aiProductionMULTI-MODAL
WhatsApp Multi-Modal Agent
Text · Image · Voice in one agent
Handles text, images, and voice messages. Intelligently routes to the right handler and responds in the correct modality.
Case study snapshot
Role
AI systems builder
Audience
AI product users and recruiters
Timeline
2025-08
Role
AI systems builder
Audience
AI product users and recruiters
Date
2025-08
Problem
Why it mattered
Unified multi-modal AI interface
Solution
What shipped
Handles text, images, and voice messages. Intelligently routes to the right handler and responds in the correct modality.
Impact
Proof signal
Unified multi-modal AI interface
< architecture />
System flow
Modality Router -> NLU / Vision / ASR -> Agent Brain -> Response (Text/TTS).
Modality Router
NLU / Vision / ASR
Agent Brain
Response (Text/TTS)
Visual architecture
01
Modality Router
02
NLU / Vision / ASR
03
Agent Brain
04
Response (Text/TTS)
quality score
Honest maturity snapshot
Code Quality8/10
UI / UX6/10
Scalability7/10
Production Ready8/10
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.
- WhatsApp API integrated into the workflow.
- Whisper integrated into the workflow.
- Modality Router stage documented in the delivery flow.
- NLU / Vision / ASR 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
- Unified multi-modal AI interface
- 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: Unified multi-modal AI interface
- Next iteration: Add richer screenshots or a narrated demo.
< stack />
n8nGeminiWhatsApp APIWhisper
< 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.