Agremo

Drone-captured aerial imagery of damaged crops processed through AI, machine learning and computer vision into quantified damage maps. Distinct analyses are productised per peril: a Hail Damage analysis, Green Snap detection and Lodging detection for annual crops, each returning identified affected areas, damage extent and crop type rather than a single blended score. Built to replace manual adjuster field scouting, so outputs are claim-ready damage maps with visual evidence attached, produced at drone resolution — centimetres per pixel rather than the 10 m of any satellite source

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Coverage

Universe, instruments and categories.

Industry: Application SoftwareMulti-line InsuranceAgricultural Services
Instruments: equitiescommoditiesstructured_products
Categories: AlternativeComputer Vision TrainingInsurance LossGeospatial
Regions: USOtherAPAC
Sample tickers: RNRHRGSIGIPGRDEADMCTVA

Dataset card

Type: not stated
Format: Very-high-resolution drone orthophotography and derived raster damage layers with polygon area attribution, plus tabular area and severity summaries; imagery and evidence layers are retained as claim attachments, so the corpus is image-plus-derived-measurement pairs
Volume: Est. tens of thousands to low hundreds of thousands of drone flights processed over the operating life, each producing one or several high-resolution orthophotos plus a derived damage layer. Because each orthomosaic runs to hundreds of megabytes at centimetre resolution, the raw imagery archive plausibly reaches hundreds of terabytes to a few petabytes — and it is the per-instance damage annotation on top of that imagery, not the volume, that carries the value
Users: N/A in consumer terms — a B2B analysis platform sold to insurers, agronomists and crop-service providers. Est. low hundreds of professional accounts (inferred from 24 employees, roughly $2M revenue and a per-analysis or subscription commercial model rather than an enterprise seat motion)
History: not stated
Update frequency: not stated
Growth: Active — current use-case and product pages maintained across a widening analysis catalogue including a dedicated hail-damage product, an active insurer-facing go-to-market, seed funding of $1.3M and roughly $2M revenue recorded against 24 employees, indicating commercial traction beyond the funding
Launched: ~2016-2018 — the corporate record is unusually old for such a small team, opened in 2016, and the product presentation reflects a mature rather than newly-launched analysis engine; the insurance-specific damage line is later, est. 2020-2022, as climate-driven claim volume pushed insurers toward drone evidence
Delivery: not stated
Entity mapping: not stated
Sample: not stated
Point-in-time: not stated
Licence: not stated

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