Data Infrastructure

Proprietary datasets
built for energy development.

We ingest public records, regulatory filings, and geospatial sources — then synthesize them into structured, linked datasets purpose-built for energy development.

Data Infrastructure

Energy data is fragmented.

The information that power developers need to make decisions — interconnection queues, parcel ownership, permit status, grid capacity — exists. But it's scattered across thousands of county websites, regulatory portals, and federal databases. Each source has its own format, update schedule, and access method.

The result: development teams spend weeks on data collection that should take minutes. Analysts toggle between dozens of browser tabs. Critical information gets trapped in PDFs that no one reads. And decisions get made on stale spreadsheets instead of live data.

We built Basepoint's data layer to solve this — by building our own from the ground up.

How we synthesize our datasets
From raw source to decision-ready intelligence.

01
Ingest

Collect from hundreds of sources

We continuously pull from federal agencies (FERC, EIA, DOE, EPA), state PUCs, ISO/RTO portals, county GIS systems, utility filings, and satellite imagery providers. Our scrapers, API connectors, and FOIA pipelines run on a rolling schedule — so when a new interconnection filing hits a queue, it's in Basepoint within hours, not months.

02
Parse

Extract structure from unstructured data

Most energy data arrives as scanned PDFs, inconsistent spreadsheets, or buried in HTML tables. Our extraction layer uses purpose-built models to identify tables, parse legal descriptions, geocode addresses, and normalize units — turning a 400-page utility IRP into structured, queryable records.

03
Normalize

Reconcile across schemas and jurisdictions

Every ISO names things differently. Every county formats parcel IDs differently. We maintain a canonical schema that maps CAISO queue entries, PJM capacity data, ERCOT filings, and SPP studies into a unified model — so you can compare a project in Texas to one in Virginia without translation.

04
Link

Connect records into a knowledge graph

A substation isn't just a point on a map — it's connected to transmission lines, interconnection queues, capacity allocations, nearby parcels, zoning overlays, and environmental constraints. We link these relationships using geospatial joins, entity resolution, and temporal matching to build a complete picture of every site.

05
Validate

Cross-reference and flag anomalies

We run automated validation against known ground truths — cross-checking queue positions against utility confirmations, verifying parcel boundaries against county records, and flagging data that drifts outside expected ranges. When conflicts arise, we surface them rather than silently picking a winner.

06
Deliver

Serve through platform, API, and models

The final dataset powers everything in Basepoint — site screening, grid analysis, financial modeling, and our AI assistant. The same data is available through our REST API for teams that need to integrate it into their own systems. Every record carries provenance metadata so you can trace it back to the original source.

Why proprietary data matters.

Licensed data feeds give you the same information as everyone else. Our approach gives you an edge.

Fresher than any alternative

We monitor and aggregate from multiple sources on rolling schedules. When a new queue entry is posted or a permit application is filed, it appears in Basepoint hours later — not after a quarterly vendor update.

Linked, not siloed

Most data providers give you tables. We give you relationships. Every parcel is connected to its grid infrastructure, zoning, environmental constraints, and regulatory history — because that's how real decisions get made.

Full provenance

Every data point in Basepoint carries metadata — where it came from, when it was collected, how it was extracted. When your VP of Development asks "how did you get this number?", you have a traceable answer.

Built for AI

Our datasets are structured specifically to power AI workflows. When you ask Basepoint's assistant a question about a site, it reasons over clean, linked data — not noisy web scrapes.

What we cover
Continuously expanding across the U.S. energy landscape.

Interconnection & Grid

ISO/RTO interconnection queues (CAISO, PJM, MISO, ERCOT, SPP, NYISO, ISO-NE)
Substation capacity and loading data
Transmission line ratings and congestion
Generator retirement and addition schedules

Land & Parcels

County parcel boundaries and ownership records
Zoning designations and land use classifications
Land value assessments
Utility territory maps
Topography, pipeline infrastructure, and flood zone data

Permitting & Regulatory

State and local permit applications and approvals
Municipal code and meeting minutes
Environmental impact assessments
FAA obstruction evaluations
Utility commission dockets, FERC filings and rate case proceedings

Resource & Market

Solar irradiance and yield estimates
Wind speed and power density
PPA pricing benchmarks by region and technology
Wholesale electricity price
REC and carbon credit market data
Our approach

We treat data like infrastructure, not a feature.

Most energy software companies bolt a data layer onto their product as an afterthought — licensing a feed here, scraping a website there. The result is gaps, staleness, and no way to trace where a number came from.

We started with data. Our engineering team builds and maintains the ingestion pipelines, parsing models, normalization schemas, and validation systems that turn scattered public records into a unified knowledge graph. The platform is built on top of this foundation — not the other way around.

This is hard, slow, unglamorous work. But it's the reason Basepoint can give you answers that other platforms can't — and the reason those answers are ones you can trust.

Schema over shortcuts

We invest in canonical data models instead of duct-taping CSVs together.

Provenance by default

Every record traces back to its source, timestamp, and extraction method.

Continuous, not periodic

Our pipelines run on rolling schedules, not quarterly batch updates.

Validation before delivery

We cross-reference and flag anomalies rather than silently passing through errors.