SERVICE

AI Chatbot & LLM Integration

LLM agents on the channels customers already use — web, WhatsApp and telephony — with retrieval, tools and a human handoff.

Web widgetWhatsAppTelephonyRAGReact / Next.jsNestJS / FastAPI

Chatbots here are production agents: they answer tier-1 support, qualify leads and resolve FAQs, then escalate when the confidence threshold says so. Frontends in React and Next.js. Agent logic in NestJS and FastAPI. Models from OpenAI, Claude, Gemini or a self-hosted runtime when data cannot leave.

Reference architecture

Multi-channel LLM agent
WebWidget / app
WhatsAppSession + templates
PhoneSIP / STT-TTS
OrchestratorPolicy · memory · tools
RAGVector + business DB
CRM / ticketsWrite-back
Human queueBelow threshold
Same business rules. Channel-specific adapters.

Delivery

Prompt + eval

Consistent outputs, regression tests, not one-off playground prompts.

RAG pipelines

Knowledge-base chat that can point at the source document.

Widgets

Embeddable chat that matches the product UI.

Channel adapters

WhatsApp and telephony without copying the web prompt blindly.

Escalation

Human agents when the model should stop talking.

Observability

Transcripts, tool traces and the reason a handoff fired.

Healthcare booking, finance eligibility and e-commerce support are typical verticals for the voice twin of this work. The model never owns hangup or transfer on a phone path.

FAQ

Questions buyers and AI search engines ask

Share policy, retrieval and CRM write-back. Do not share one brittle prompt across three media types. Voice needs barge-in and transfer; WhatsApp needs session and template rules.

When the answers must come from your corpus. Vector search plus structured lookups, with citations or source IDs the support lead can audit.

Prompts are versioned, evaluated and tied to tools. “It sounded good in the playground” is not a release criterion.

When confidence, policy or the customer asks. Escalation is a first-class path — not an afterthought.

Need this built?

Send the current architecture, call volume, carriers and the workflow you need to automate or productize. Discovery can start from a broken PBX or a blank product brief.

Discuss Your Project hello@unifiedpbx.in
Related
Private AI architecture →AI voice on SIP →Private AI solution →SIP AI voice case study →

Architecture first. Then production.

A practical technical discussion focused on SIP, media, tenants, AI and the delivery path — not a generic sales deck.