Not Just Calling an API.
Building a Moat.
Feedii's core innovation is a proprietary multi-model dynamic routing architecture — and a correction data flywheel that gets smarter with every user interaction.
Multi-Model Dynamic Routing
How we dynamically orchestrate AI models for maximum accuracy at minimum cost
System Architecture
Feedii AI Routing Layer
User Action
📸 Photo / Text / Voice Input
Core Innovation
🔀 Dynamic Router
Query complexity analysis
Primary Model
Gemini
Complex multi-ingredient meals
High visual accuracy
Triggered: ambiguous inputs
Secondary Model
DeepSeek
Simple text lookups
Cost-efficient routing
Triggered: simple inputs
Result
✅ Structured Nutrition Data
Calories, macros, confidence score
Feedback Loop
🔄 User Correction → Training Signal
Every correction improves future accuracy
Speed Without Sacrifice
Simple queries route instantly to the lightweight model. Complex dishes escalate to the powerful vision model. Users get sub-second responses regardless of complexity.
Cost-Efficient at Scale
Dynamic routing reduces API costs by 60-70% compared to sending every query to the most expensive model. This enables sustainable unit economics at our price point.
Model Agnosticism
Our routing layer is model-agnostic. As better models emerge, we swap them in without changing the user experience. We're never locked into one provider.
The Correction Data Flywheel
Our defensible moat — a closed loop that compounds with every user interaction
Core
Feedii
AI Engine
User Logs Meal
AI generates nutritional estimate from photo or text
User Corrects AI
When AI is wrong, user adjusts the value
Correction Captured
Every correction is a labeled training signal
Model Improves
Accuracy compounds — harder for competitors to replicate
↻ Loop repeats with every meal logged — compounding accuracy over time
🏰 Why This Is Our Moat
1,000,000+ structured data points already validated
Correction data is proprietary — competitors can't buy it
Each user cohort creates regionally-specific accuracy
Data flywheel accelerates as user base grows
⚠️ Why Big Players Can't Copy This
They have broad models, not nutrition-specific correction loops
Our data is contextualized by meal time and user history
Privacy-first design means our data is richer — users trust us more
Switching costs grow as personalization deepens
Technical Milestones
What we've built, validated, and proven
Structured data points
Validated across core decision logic
App version shipped
Production iOS app with full AI pipeline
Avg. AI response time
From photo capture to nutrition data
Dynamically orchestrated
Gemini + DeepSeek routing layer live
Built for Privacy. Trusted by Users.
Our AI pipeline processes images transiently — photos are analyzed and immediately discarded. No biometric data stored. No data sold. This isn't just policy — it's architecture.
No Photo Storage
Images processed in-memory, never persisted to servers
No Data Selling
Your nutrition data is yours. We generate revenue from subscriptions only.
Right to Delete
One-tap account + data deletion. Always.