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Customer-facing AI agents
Agents that face your customers and hold the brand line.
What it is
Customer-facing AI agents are conversational systems that do real front-of-house work. Intake and qualification, support triage, booking and account questions, guided onboarding. We scope what the agent owns, what it hands to a human, and the register it is allowed to speak in, then build it on your stack with your knowledge behind it. The output is a front line that answers in seconds and never drifts off brand.
Who it's for
Operators whose team answers the same questions every day. Firms losing enquiries that arrive outside business hours. Founders who want AI in front of customers but will not accept a generic widget speaking for the brand.
What's included
- Scope and escalation design (what the agent owns, what humans keep)
- Knowledge structuring and grounded retrieval setup
- Voice and guardrail specification in the brand register
- Custom interface or embed built into the existing surface
- Evaluation against real conversations before launch
- Transcript monitoring and a tuning cadence after launch
Recent proof
FAQ
- How is this different from your AI workflows capability?
- AI workflows are internal. They compress a team's recurring work behind the curtain. Customer-facing agents stand in front of the business, speak in its register, and carry escalation rules because the cost of a wrong answer is public. Different audience, different guardrails, different build.
- Will it hallucinate at a customer?
- The agent answers from grounded retrieval over your knowledge, not from the open model. Where it is uncertain it hands off to a human rather than guessing, and the evaluation suite runs against real conversations before anything goes live. Transcripts are monitored after launch.
- Which channels does it cover?
- Web chat first, because that is where most intake happens. Email and WhatsApp where they earn their place in the operating reality. Voice is scoped case by case; we will say so plainly if it is not ready for your use case.
- What happens after launch?
- The Maintain stage. Transcripts get reviewed, prompts and knowledge get tuned on a cadence, and the escalation boundary moves as trust in the agent grows. An agent that is not maintained drifts; ours are watched.