Asset Location Data is the methodology behind alphaX's physical-footprint intelligence — the coordinate-level layer that every nature, climate and geopolitical risk score at alphaX is sampled against. It can't be bought off the shelf as a location file.
Every position an institution holds has a physical footprint — mines, plantations, refineries, terminals, warehouses. A drought does not hit a company, it hits a river basin, and with it the eleven facilities inside that basin rather than the ninety outside it. Nearly every risk analytic on the market still scores at company or sector level, because pinpointing where a company actually operates has been too expensive to do at scale.
An asset location on its own says nothing about how contested the ground underneath it is. The same discovery pipeline also geolocates everything else sharing that basin, watershed or grid connection — competitors, farms, water utilities, ports — so a scarcity signal can be attributed to the right assets instead of averaged across a sector.
Reference assets are typed and geolocated with the same discovery pipeline used for target companies — spanning agriculture, energy, logistics, government and more. Grounded against over 8,000,000 reference locations, so a basin-level view can account for who else is drawing on the same resources.
Asset Location Data is the layer everything else at alphaX sits on. Every risk layer is only as precise as the coordinate it is sampled at, and every financial attribution is only as sound as the entity the asset is tied to. Beyond name, address, latitude and longitude, each record carries the fields that let an analyst judge how much to trust it, and how much to care.
coord_precision is one of rooftop, street, neighbourhood, city or country — so an analyst can exclude anything coarser than street level before running a basin-level intersection.
Operational, under construction, planned, closed, mothballed, temporarily closed, closing, or under exploration — read alongside the date that status was determined.
Capacity and units capture operational throughput where it is disclosed. Without it, a site count cannot be weighted for how much it actually matters.
Source, attribution and research date travel with the record, alongside the source's own asset description next to our standardised classification — so any mapping decision can be re-examined.
There is no single method that finds corporate facilities across every sector and country, so we didn't build one. Each stage below is an independent component under a central orchestrator, and every stage reads and writes the same strictly-typed asset schema — so sources as different as a mining registry and a company's own site map converge into one structure.
Build the company's corporate family, subsidiaries and ownership structure first — every later stage reads this.
Pull candidate assets from structured public registries covering specific sectors, then resolve each row to the company.
An agentic system with search tooling nominates candidate URLs, fetches them, and extracts structured asset rows targeting registry gaps.
Geocode addresses that arrive without coordinates, record the source and precision of every coordinate, then classify the asset type.
Clean, prioritise, deduplicate, and run a coverage check against the company's expected footprint.
A final verification pass over the coordinates themselves, closing the loop back to the schema.
Registry rows are matched back to companies using Fellegi–Sunter probabilistic record linkage — the standard statistical framework for deciding whether two records describe the same entity. Non-matches are dropped rather than guessed at.
The same probabilistic linkage method used across official statistics agencies decides whether a registry row and a company are the same entity — with a defined confidence, not a string-similarity guess.
They become known assets, and they tell the web-discovery stage which sites are already covered — so it searches for what is missing instead of re-finding what it already has.
Asset location data always contains a degree of error — the question is whether the pipeline is built to find it. Ours applies three checks, different in kind, so they fail independently.
Ocean boundary layers flag land-based asset types geocoded into the sea, with genuine offshore types such as rigs and wind farms excluded. Urban boundary layers flag large-scale mining sites placed in the middle of a city.
A duplicate is the same company, the same asset type, within a defined proximity. Candidates are confirmed individually, then collapsed to the more authoritative record. Every merge keeps an audit trail.
Rather than only asking whether each asset found is correct, an automated check asks whether the company's expected footprint has been captured at all. A high-severity gap re-triggers discovery, up to three times.
Raw descriptions arrive as things like "Fab 4", "Assembly Plant" or "Coal Unit 1". We map them to a custom taxonomy of 50 asset type codes in 14 top-level groups, grounded in NAICS 2022 but adapted for physical and nature materiality.
A persistent mapping table resolves any description seen before — no model call needed.
Unseen descriptions go to a language model, which assigns a best-fit code and a confidence flag. High-confidence results join the mapping table.
Ambiguous cases — "Manufacturing Plant" could be food, chemical or machinery — are resolved per company using that company's own NAICS revenue breakdown. Only the remainder reach a model again, with full company context.
A new asset database publishes every month. Underneath that, investigation cycles are tiered by how fast the physical world actually changes.
Offices and plants that stay put are re-investigated on a slower cycle — the physical reality barely moves year to year.
Data centres are the obvious case: build-out is fast enough that an annual cycle would publish a map that's already wrong.
Countries or regions seeing heavy infrastructure investment or regulatory change get prioritised the same way — the cadence follows the risk.
Nature, climate and geopolitical risk are all sampled at the coordinates Asset Location Data provides. Building it required the same financial-data foundation that most competitors sell as separate products, if they sell them at all.