Voice AI agents

What the voice agents service includes

We design and build a voice AI agent that calls your database itself and takes incoming calls by your scenario. The work includes writing dialogue scenarios and branching logic, selecting and tuning the speech recognition and synthesis engines, connecting to telephony via SIP and integrating with your CRM or contact base. The communication equipment, phone lines and servers stay yours or your provider's — we write the software part, tune it to your numbers and channels and bring it to production readiness. Separately, we work out the handling of failures: what the agent says on a missed call, a transfer to an operator or an unrecognized response. In the end you get a tuned voice loop with call logs and connection-rate metrics, not an abstract demo.

How the agent hears and speaks

In essence, a voice agent is a chain of four blocks working in real time. Incoming audio from the line goes into the speech recognition module (STT), which turns the caller's words into text; then the language model (LLM) decides what to say by the scenario, and speech synthesis (TTS) voices the answer back into the line. All of this runs through SIP telephony, which holds the connection itself and passes the audio stream between the caller and the system. Latency over the full cycle is critical: if the answer arrives later than a second to a second and a half, the conversation feels like talking to a robot, so the blocks are selected and tuned for minimum response time. We assemble this chain on the client's or provider's infrastructure and calibrate every junction, rather than just wiring ready-made services together at random.

Where speech technology grew from

Machine speech synthesis is counted from the Voder — a device that Homer Dudley and his team at Bell Labs showed at the 1939 World's Fair; it grew out of Dudley's work on the vocoder, which had been going since 1926. Recognition came later: in 1952 those same Bell Labs built the Audrey system, which distinguished spoken digits by their formants, and in 1962 IBM showed the Shoebox, which understood 16 words. In the 1970s a DARPA program (1971–1976) led to the HARPY system from Carnegie Mellon University, which by 1976 already recognized about a thousand words thanks to a beam-search algorithm. These early machines worked with a tiny vocabulary, but they proved that a computer is in principle capable of both hearing and speaking. A modern voice AI agent is a direct continuation of this line, only the vocabulary and quality have grown by orders of magnitude.

Why software and setup decide the outcome

The hardware for a call is equally available to everyone, and the difference in the result is made precisely by the software part: the scenario, the accuracy of recognition and the quality of the voice. If STT confuses "yes" and "no" or loses numbers, the agent steers the conversation the wrong way, and no telephony will fix that — the error is born in the software and in its tuning to your vocabulary, accents and line noise. Synthesis isn't neutral either: unnatural or choppy speech makes people hang up in the first seconds, so the voice is selected and tuned for the task rather than set by default. The scenario has to cover the real branches of a conversation, including interruptions, pauses and non-standard answers, otherwise the agent breaks on the very first live dialogue. We treat this as an engineering task: we measure the share of correct recognitions and call completion, rather than trusting a demo on a clean studio voice.

What tools we work with

The voice agent's stack is built on four components, and we select each one for the client's language, budget and load. STT is responsible for turning speech into text and is tuned for the required languages, industry terms and telephone-quality audio. TTS synthesizes the answer in a voice that is chosen for the audience and checked for naturalness and intelligibility on the line. SIP telephony links the agent to real numbers and operators, handles receiving and initiating calls and connects to your PBX or provider. The LLM holds the dialogue logic on top of the scenario — it understands the caller's intent and formulates answers within the set rules. The specific engines in each block are interchangeable, and we choose the combination by latency, cost per minute and accuracy on your material, while setting up the infrastructure on the client's side.

When the key standards appeared

The first full-fledged text-to-speech synthesis system for English was assembled by Noriko Umeda's team at Japan's Electrotechnical Laboratory in 1968. The commercial turning point came with DECtalk from Digital Equipment Corporation in 1984 — it was in its recognizable voice that Stephen Hawking spoke for many years. The neural-network era of synthesis began in 2016 with WaveNet from DeepMind, which for the first time generated the sound waveform directly and set the naturalness bar for today's voices; recognition switched to neural networks earlier, relying among other things on the LSTM of Hochreiter and Schmidhuber from 1997. The telephony foundation was set by the SIP protocol: the first version came out as RFC 2543 in 1999, and the current edition, RFC 3261, in June 2002. All of modern voice telephony with artificial intelligence rests on these anchor dates.

Why you can trust this to us

The combined experience of our development team in IT exceeds 45 years, and we assemble the voice loop as engineers rather than as sellers of ready-made boxes. We don't manufacture hardware and we say so honestly: your lines and equipment stay yours, and our area of responsibility is the scenario, recognition, synthesis, SIP integration and tuning to your real calls. Before the production launch the system goes through checks on live dialogues: we measure recognition accuracy, call completion and the agent's behavior on non-standard answers, and we fix things before it dials its first client. The logs and metrics are open, so you can see where the agent works and where the scenario needs more work. This approach removes the main risk of a voice AI agent — a blind launch that crashes conversion and reputation within the very first few hundred calls.