FoodBridge demo
Coordinate the collection
Arrange and track transport so a match becomes a completed collection.
The challenge
Matches fail at the transport step, and nobody finds out until the food is already discarded.
The scenario
A match is made but no vehicle is confirmed, and the window is four hours.
What goes in
- Confirmed matches
- Donations paired with recipients.
What you can ask
Illustrative prompts. Results depend on your own data and are not deterministic.
- “Arrange collection for these donations.”
- “Where is collection failing most often?”
- “Which pickups are at risk today?”
How Clearception approaches it
How the work is structured. Each step shows what its output is grounded in.
Schedule collection
SourceArrange transport against the available window.
Track
ObservedFollow the collection to completion.
Flag risk
AI-inferredSurface pickups unlikely to happen in time.
What you get
The shape of what comes back — not a promised result.
Collection schedule
ObservedPickups with status and at-risk items highlighted.
Why it matters
- Fewer failed collections
- Problems surface while there is still time to fix them.
Related demos
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Match surplus to nearby programmes by what they can actually collect, store and use.
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Report what actually moved
Produce donation impact reporting as a by-product of the workflow rather than a separate exercise.
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Find surplus while it can still be donated
Surface surplus from operational signals early enough to arrange collection.
3 minute walkthrough
- Inventory Optimisation
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Want to try this with your own work?
Open FoodBridge and bring your own data. No signup is needed to read these demos.