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Conservation demo

Identify species across a camera-trap deployment

Turn tens of thousands of frames into species records, each with confidence and its frame.

Try Conservation
4 minute walkthrough

The challenge

A deployment produces far more frames than anyone can review, and most of them are empty.

The scenario

Three months of camera-trap data has arrived and the ecology team has two days to make sense of it.

What goes in

Camera-trap imagery
Frames as captured, including empty ones.

What you can ask

Illustrative prompts. Results depend on your own data and are not deterministic.

  • “Identify the species in this set of frames.”
  • “Which identifications are least certain?”
  • “Show me everything unreviewed.”

How Clearception approaches it

How the work is structured. Each step shows what its output is grounded in.

  1. Filter empties

    Observed

    Separate frames with no subject from those worth review.

  2. Identify

    Observed

    Classify species with a confidence value per frame.

  3. Queue for review

    AI-inferred

    Surface low-confidence identifications for human confirmation.

  4. Record confirmation

    Human-verified

    Log human decisions distinctly from model output.

What you get

The shape of what comes back — not a promised result.

  • Species records

    Observed

    Identifications with confidence and the frame behind each.

  • Review queue

    AI-inferred

    Uncertain cases ordered for expert attention.

Why it matters

Expert attention where it counts
Empty frames stop consuming review capacity.
Auditable records
Every record keeps its frame and its confidence.

Related demos

Want to try this with your own work?

Open Conservation and bring your own data. No signup is needed to read these demos.

Open ConservationSee pricing