Case study 01 · Conversational AI
Omnichannel AI operations platform
One workspace to create, control, and run AI assistants across six messaging channels.
Product architecture + interface system · Client name withheld
- Adapters
- Knowledge
- Guardrails
Challenge
Businesses often add bots one channel at a time. Settings, history and quality checks end up scattered, with no reliable handoff to a person.
Design one model that handles different business roles, shared knowledge, each channel’s limits, team permissions, and a clear path from bot to human.
System response
One assistant setup is reused across every channel, with each business’s data kept separate and one inbox for operators. Deployment is a guided workflow, not a developer-only task.
Product scope
- Multi-tenant organisation and role controls
- Reusable AI employee personas and working rules
- WhatsApp, Telegram, SMS, Messenger, Instagram, and web adapters
- Knowledge ingestion with source-aware retrieval
- Response validation, escalation, and human handoff
- Conversation quality and channel analytics
System architecture
Channel adapters normalise inbound and outbound messages.
Conversation state keeps context across channels.
Knowledge retrieval grounds answers in approved material.
Guardrails check responses before they are sent.
The operator layer handles exceptions, handoff, and oversight.
Observable outcome
What the product and its repository show. These are not confidential client figures.
One configuration model for many customer-facing channels
Clear separation between automated work and operator decisions
Quality controls built into the workflow, not added later
Repository evidence
- Detailed multi-tenant product requirements
- Channel capability and degradation rules
- Role, knowledge, guardrail, and analytics models
Built with
- Next.js
- TypeScript
- LangGraph
- PostgreSQL
- Row-level security