Voice AI Agents
Outcomes
Calls answered immediately, at any hour
No queue, no voicemail, no abandoned callers. For businesses where a missed call is a lost customer, this is usually the entire business case.
Consistent call handling
The same questions asked, the same information captured, the same policy applied, with a transcript and structured record of every call.
Human time reserved for calls that need it
Routine calls handled end to end; complex or sensitive ones transferred with full context.
Honest limits
Voice is harder than chat. Latency, accents, background noise, interruption and emotional register all matter, and the failure modes are more visible because the caller is waiting in real time. We scope accordingly.
What we build
Inbound call handling for booking, enquiries, status checks and triage, integrated with your calendar, CRM or ticketing system.
Outbound calling for reminders, follow-ups, confirmations and qualification — within the consent and regulatory constraints of the jurisdictions you operate in, which we treat as a design input rather than an afterthought.
Call routing and triage that understands the reason for the call and directs it correctly, replacing menu trees people already dislike.
Post-call processing — transcription, summary, structured data extraction, CRM update, follow-up task creation. Often valuable even without an agent handling the call itself.
Warm transfer to a human with conversation context passed across, so the caller does not start over.
Multilingual handling where your callers need it, tested against real accents rather than clean studio audio.
How it works
Weeks 1–2 — Call analysis. We listen to real recordings of the call types in scope. Duration, structure, common deviations, where calls go wrong. This is where scope gets set realistically.
Weeks 2–3 — Conversation and escalation design. The flow, the recovery paths, and the explicit conditions for transferring to a person. Escalation is designed first.
Weeks 3–6 — Build. Speech recognition, dialogue logic, speech synthesis, telephony integration, system actions. Latency budget managed throughout — total response time above roughly a second reads as awkward, and this constrains architecture more than anything else.
Weeks 6–8 — Testing under real conditions. Accents, noise, poor lines, interruptions, callers who change subject mid-sentence. Voice fails in ways text never does.
Weeks 8–10 — Pilot. Limited live traffic, call review, iteration. Every call recorded and reviewed initially, because early failures are cheap to fix and expensive to leave.
Ongoing. Call review, prompt and flow tuning, expansion of scope as performance justifies it.
Technology
Speech recognition: current leading providers benchmarked against your actual audio, including accented and noisy samples. Recognition quality varies far more across providers on real telephony audio than on benchmarks.
Dialogue: language models with tight latency budgets, streaming responses, and interruption handling.
Speech synthesis: ElevenLabs, Cartesia and comparable providers, selected for naturalness and latency together.
Telephony: Twilio, LiveKit or your existing platform.
Integration: calendar, CRM, ticketing, order systems — the agent acts, not just talks.
Where this applies
Strongest for high call volume in a narrow set of predictable call types, where missed calls carry direct cost.
Weakest where calls are long, emotionally sensitive, or highly variable. Some conversations should be handled by a person, and we will say which of yours those are.
How we scope and price
Fixed scope per call type, quoted after call analysis. Cost is driven by the number of call types, integration depth, language requirements, and how tight the latency and quality bar needs to be. Per-minute run costs — recognition, inference, synthesis, telephony — are modelled explicitly, because voice carries higher ongoing cost than text and it should be visible before you commit.
Frequently asked questions
They should, and in a growing number of jurisdictions they must be told. We build disclosure in. In practice callers accept it readily when the agent is useful and transfers cleanly.
Current synthesis is genuinely good. The bigger determinant of naturalness is conversational behaviour — handling interruption, not talking over people, recovering from misunderstanding — which is engineering rather than voice quality.
The main practical risk, and the reason we benchmark recognition against your real audio during scoping rather than trusting vendor claims.
Warm transfer to a human with context. Designed first, tested hardest.
Depends entirely on jurisdiction, consent status and call purpose, and the rules differ substantially across the UK, EU, UAE and India. This constrains design and we treat it as a requirement from day one — but you should take your own legal advice on your specific programme.
It is a per-minute cost across several components, and it varies with call length and provider choice. We model it during scoping with your actual call duration data.
Yes, with quality varying by language and accent. We test against your real caller base rather than assuming parity.
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