Generative AI: Telecom and VoIP

Generative AI in Telecom and VoIP

Voice AI built by people who already run FreeSWITCH, Asterisk and Kamailio in production. We connect streaming speech to text, language models and text to speech directly to your SIP and RTP legs, so voice agents answer fast, allow barge in and transfer to a human with full context. Around the call we add agent assist, call QA and NOC copilots. Senior engineers own the design, AI coding agents carry the volume, and every line is reviewed by a human.

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Key highlights

The strongest telecom use cases are AI voice agents for high volume calls, real time agent assist, automated call QA and NOC copilots.
We integrate voice AI at the telephony layer, on FreeSWITCH, Asterisk, Kamailio or WebRTC, not as a widget bolted onto a third party platform.
Latency is engineered end to end: streaming speech to text, streaming model output and streaming text to speech, with barge in.
Outbound AI calling is designed around consent and disclosure rules such as the FCC position on AI voices and TRAI rules in India.
A voice agent proof of concept usually takes about 4 weeks; production on your telephony usually takes 10 to 16 weeks.

Why telecom AI projects stall, and what is worth solving

Voice AI demos are easy. Production voice AI on real trunks, real codecs and real call volume is where most projects stop, because the AI vendor and the telephony team never meet.

Latency that makes callers hang up. Batch speech to text, a full model response and then synthesis adds up to well over a second of silence. Barge in does not work, callers talk over the bot, and they ask for a human in the first few seconds.

Voice AI that stops at the SIP trunk. Hosted voice bot platforms assume their own telephony. Your estate runs FreeSWITCH, Asterisk or Kamailio behind an SBC, with G.711 and Opus, DTMF and transfers, and nobody owns the bridge.

Agents searching wikis mid call. Contact center agents put customers on hold to find an answer, then spend minutes on after call notes. Quality teams sample only a small fraction of calls by hand.

NOC teams buried in alarms and traces. One way audio or failed registrations mean reading SIP ladders in HOMER or sngrep. Only a few senior engineers can do it quickly, and they are always on call.

Consent and disclosure rules for AI calls. In the US, AI generated voices count as artificial voices under the TCPA, so outbound AI calls need prior consent. In India, TRAI and DLT rules govern commercial communication. Projects that ignore this get blocked by legal.

Per minute costs that do not scale. Hosted speech and model pricing that works for a pilot can exceed the margin on a high volume call. Without a plan for caching, model routing or self hosting, the business case breaks at scale.

Use cases

Generative AI use cases we build for telecom and VoIP

Each use case connects to your telephony, CRM and BSS, is measured on recordings and tickets from your own operation, and keeps a human one transfer away.

AI voice agents on your telephony

Audio streams from FreeSWITCH or Asterisk through ARI external media or AudioSocket to streaming speech to text with Deepgram or Whisper, a tool using language model that can look up the CRM or book a slot, and streaming text to speech with ElevenLabs or Cartesia. Barge in is supported, and warm transfer to a human carries the transcript.

Agent assist and after call work

Real time transcription surfaces knowledge base answers from a RAG index while the customer is speaking, then the model drafts the call summary, disposition and follow up into Salesforce, HubSpot, Zoho or your own CRM for the agent to confirm.

Automated call QA

Every recorded call scored against your script, compliance statements and soft skills rubric, with evidence quotes. QA leads calibrate the rubric against their own scores before it is trusted, and card numbers are already removed by PCI pause and resume.

Conversational IVR in many languages

Replace deep DTMF trees with natural language routing in English, Arabic, Hindi and other languages your callers use. Speech models are evaluated on your own recordings per language before rollout, with DTMF fallback kept for every menu.

NOC copilot

Read only access to HOMER SIP captures, Prometheus metrics and alarm feeds. Engineers ask why calls to a carrier are failing and get a plain language reading of the SIP ladder, correlated alarms and a drafted incident summary. The copilot suggests; engineers change configuration.

CDR, billing and fraud analytics agent

Text to SQL over CDRs and rating data for questions such as which routes lost margin last week, with the query shown. Anomaly detection models flag toll fraud and IRSF patterns, and the language model explains each alert to the on call analyst.

How we deliver

From one call flow to the whole network

01
Use case discovery and data readiness
We review call reasons, recordings, transcripts, CRM access and your telephony topology: trunks, SBCs, codecs and switch versions. Output: a ranked use case list, a latency budget and the first call flow scoped.
02
Evaluation harness and prototype
A golden dataset of real calls and tickets with acceptance criteria for intent accuracy, task completion and response latency, then a prototype bridged to a test extension on your own switch.
03
Guarded pilot with human in the loop
A percentage of live calls on one queue, with instant transfer to a human, every call recorded and transcribed for review, and account changes or payments always confirmed by a person or a PCI compliant flow.
04
Production rollout with monitoring
Ramp by queue with dashboards for containment, transfers, latency per leg and cost per minute, alerts on drift, and quarterly reviews of speech and language models as new versions ship.

AI governance for telecom and VoIP

Telecom AI sits on recorded voice, personal data and regulated outbound calling. We design for disclosure, consent and data protection from the first call flow.

FCC TCPA ruling on AI voices STIR/SHAKEN (US) TRAI TCCCPR 2018 and DLT EU AI Act transparency (Article 50) GDPR and UK GDPR PCI DSS v4.0 for payment calls Call recording consent rules Self hosted speech and language models

Callers know they are talking to AI. In February 2024 the FCC ruled that AI generated voices are artificial voices under the TCPA, so outbound AI calls need the same prior consent as prerecorded calls. The EU AI Act requires people to be told they are interacting with an AI system from 2 August 2026, and India's TRAI and DLT rules apply to commercial voice and SMS. Our agents disclose themselves and outbound flows check consent before dialling.

Recordings and transcripts are personal data. Card numbers are kept out of recordings with PCI pause and resume or DTMF masking, transcripts are redacted before they reach a language model where the task allows, and retention follows your GDPR or local policy. Every agent action is logged against the call ID.

Model choice follows residency and cost. Hosted models run under enterprise terms that exclude training on your data, with regional endpoints where available. When residency rules or per minute economics demand it, we self host Whisper and open weight language models on GPUs in your data center or cloud account.

Models and tooling we use for telecom AI

The open source telecom stack we have run since 2018, paired with streaming speech and language models chosen per use case and per language.

FreeSWITCH and ESL Asterisk ARI and AudioSocket Kamailio and OpenSIPS WebRTC and LiveKit Deepgram and AssemblyAI Whisper (self hosted) ElevenLabs and Cartesia Claude, GPT-5 and Gemini Llama 4 and Qwen (self hosted) LangGraph HOMER SIP capture Langfuse

FAQ

Common questions

How much does an AI voice agent cost to build and run?

It depends on scope and call volume, so we quote from a written estimate rather than a price list. Most clients start with a fixed price proof of concept on one call flow. Running cost is mostly per minute speech and model usage, which we pass through at cost and model for your volume up front, including when self hosting pays off. Our pricing page lists current entry points.

Is call audio safe to send to a language model?

Yes, when it is designed for. We use enterprise APIs that do not train on your data, redact card and personal data from transcripts where the task allows, keep recordings in your own storage, and can self host speech and language models when audio must not leave your network.

What stops a voice agent giving wrong answers?

Grounding, limits and a human one transfer away. The agent answers from your CRM, BSS and an approved knowledge base, can only call the tools it is allowed to, and must confirm anything that changes an account. A golden set of real calls runs on every change, and uncertain intents go to a human.

How long does it take to put a voice agent on our switch?

Usually about 4 weeks for a proof of concept on a test extension and 10 to 16 weeks for production on live queues. Existing FreeSWITCH or Asterisk estates are usually quicker to integrate than starting from scratch.

Should we buy a voice AI platform or build on our own telephony?

Buy if you are happy to move calls onto the vendor's telephony and the use case is standard. Build on your own switch when you already run FreeSWITCH, Asterisk or Kamailio, need control over routing, recording and latency, or need per minute costs that hosted platforms cannot match. We also integrate hosted speech services into your own stack.

Can the voice agent handle languages other than English?

Yes, with testing per language. We evaluate speech to text and text to speech quality on your own recordings for each language and accent, including Arabic and Indian languages, before rollout, and keep a DTMF or human fallback.

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