Capability · Engage

The agent that talks like your best person. On every channel.

Voice. Web chat. WhatsApp. Email. Kiosk. Same agent. Same memory. The same answer in 90+ languages, on whichever surface the customer reaches you on.

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One persona · many channels
Concierge tone · 90+ languages
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One turn, fully traced

Every reply is a plan. Below threshold, it’s a handoff.

Each reply follows four steps: understand, check facts, plan, and answer. If the facts are not strong enough, Fairshift sends the talk to a person.

Inside the threshold
Customer · Madrid · Spanish
“¿hacen check-ins los fines de semana?”
intent→
booking_enquiry · weekend
knowledge→
policy:hours.weekend (matched, 0.94)
policy:walkins (matched, 0.91)
plan→
answer_directly + offer_booking
reply→
“Sí, atendemos walk-ins sábado y domingo entre 10am y 7pm. ¿Te aparto un espacio?”
Example trace · 0.93 confidenceescalation · none
Below the threshold
Customer · São Paulo · Portuguese
“vocês têm cobertura para acidente de trabalho?”
intent→
policy_question · workplace_accident
knowledge→
policy:coverage.workplace (no match, 0.34)
policy:claims (partial, 0.41)
plan→
below_threshold · escalate_to_human
reply→
“Boa pergunta. Não tenho a resposta exata aqui. Já estou conectando você com um especialista.”
640ms · 0.34 confidencehanded off · ticket #14a2

These steps run for each reply, language, and channel. The agent answers from approved facts or sends the talk to a person.

What this means for buyers

Embodied. Grounded. Transferable.

01
Embodied

Photoreal face plus real voice plus real reasoning, every conversation. Customers know they are talking to AI; they choose to keep talking because it works.

02
Grounded

The agent only answers from your knowledge base. Below confidence, it says it does not know and routes to a human, with full context attached.

03
Transferable

What worked on web chat works on voice, WhatsApp, kiosk, email, mobile. Same persona, same memory, same outcomes, every channel.

It speaks

90+ languages. Detected from the first word.

Not a translation layer. Each conversation runs in the customer's language, switched mid-sentence if they switch.

English·العربية·Português·日本語·한국어·Bahasa Indonesia·Tiếng Việt·Polski·Ελληνικά·Svenska·हिन्दी·اردو·Kiswahili·Čeština·Dansk·English·العربية·Português·日本語·한국어·Bahasa Indonesia·Tiếng Việt·Polski·Ελληνικά·Svenska·हिन्दी·اردو·Kiswahili·Čeština·Dansk·
Español·中文·Français·Deutsch·Italiano·Türkçe·Nederlands·ไทย·עברית·Magyar·বাংলা·Filipino·Română·فارسی·Norsk·Español·中文·Français·Deutsch·Italiano·Türkçe·Nederlands·ไทย·עברית·Magyar·বাংলা·Filipino·Română·فارسی·Norsk·
Runtime, in honest specs

What the runtime actually does, measured.

Each setup tracks speed, language, memory, and data safety. These numbers come from this workspace. They do not cover every customer.

First voice response
Measured per deployment and visible in the workspace. Actual latency varies by network, model, and voice configuration.
Languages handled
90+ natively, voice and text. Code-mixing supported.
Memory persistence
Indefinite per customer, scoped to your tenant. Same memory across every channel the customer uses.
Knowledge isolation
Per-tenant index. Agents never reach across tenants. Verified at the API boundary.
Below-threshold behavior
Graceful 'I don't know' plus escalation, not a guess. Threshold tuned per tenant.
Tone control
Configured by Fairshift on your behalf. Tenants don't write prompts or pick models.
Voice rails
Owned end-to-end. Sub-second response holds at peak because the path is ours, not a vendor's.
Handoff to human
Tone, intent, knowledge confidence, and explicit triggers each route to a human with full context attached.
The other half

The interaction is the input. The output is a conversion.

See an avatar answer a real call.

In 30 minutes, we can test a real customer talk. We can use your channels and your customer's language.

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