phrases, refine prompts, and tune thresholds such as barge in or silence detection.
Simple dashboards that break down flows by outcome can show you where callers drop off or get confused when talking to your Voice AI assistant. Over time, this feedback loop is what turns your open source Voice AI stack from a prototype into a dependable channel that customers trust.
When to graduate from open source stack to SuperU.ai
The big advantage of building a Voice AI assistant with open source tools and APIs is control. You decide how calls are routed, which ASR, TTS, and NLU components are used, where data is stored, and how everything is deployed. The tradeoff is that you have to maintain the stack, keep services patched, and own reliability when call volumes spike.
That is where platforms like SuperU.ai come in. Once you know what works for your use case and want to move from an experimental Voice AI assistant stack to a production ready system for your call center, a managed platform can provide hosting, scaling, analytics, and battle tested workflows on top of the same architectural principles you prototyped with open source. You keep the freedom to integrate with your tools through APIs while letting SuperU.ai handle the messy parts of real world telephony and Voice AI orchestration.
Also Read: Inside Voice AI Architecture: From APIs to Phone Call Automation Workflows
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