How is this different from an IVR?

IVRs use rigid menus and frustrate users. Voice agents understand free-form speech, ask clarifying questions, and call APIs to actually complete the task. The architecture is also entirely different - IVRs are a tree; voice agents are an LLM with tools.

What about accents, hold music, and bad connections?

All real-world problems we benchmark against. We run shadow mode (agent listens, doesn't act) on a slice of your real call traffic during weeks 4–6, so we measure performance on your customers - not on staged test calls.

Can the agent escalate to a human?

Yes, and gracefully - warm transfer with context, not 'connecting you now' followed by silence. We design the human escalation path as a first-class flow, not a fallback, because that's where most voice projects fail.

Is this TCPA / GDPR / two-party consent compliant?

It is by design - disclosure scripts at call start, consent capture, recording retention rules, and DNC integration are part of the build. We'll review against your specific jurisdictions and industries in discovery.

AI Voice & Telephony

AI Voice & Telephony - Real Calls, Real Agents

Voice is back - and not as IVR. We build AI voice agents that handle real customer calls end-to-end: appointment booking, support triage, outbound qualification - under 700ms latency, with the guardrails that keep them from going off-script on a recorded line.

What happens on every call, in milliseconds

1
Audio in
Telephony layer (Twilio/LiveKit) streams audio
2
Speech → text
Streaming STT (Deepgram, Whisper)
3
LLM decide
Tool routing, RAG, guardrail check
4
Tool / API
Calendar, CRM, ticket system
5
Text → speech
Streaming TTS (ElevenLabs, Cartesia)

First-token latency under ~700ms is the threshold for 'feels like a person'.

What you get

Voice architecture across telephony (Twilio, Plivo, SignalWire), realtime layer (LiveKit, Vapi, Pipecat), STT/TTS, and the LLM brain

Sub-700ms first-token latency - the bar between 'feels like a person' and 'feels like a bot'

Tool/API integration so the agent can actually do things: book in your calendar, file tickets, update CRMs

Interruption handling, barge-in, and graceful escalation to a human when the agent isn't sure

Compliance for recorded calls: TCPA, GDPR, two-party consent - built into the call flow, not bolted on

Evaluation harness with real-call transcripts, so every release is gated on quality not vibes

Cost model per call - voice + LLM + transcription stacks fast, and we'll size it for unit economics that work

When it fits

You handle volume of repetitive calls (appointment booking, qualification, tier-1 support) and a human is overkill
You can measure success: bookings completed, tickets resolved, calls handled without escalation
Failure is recoverable - a missed booking can be re-confirmed, not a life-safety call
You're willing to start narrow (one call type) and expand once the eval harness shows it's safe

When it doesn't

The call type is emotionally sensitive (crisis, complaints, sensitive medical) - voice agents are wrong for that
Volume is too low to justify the build - under ~5k calls/month a human team is usually cheaper
Your phone system is proprietary and integration is closed - we can't bridge what we can't reach

Process

Week 1: call-flow design and tool contract definition. Weeks 2–3: latency-first prototype with one call type end-to-end. Weeks 4–6: guardrails, eval harness, and shadow mode against real calls (agent listens, doesn't act). Weeks 7–10: live with a small slice of traffic, scaling up behind a feature flag once the metric holds.

Full delivery process

Pricing

Fixed-price builds for first call type: $80–180k depending on integration surface. Quarterly pod engagement for expansion across call types. Per-call infrastructure cost (telephony + STT/TTS + LLM) typically lands $0.08–0.30/call at scale.

See engagement models

Case studies

Human Resources & Recruitment

AI-Powered Applicant Tracking System

Comprehensive ATS solution with AI-driven candidate matching, automated resume parsing, and real-time recruiter-candidate communication serving 10K+ monthly candidates.

How we'd build it

Blueprints: example projects with the plan, the architecture and the targets we'd start from.

Blueprint · Dental & physio clinic groups

Dental Booking Platform With an AI Receptionist

Online booking across clinics and chairs, reminders and recalls to cut no-shows, and an AI phone receptionist that hands off to your team.

FAQ

How is this different from an IVR?: IVRs use rigid menus and frustrate users. Voice agents understand free-form speech, ask clarifying questions, and call APIs to actually complete the task. The architecture is also entirely different - IVRs are a tree; voice agents are an LLM with tools.
What about accents, hold music, and bad connections?: All real-world problems we benchmark against. We run shadow mode (agent listens, doesn't act) on a slice of your real call traffic during weeks 4–6, so we measure performance on your customers - not on staged test calls.
Can the agent escalate to a human?: Yes, and gracefully - warm transfer with context, not 'connecting you now' followed by silence. We design the human escalation path as a first-class flow, not a fallback, because that's where most voice projects fail.
Is this TCPA / GDPR / two-party consent compliant?: It is by design - disclosure scripts at call start, consent capture, recording retention rules, and DNC integration are part of the build. We'll review against your specific jurisdictions and industries in discovery.

TekPiq engineers in an architecture session at the whiteboard, one of them sketching the data model — Architecture session in our office - client details on the board are blurred.

Who builds it

Our own team, working from our office in East Sarajevo, Bosnia and Herzegovina. The engineers on your scoping call are the ones who build it.

Meet the team

Ready to talk ai voice & telephony?

30-minute scoping call. No obligation, no hard sell.