Launching today
Gemral Edge
Point-in-time US public records, tied to the right ticker
1 follower
Point-in-time US public records, tied to the right ticker
1 follower
US federal contracts, defense contract announcements, lobbying, congressional and insider trades, tied to the ticker that was valid on the day each record became public. Delivered as Parquet files, a REST API and an MCP server for AI assistants.
Hi Product Hunt, I'm Jennie, founder of Gemral Edge.
If you have ever joined public government records to stock tickers, you know where it breaks. A contract goes to a subsidiary or a holding LLC. A ticker gets renamed or reused. The record you joined on was dated by when the work happened, not when anyone could read about it. Your history quietly looks better than reality.
Edge fixes the join. Every record is tied to the company and ticker that were valid on the day it became public. Publication lags are applied, so the data can be joined without look-ahead. Delisted and acquired companies stay in the history under the ticker they had at the time.
What's inside: federal contract transactions, daily defense contract announcements, subcontractor links, bid protests, lobbying filings, corporate PAC contributions, congressional trades and insider trades. For AI-infrastructure companies we also track data-center sites, air permits, permitted backup-generator MW, substations, grid demand, equipment imports, capex and satellite measurements.
Each record type ships only after a sampled accuracy check against the original public documents (95% minimum). Data comes only from public sources and contains no material non-public information.
What it does not do: permitted backup-generator MW is an upper bound on on-site backup capacity, not IT load; we do not convert it. Imports are industry context by product code and origin country, not company-level shipments.
Use it as Parquet files, a REST API, or connect your AI assistant through the MCP Registry (search "gemral-edge"). More at https://www.gemral.com/edge/intro
We also published two working papers that show how the data is built and checked, and measure what a naive join gets wrong: https://www.gemral.com/edge-research/papers/white-paper-dating-government-demand.pdf and https://www.gemral.com/edge-research/papers/white-paper-physical-bottlenecks-ai-infrastructure.pdf
Questions or a sample file: support@gemral.com. I read every reply here today.