Verify + route
What used to be a person with a form
Identity and purpose first. Then the department, or an escalation.
Case Study
LLM-powered voice assistant for call-center.
Inbound calls were verified and routed by hand. Peak hours made that slow and error-prone; a global, multilingual book made it worse. Vstorm put an LLM voice assistant on the line: identity, intent, then the right queue — or an escalation.
The outcome
Verify, classify, route — without adding a night shift
The published result is hyper-automation of inbound handling: higher volume, fewer manual steps, 24/7 coverage, more than one language. We do not publish a call-count, an average-handle-time, or a containment percentage for this customer.
This is an undisclosed call center, not the US telecom device-activation story (98% / 330 minutes). STT and TTS are how the caller hears a voice. RAG and classification are how the request finds a queue.
24/7
The line does not sleep
Inquiries outside office hours still get an answer path.
STT / TTS
Speech in, speech out, LLM in the middle
Multiple languages on the same stack. LangChain holds the model steps.
About the client
The customer is not named here. It is a call-center operation handling inbound customer calls, already on a telecommunications stack, that needed verification and routing to scale with a global, multilingual book — without paying for every extra peak-hour seat.
The voice assistant is the product of this engagement. It is not a claim that the client is a voice-AI vendor.
Vstorm's impact
Vstorm's impact, the TL;DR
- Inbound verification and routing automated on the existing telecom stack
- Speech-to-text and text-to-speech for a natural voice path
- Question answering, RAG, summarization, extraction, and classification
- LangChain for model steps that have to stay accurate when the request is messy
- Multiple languages; 24/7 coverage
- Human agents free for work the model should not close
The challenge
Manual verification, peak-hour delay, a map of languages
An audit of the existing solution showed three failures that compounded: people on the line meant slow response; people on verification meant more errors; the book was global and the process was not. Peak times made all three worse.
The brief was a voice assistant on their stack — question answering, RAG, summarization, information extraction, classification, STT/TTS — with LangChain holding the models together.
How we delivered
Audit the queue. Then put a voice on it.
The assistant had to live on the telecommunications systems they already ran.
Audit the manual path
Workshops on inbound flow: verification, routing, error under load, and the language mix. The gap was not another IVR tree. It was a model that could hear intent.
- Inbound map
- Peak-hour failure modes
- Language mix
Proof of Value on real spoken requests
STT into the LLM, classification for the queue, RAG when the answer needs a document, TTS back to the caller. Escalation when the model should not close.
- STT / TTS path
- Classify and route
- RAG for hard queries
24/7 on the existing stack
Integrated with the telecommunications systems already in place. Multiple languages. Uninterrupted hours. Agents spend time on what still needs a person.
- Telecom integration
- Multilingual coverage
- Always-on path
How it works
Hear the caller. Name the request. Pick the queue.
An inbound call hits the assistant. It verifies identity and purpose, reads the spoken request in real time, classifies it, and either routes to the right department or escalates. Speech recognition and voice response stay on for the whole turn so the caller is not dropped into a keypad tree.
Inbound call received
↓
Identity and purpose Voice assistant verifies the caller and the reason
↓
Real-time query processing LLM understands the spoken request
↓
Query type identified Request classified for routing
↓
Routed to department Sent to the appropriate team
Escalated Passed on if needed
Speech-to-text and text-to-speech throughout, running 24/7 across multiple languages.
Results
More calls. Fewer hands on the first minute.
Inbound handling is automated enough that volume can rise without a matching rise in manual verification. Response is faster; routing errors drop. The assistant can tailor the turn to the caller. We do not publish a containment rate. The published facts are verify-and-route, 24/7, multilingual STT/TTS, and a team that can spend the hours on engagement instead of the first form.
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