NLP-powered website and app chatbots trained on your FAQs, services, and products — capturing leads, handling enquiries, and handing off to human agents when needed. 24/7.
An AI chatbot on your website is a 24/7 conversion asset. It handles the questions your visitors have at 10pm when your team has gone home, captures lead details through conversational dialogue rather than a form, qualifies prospects before routing them to the right team member, and resolves the repetitive FAQ queries that consume your support team's time.
Over 40% of Indian website visitor sessions happen between 7pm and midnight. Without an automated chat system, every after-hours visit that has a question leaves without converting.
Contact forms with 6+ fields have abandonment rates above 70% in India. A conversational chatbot interface converts the same information through dialogue.
Sales and support teams spend significant time answering the same 20 questions about pricing, eligibility, timelines, and service scope.
Chatbots that cannot recognise when a conversation needs a human — and hand it off smoothly with full conversation context — frustrate users and lose deals.
Intent mapping, entity extraction design, conversation flow architecture, and training data preparation — the intelligence layer that makes the chatbot understand queries.
Full chatbot development, website widget implementation, mobile SDK integration, and testing across all major query types and edge cases.
Escalation trigger logic — sentiment analysis, keyword detection, explicit user request — with full conversation context passed to the live agent.
Chatbot-captured leads automatically pushed to CRM as new contacts or leads with conversation transcript attached.
Dashboard showing conversation volume, intent distribution, handoff rate, lead capture rate, and CSAT scores.
FAQ collection from client, intent and entity mapping, conversation flow design, NLP model training, and persona and tone definition.
Chatbot development, widget implementation, CRM integration, human handoff setup, and end-to-end testing.
Live deployment, first-week conversation monitoring, intent model refinement based on real queries, and handoff optimisation.
An EdTech platform was losing after-hours enquiries because the sales team worked 9am-7pm. After deploying a chatbot handling 35 common FAQs, lead capture increased 42% — with 68% of conversations resolved without human intervention and the most conversations happening between 9pm-11pm.
Results are client-specific. Past performance does not guarantee future results.
Rule-based chatbots follow a decision tree and break immediately on any query outside the trained tree. NLP-powered chatbots use machine learning to understand the intent behind a query — not just exact keyword matching but semantic meaning. LLM-based chatbots using GPT-4 or Claude can handle highly open-ended conversations with human-level language understanding, appropriate for complex technical support or nuanced advisory conversations.
Indian users communicate with chatbots in ways that differ from Western norms: Hinglish, abbreviations common in WhatsApp communication, less formal sentence structures, and a tendency to ask multiple questions in a single message. A chatbot trained only on formal English will fail significantly on Indian traffic. We train chatbot models on India-specific language patterns and always include Hinglish handling in the training data for consumer-facing deployments.
The triggers for human handoff we design into every chatbot: explicit user request to talk to a human, negative sentiment detection, high-intent signals like pricing queries beyond a threshold, and query complexity beyond trained scope. On handoff, the human agent receives the full conversation transcript and the user's contact details — enabling them to pick up the conversation without the user repeating themselves.