Convert Web Traffic. Resolve Support with Factual Chatbots.
Factual RAG Vector Databases
Your chatbot answers queries strictly using verified documents. Context filters prevent the LLM from fabricating product details or pricing.
Cooperative Lead Handoffs
When high-intent leads or complex queries are identified, the agent triggers instant Slack alerts and assigns them to your live reps.
Multichannel Synchronisation
Deploy across website widgets, WhatsApp, Telegram, Discord, or Zendesk simultaneously — all sharing the same unified knowledge base.
AI-powered chatbot widgets for startups seeking fast, automated FAQ answering.
Advanced RAG chatbots designed for e-commerce leads, support systems, and CRM integrations.
Fully custom AI conversational systems linking legacy databases and multiple languages.
Extended RAG Knowledge Base
Expand vector storage limits to ingest larger document archives and product catalogs.
Multi-Language Support
Deploy your chatbot across multiple languages with localized response logic.
E-Commerce Platform Sync
Direct Shopify, WooCommerce, or Magento integration for real-time order and inventory data.
Live Agent Escalation Module
Intelligent handoff to human support with full conversation context forwarded.
Our chatbots deploy on your website (custom widget), WhatsApp Business, Telegram, Discord, Zendesk, Intercom, and any platform with a webhook or API.
We configure strict retrieval guardrails — the LLM is only permitted to answer using content retrieved from your verified vector database. If no relevant document is found, the agent escalates to a human rep rather than guessing.
Yes. We build direct API connectors to pull real-time inventory, order status, and product data from your e-commerce platform. Customers can track orders, check stock, and process return requests automatically.
Standard chatbot deployments are live in 7–10 business days. Custom RAG builds with large document archives or multi-platform deployments range from 10–14 business days.
The agent detects the knowledge gap and triggers a configurable escalation path — either collecting the query for your team to review, or transferring the session to a live agent with full conversation context.