Engineered for precision, security, and scale
Discover how our shared NestJS core backend, Pinecone vector ingestion, and Next.js frontend work together to deliver an enterprise-grade AI chatbot experience.
Multi-Source RAG Knowledge Engine
Never train on stale data. Ingest sitemaps, PDFs, customer documentation, and FAQs. Our BullMQ worker pipeline automatically cleans, chunks, and creates OpenAI embeddings stored in Pinecone with strict tenant namespace isolation.
- • Automated sitemap web crawler with scheduled recrawls
- • Content hashing to prevent redundant vector reprocessing
- • Strict per-tenant vector partitioning preventing data leakage
15-Stage AI Orchestration Pipeline
We never do simple user-to-LLM forwarding. Every incoming message passes through tenant resolution, rate limiting, intent classification, context building, model routing, guardrails, and response validation before hitting the browser.
- • Dynamic prompt injection and instruction-hijacking defenses
- • Multi-provider model routing (GPT-4o, Claude 3.5, Gemini 1.5)
- • Controlled business function calling with backend authorization
Seamless AI to Human Takeover
When a visitor asks complex questions or requests a human agent, the conversation engine smoothly transitions control. The agent sees the full chat history and can reply directly in real-time while the AI is paused.
- • Real-time inbox with unread and escalation filters
- • Email and webhook alerts dispatched immediately to staff
- • Return-to-AI toggle when the agent completes the support session
Live Google Calendar Scheduling
Visitors can book meetings directly inside the chatbot window. The backend checks real-time slot availability via encrypted OAuth tokens, handles timezone conversions, and creates idempotent calendar events with Google Meet links.
- • Atomic booking creation preventing double-booking slots
- • Encrypted token storage at rest with AES-256
- • Automated confirmation emails sent to both host and attendee