FreshRoute AI
Predict Demand. Reduce Waste.
Deliver Smarter.
Urban Food Supply Matchmaker with Predictive Logistics
FARMERS
BUYERS
SMART LOGISTICS
01 / OPPORTUNITY
The problem
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Disconnected supply
Farmers struggle to reach reliable urban buyers and often lack visibility into local demand.
Uncertain demand
Restaurants and retailers need fresh produce but cannot always predict what is available.
Inefficient deliveries
Poor coordination increases delays, avoidable trips, and potential food waste.
02 / SOLUTION
One platform. Three connected decisions.
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Predict
Estimate near-term produce demand from historical records and identify supply gaps.
Match
Rank compatible farmers and buyers by produce, quantity, price, and proximity.
Deliver
Group delivery stops and suggest practical routes with clear distance assumptions.
FreshRoute AI turns scattered supply and demand into actionable logistics decisions.
03 / WORKFLOW
How FreshRoute AI works
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01
Farmer lists produce
02
Buyer posts demand
03
Forecast engine estimates needs
04
Matching engine ranks offers
05
Logistics planner sequences stops
Outputs: marketplace matches • supply-gap insights • suggested delivery plan
04 / PRODUCT PREVIEW
Dashboard concept
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Illustrative UI concept, not a verified screenshot of the deployed application.
05 / PREDICTIVE ANALYTICS
Demand forecasting + supply-gap intelligence
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Forecasting baseline
Use recent historical demand and a moving-average baseline to estimate short-term demand by product and area.
Actionable insights
Compare forecast demand with listed supply, flag possible shortages, and suggest sourcing actions.
Demo forecasts must be labeled when based on synthetic data. Real-world accuracy requires validation.
06 / LOGISTICS
Smart matching and delivery planning
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Matching logic
Filter by produce type, quantity, and budget. Rank candidates using a transparent score and proximity when coordinates exist.
Delivery logic
Display farms and buyers on a map. Suggest stop order using a lightweight nearest-neighbor heuristic.
Approximate straight-line routes are not claimed as verified road travel times or globally optimal routes.
07 / TECHNICAL IMPLEMENTATION
Technology architecture
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Frontend
React • Vite • Tailwind • charts • map
Backend
Python • FastAPI • REST endpoints
Data
SQLite • listings • demand • orders
Intelligence
Forecasting • matching • hosted Gemma*
Extensions
Snowflake CoCo* • Solana Agent Registry*
* External integrations require real credentials, verified access, and successful tests.
08 / DIFFERENTIATION
AI and hackathon integrations
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Gemma 4*
Explain forecasts and help create produce descriptions through verified cloud-hosted inference.
Snowflake CoCo*
Assist with supply-demand analytics and SQL workflows when configured and verified.
Solana Registry*
Register the logistics agent through an official registry flow with approved wallet signing.
* Planned or conditional integrations: do not present as completed until verified.
09 / DEMO SCRIPT
Live demo: the farmer-to-city journey
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1. Create supply
Farmer adds a tomato listing.
2. Create demand
Restaurant posts a tomato requirement.
3. Show intelligence
Dashboard reveals forecast and ranked match.
4. Plan delivery
Map displays suggested delivery stops.
5. Explain decisions
Show real hosted AI output if connected.
10 / ROADMAP
Impact and next steps
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What the MVP demonstrates
A connected workflow from produce listing to buyer matching, demand insights, and delivery suggestions.
What comes next
Validate forecasts on real demand data, connect road-routing services, onboard suppliers, and verify external integrations.
Measure success through match fulfillment, time saved, and reduction in avoidable produce loss.
FreshRoute AI
Smarter food supply starts with better connections.
THANK YOU