Every score starts with data. Every recommendation starts with a score.
OptiScore evaluates EV charging investment potential at two levels of detail — the surrounding market and the exact site — and combines them into a single, defensible verdict.
How OptiScore works
From neighborhood to exact address in three steps.
Tract-level scoring
We divide the country into census tracts — neighborhood-sized areas of 2,000–8,000 people. For each, we fuse demographics, EV adoption, existing infrastructure, highway access, employment, and housing into four composite indices. This answers: how good is this general area?
Site-specific adjustment
A tract can span several square miles. When a user searches an address, we refine the tract scores with micro-location data: proximity to the nearest highway ramp, nearby commercial activity, the residential character of the block group, and a live count of competing chargers within a half-mile.
The verdict
The adjusted sub-scores feed the Opportunity Score (0–100), synthesizing demand potential, competitive pressure, pricing power, and site attractiveness into a single investment signal. Higher is better.
The four indices
Each index is a composite computed for every market, then refined for the exact site. Our inputs are transparent; the model that combines them is proprietary.
Demand Potential
How much charging demand potential exists in this area.
Market Pressure
Higher = more crowdedHow saturated the existing charging market already is.
Pricing Power
Whether the local market supports premium charging rates.
Gap Score
The net opportunity — demand potential minus supply.
Built on public, auditable data — no black boxes.
Every input comes from authoritative federal, state, and open-source datasets. Our model is proprietary; our inputs are transparent.
Registration sources vary by jurisdiction. 33 states publish EV counts at ZIP, county, or finer granularity — pulled directly from state DMVs, Atlas EV Hub, and open-data portals. The remaining 18 report only a statewide total (via U.S. DOE AFDC); those are allocated to counties by income-weighted population until a finer source is published.
Browse all 51 jurisdiction sourcesexpand ▾
Rigorous, reproducible, multi-granularity.
OptiScore measures relative opportunity — it ranks each market and site against its peers, so a 73 is a clearer bet than a 51 today. It's deliberately not a demand forecast: with EV adoption still early and charging behavior immature, we've found benchmarking more honest than projecting absolute numbers. That's why we speak of demand potential rather than predicted demand. As richer utilization data becomes available, we expect to layer in forecasting where the data supports it.
Multi-granularity scoring
We layer tract-level demographics with block-group residential patterns and block-level commercial activity, then add site-specific measures drawn from a radius around the exact address — nearby competing chargers, corridor proximity, and surrounding amenities. Together they distinguish a highway interchange from a quiet side street within the same tract.
Peer-group normalization
A suburban New Jersey tract isn't compared against downtown Manhattan. Every variable is normalized within its peer group, so scores reflect true local context — not geographic noise.
Public inputs, proprietary model
Every data source is publicly available and independently verifiable. Our value is in how we combine, weight, and adjust these inputs — not in access to secret data.
Continuous refresh
Data is refreshed as its underlying public sources are updated — we monitor them regularly and pull the latest as it's released. AFDC competitor-station data is refreshed frequently, and the station map reads from a live public feed.
See your market before you commit capital.
Now onboarding a limited group of early partners for pilot analyses and product feedback.