Intelligent conversational interfaces that understand context and intent.
Advanced NLU engines that understand intent, extract entities, handle ambiguity, and maintain conversation context.
Deploy across web, mobile, WhatsApp, Slack, Teams, and voice assistants from a single conversational logic layer.
Deep integrations with Salesforce, HubSpot, Zendesk, and custom backends for personalised conversations.
Real-time translation and native support for 50+ languages with cultural context awareness.
Intelligent escalation to live agents with full conversation context, sentiment analysis, and priority routing.
Conversation analytics tracking resolution rates, satisfaction scores, common queries, and bot performance.
It is grounded in your own data and built to understand context and intent rather than match keywords. It is also built to escalate cleanly to a person when it should, and to log every exchange — so the failure mode is a handover rather than a confidently wrong answer.
It escalates rather than improvises. Clean escalation paths are part of the build, because the cost of a plausible wrong answer to a customer is higher than the cost of a handover, and trust lost that way is slow to recover.
Yes — integration with the systems you already run is the substantial part of the work, more so than the conversational layer. Authentication, permissions and reliable handover into your existing queues typically consume more effort than the model configuration.
By resolution and escalation rates rather than conversation volume, alongside a maintained evaluation set. A system that handles fewer conversations correctly is worth more than one handling many badly, and the metrics should reflect that.