The Problem
The Problem
Customer conversations arrive in multiple formats and can be fragmented across consecutive messages. The system needs to process that context coherently and preserve a human escalation path.
What I Built
What I Built
I built a multimodal workflow with webhooks, HTTP Request integrations, JSON processing, an intelligent message buffer, LLM-driven business logic, persistent context and human handoff.
- Text, audio and image inputs
- Webhooks and HTTP Request integrations
- JSON processing and message normalization
- Intelligent buffering before agent processing
- PostgreSQL context and Qdrant-supported retrieval
- Human handoff when needed
Architecture
Sanitized public system flow
Public Architecture / Generic Labels
Text / Audio / ImageSystem flow
Webhooks + HTTPSystem flow
JSON ProcessingSystem flow
AI ProcessingSystem flow
Business LogicSystem flow
PostgreSQL / QdrantSystem flow
Text / Audio / Image→Webhooks + HTTP→JSON Processing→AI Processing→Business Logic→PostgreSQL / Qdrant→Automated Response→Human Handoff
This public diagram is intentionally abstracted and does not expose client names, private endpoints, internal IDs or operational data.
Technologies
Technologies used
n8nAI / LLMsWebhooksHTTP RequestJSONPostgreSQLQdrant
Value / Outcome
Capability created
The system automates multimodal customer processing while preserving context and human control for cases that need intervention.