A customer-facing assistant is a different problem from an internal tool: it needs your brand's voice, your product's real answers, and a clean handoff the moment it's out of its depth. We build that, not a generic chatbot.
Persona, grounding, and escalation — the full layer that makes a copilot trustworthy with real customers.
A conversational voice trained on your actual brand guidelines and past support conversations.
Answers sourced from your live product docs and policies, with the same refuse-when-unsure discipline as our RAG systems.
Full conversation context passed to a human agent — customers never repeat themselves.
One engine across web chat, in-app, voice, and WhatsApp, not a rebuild per channel.
Ongoing tracking of containment, satisfaction, and groundedness after launch.
The conversation topology and the grounding pattern — not a slide about “AI customer service.”
// elhaa Conversation Router const reply = await elhaaConverse.respond({ message: customerMessage, persona: 'brand-v2', groundingSources: ['product-docs', 'policy-kb'], escalateBelow: 0.85, passFullContext: true });
Support chat volume tripled after a product launch, and the existing rule-based chatbot frustrated customers into abandoning conversations rather than resolving anything.
We replaced it with a grounded copilot trained on the actual product docs and past resolved tickets, with a strict escalation threshold and full-context handoff when confidence dropped.
*Illustrative example based on a representative engagement.
Analyse real support transcripts to find the questions worth automating first.
Define tone, guardrails, and the handoff rules for when a human should take over.
Connect the copilot to your live product and policy data, with citations where it matters.
Staged rollout with conversation-quality monitoring and continuous prompt/flow tuning.
Share of conversations resolved without human escalation.
Share of answers actually supported by your product and policy sources.
Customer satisfaction specifically on AI-handled interactions, tracked separately.
Fully loaded cost per resolved conversation vs a human-handled baseline.
It's designed to absorb repetitive, well-defined questions so your team spends time on the conversations that actually need a human — not to replace judgement calls.
The copilot is grounded in your actual product and policy documents, using the same citation-and-refusal discipline as our RAG systems — if it can't find a supported answer, it hands off instead of guessing.
Yes — the underlying engine is channel-agnostic; each channel gets its own interface but shares the same grounded knowledge and escalation logic.
The full conversation context, not just a summary, is passed to the human agent — customers don't have to repeat themselves.
A first version on your highest-volume use case typically launches in 6–10 weeks, including a staged rollout with monitoring before full traffic.
A 30-minute call. We'll tell you honestly whether this is the right solution — and what it would take.
A short form, then a 30-minute call. We reply within one working day.
We'll be in touch within one working day.