aiResearchRESEARCH
Zorox
WhatsApp Agent w/ Intent Routing
Production WhatsApp agent with multi-intent routing, context memory, and human escalation paths.
Case study snapshot
Role
AI systems builder
Audience
AI product users and recruiters
Timeline
2026-02
Role
AI systems builder
Audience
AI product users and recruiters
Date
2026-02
Problem
Why it mattered
The project needed a clear, working implementation of WhatsApp Agent w/ Intent Routing.
Solution
What shipped
Production WhatsApp agent with multi-intent routing, context memory, and human escalation paths.
Impact
Proof signal
Delivered a working project with a clear technical outcome.
< architecture />
System flow
Intent Classifier -> Context Memory -> Tool Selection -> Escalation.
Intent Classifier
Context Memory
Tool Selection
Escalation
Visual architecture
01
Intent Classifier
02
Context Memory
03
Tool Selection
04
Escalation
quality score
Honest maturity snapshot
Code Quality8/10
UI / UX6/10
Scalability7/10
Production Ready6/10
Before
Manual questions, scattered context, and slow follow-up.
After
An agent workflow that routes intent, calls tools, and returns useful next actions.
capabilities
Features
- n8n integrated into the workflow.
- Gemini integrated into the workflow.
- Redis integrated into the workflow.
- Postgres integrated into the workflow.
- Intent Classifier stage documented in the delivery flow.
- Context Memory stage documented in the delivery flow.
tradeoffs
Engineering challenges
- Keeping the implementation clear enough to explain while still solving the core technical problem.
- Choosing a scope that could be shipped, tested, and documented.
- Turning technical work into proof a visitor can evaluate quickly.
proof
Results
- Delivered a working project with a clear technical outcome.
- Documented the engineering path and important learnings.
- Made source code or technical proof available for review.
reflection
What I learned
- Tradeoff learned: Keeping the implementation clear enough to explain while still solving the core technical problem.
- Proof learned: Delivered a working project with a clear technical outcome.
- Next iteration: Add richer screenshots or a narrated demo.
< stack />
n8nGeminiRedisPostgres
< next iteration />
Future improvements
Add richer screenshots or a narrated demo.
Add before/after metrics and usage notes.
Package the project as a reusable template or deployable demo.