Service / AI
Voice agents that sound like a good colleague.
Phone and in-app voice agents that answer, qualify, book and hand over to people, with turn-taking fast enough to feel like a conversation.
[ 01 ]The short answer
What is Voice AI Agents?
A voice AI agent answers or places calls and holds a spoken conversation: speech-to-text, a language model that decides what to say and which tools to call, and text-to-speech, streamed so the caller is not left waiting. We build them around a narrow job, such as qualifying a lead, booking an appointment or triaging a request, with interruptions handled, data written to your systems and a clean handover to a person when the call needs one.
What you get
- Voice agent with call flows and tools
- Telephony integration and numbers
- CRM and calendar integration
- Handover to people with call summary
- Latency and outcome dashboard
[ 02 ]What we build
The pieces, built to last.
- 01
Inbound call agents
Answer every call, identify intent, resolve routine requests and route the rest with a summary.
- 02
Outbound calling
Follow-ups, reminders and qualification calls that respect consent and calling-hour rules.
- 03
Streaming pipelines
Streaming speech recognition, model and synthesis, with barge-in and turn detection.
- 04
Tool calls mid-call
Check availability, look up an order or book a slot while the caller is still on the line.
- 05
Multilingual voices
Agents that switch languages and accents to match the caller.
- 06
Call analytics
Transcripts, outcomes and sentiment written to your CRM, with recordings where consent allows.
[ 03 ]Where it pays off
Where it earns its keep.
- 01
Lead qualification
Every inbound lead called within minutes and booked if they fit.
- 02
Appointment booking
Clinics, services and showrooms that never miss a booking after hours.
- 03
Customer service triage
Routine questions answered, complex ones handed to the right team with notes.
- 04
Collections and reminders
Polite, compliant reminder calls with payment links sent by text.
[ 04 ]How we deliver
Four steps, one team, start to finish.
The same people scope it, build it and stay after launch. Timings are typical for a first release; we agree yours after the deep dive.
- 01Week 1
Find the decision
We pick the decision or task worth automating, measure how it is done today and agree what better means in numbers.
- 02Week 1 to 2
Build the eval first
A labelled test set from your own data comes before any prompt or training run, so every change is scored, not argued.
- 03Weeks 2 to 10
Build with guardrails
Models suggest, code decides. Permissions, injection checks, human override and audit logs are part of the first version.
- 04Ongoing
Ship and improve
We launch to real users, watch cost, latency and quality per request, and feed failures back into the eval set.
[ 05 ]Stack
Tools we reach for.
Chosen per project, and we work in yours. Nothing here is a lock-in: you own the code and the accounts.
- Twilio
- LiveKit
- Pipecat
- Deepgram
- ElevenLabs
- Cartesia
- Sarvam AI
- OpenAI Realtime
- Claude
- Google Calendar API
[ 06 ]Further reading
Problems we found, and how we would solve them.
- AI agents3 min read
Why AI agent pilots stall before production
ProblemAgent pilots impress in demos and stall before production because nobody can show they work, limit what they can do, see what they did, or overrule them.ProposalDraw a hard decision boundary: the model scores and drafts, deterministic code decides and acts, every step is logged with its reasoning, and a person can overturn any decision in one click.Read the proposal - Cost3 min read
Your AI bill is a routing problem
ProblemAI applications send every request to a frontier model with a long prompt, so cost and latency grow with usage while most requests never needed that capability.ProposalRoute each request to the cheapest model that passes your evaluation for that kind of request, cache shared prompt prefixes, batch what can wait, and distil a small model for the highest-volume task.Read the proposal
[ 07 ]FAQ
Questions buyers ask.
Natural enough that latency, not the voice, is what callers notice. We tune the pipeline so responses start in well under a second and callers can interrupt.
Rules differ by country and by inbound or outbound use. In the US, AI-generated voices on outbound calls fall under the TCPA's consent rules. We design consent, disclosure and opt-out into every flow and review them with your counsel.
Yes. It transfers the call or books a callback, and passes a summary so the caller never repeats themselves.
[ 08 ]More AI services
- Custom AI Model DevelopmentA model trained on your data, owned by you.
- LLM Fine-Tuning and DistillationFrontier quality at a fraction of the cost.
- AI Agent DevelopmentAgents that finish the job, inside the rules.
- AI Workflow AutomationHours of back-office work, done in minutes.
- RAG DevelopmentAnswers from your documents, with citations.
- AI Memory and Knowledge GraphsAI that remembers what your organisation learned.
- MCP Server DevelopmentYour product, usable by every AI assistant.
- Document AI and Intelligent Document ProcessingPaperwork turned into clean, checked data.
- Private LLM DeploymentAI on your infrastructure, data never leaves.
- LLMOps, Evaluation and GuardrailsKnow your AI works before customers find out.
- Predictive ML and ForecastingDecisions made on what will happen next.
One company at a time