ElevenLabs + VoxImplant voice agent in 12 weeks
Senior fullstack engineer who built a production-grade voice AI layer for a customer-support operation, replacing ~70% of inbound first-level calls.
Who they are
A mid-size service-based business, 30-60 people, EU-based, running a customer-support operation that handled hundreds of inbound calls daily. Mix of order confirmations, status inquiries, basic troubleshooting – the kind of calls that don’t need a human but were taking up most of the team’s day. They had VoxImplant telephony and an internal admin server, but no one with deep AI-integration experience.
What was on the table
“We want to add an AI voice agent” sounds simple. In production, it’s three engineering problems stacked.
First, integrating an LLM-driven voice agent (ElevenLabs) into a working telephony stack (VoxImplant) without breaking existing call flows. Second, making the agent useful: recognizing the caller by phone number, pulling context from the internal server, and handing off cleanly to a human when needed. Third, logging everything (transcripts, audio recordings, outcomes) for compliance and quality review.
Most senior backend devs can do one of those. Few can do all three on a production timeline.
Why they chose UASoftDev
We had a fullstack engineer with prior experience in both VoxImplant telephony APIs and LLM-based agents (ElevenLabs + OpenAI). Specific stack overlap, uncommon profile.
Alexander (CTO) reviewed the architecture proposal before kickoff. First match within 60 hours.
What the engineer delivered
- Designed and built the integration layer between ElevenLabs voice agents and VoxImplant telephony, handling call routing, agent invocation, and fallback to human operators
- Built caller identification logic: incoming call number cross-referenced against the client’s internal database, with context (order status, customer history) passed into the agent’s session
- Implemented multilingual IVR flows (English + [client’s local language]) – “press 1 for orders” replaced with natural-language conversation
- Set up end-to-end logging: transcripts, audio recordings, conversation outcomes saved to the client’s admin server via API calls, queryable for QA and compliance
- Built handoff protocols: when the agent detects edge cases, complaints, or specific keywords, the call is routed to a human operator with the conversation context already loaded
What changed
After 3 months of buildout and tuning, the AI voice agent went live and now handles ~70% of inbound first-level calls — order confirmations, status inquiries, basic troubleshooting.
The engagement transitioned from build phase into ongoing maintenance and expansion – adding new agent capabilities, training on new use cases, expanding to outbound (order confirmation calls).
By the numbers
Previous vendors quoted 6-month timelines and didn’t know the stack - UASoftDev came in with ready expertise and shipped to production in 12 weeks.
Running a voice-driven workflow? Add AI without rebuilding your stack.
If you’re running a customer-support operation, telephony service, or any voice-driven workflow and want AI to handle the routine layer - this is what we run through Staff Augmentation. Compatible telephony stacks: VoxImplant, Twilio, Vonage. Compatible AI providers: ElevenLabs, OpenAI, Anthropic.