We construct custom chatbot systems that use Retrieval-Augmented Generation (RAG) and vector databases (such as Supabase pgvector). Your chatbot is securely trained on your specific product catalogs, company knowledge, and operational guidelines. We utilize Postgres Row-Level Security (RLS) and encrypted API pipelines to guarantee that all proprietary data and customer conversations remain fully private, secure, and are never used for public LLM training.

Our chatbot systems are fully omnichannel. We design and deploy integrations for Facebook Messenger, Instagram Direct, WhatsApp Business API, and custom web widgets. On the backend, we run n8n workflow automations to sync conversations and data directly with your CRM (HubSpot, Salesforce, or custom Supabase tables), updating lead statuses and logging customer records in real time.

Yes. By connecting directly to your database or e-commerce platform (Shopify, WooCommerce, or custom databases) through secure REST APIs, our AI chatbots can fetch product inventory, check real-time order tracking status, and trigger automated abandoned cart recovery sequences on WhatsApp and Messenger to re-engage customers and drive conversions.

We implement a seamless hybrid support workflow. When the chatbot encounters a complex request, detects negative user sentiment, or if the user explicitly asks for a human, it triggers an instant handoff alert. The system routes the conversation directly to your team on Slack, WhatsApp, or live-chat dashboards (like ManyChat or Crisp) and pauses autonomous AI replies for that user.

You retain 100% intellectual property (IP) ownership. Upon completion and deployment of your chatbot system, all custom Next.js frontend code, n8n workflow templates, database schemas, and trained prompts are fully transferred to you. There are no vendor lock-ins or recurring proprietary fees; you have full control over your software stack.

Onboarding and system mapping are completed within 3 to 5 business days, where we set up shared Slack channels and audit your datasets. The complete design, database engineering, training, and deployment of a fully customized RAG chatbot system takes between 2 to 4 weeks, ending with thorough accuracy verification and user training.

The chatbot is built with native multilingual support. Leveraging multi-language vector embeddings and advanced LLMs, it automatically detects the input language (Bangla, English, and over 100 other languages) and generates a natural, contextual reply in that same language, facilitating effortless customer support for diverse local and global customer bases.

We implement a multi-layered accuracy check system. By restricting the chatbot's operational context strictly to your verified database using prompt guardrails, the AI is prevented from speculating. Furthermore, if a query yields a low confidence rating from the vector search engine, the chatbot routes the inquiry to a fallback prompt or triggers an automated handoff to a human support agent instead of guessing.

Yes, we can extend your chatbot pipelines to voice channels. By connecting voice-agent APIs (like Vapi, Retell AI, or Twilio) with our n8n backend, we can engineer custom voice agents capable of answering phone calls, scheduling customer appointments, or answering FAQs over the phone with low latency and human-like voice characteristics.

Rather than charging per-agent seat, we deploy systems using your own API credentials (OpenAI, WhatsApp Business, Supabase). You only pay the direct raw usage costs, which are fractions of a cent per message. We build in query rate-limiting, context caching layers, and token optimization protocols to keep your operational overheads as lean and predictable as possible.