Budgets, programs, contracts and companies, joined in one record.
One reconciled record of government spending, delivered where the work happens: in the application, through the API and feeds into your models and warehouse, and over MCP to the agents you already run.
Budget justifications, committee reports, enacted appropriations, contract actions, solicitations, opportunities, corporate registries, agency structures and the officers who buy, held together as one graph.
An analyst works in it directly. Research a company, program, office or market, monitor a portfolio, screen, and produce a sourced report someone else can check.
Every view as an endpoint; bulk feeds for teams that would rather load the graph than query it.
Digests, screens and alerts scheduled over MCP from assistants you already run.
Deterministic underneath, probabilistic on top.
If a user asks HighGround for contract utilization, the model should understand the question, identify the right company or contract, retrieve the calculation, and reason about what it means. It should not invent the definition of utilization at inference time.
Utilization, market concentration, obligation growth, ceiling growth and competitive intensity are defined in code, applied identically on every run, and tied to a known source vintage. For the same data and the same definition, the answer holds regardless of which model is providing the interface.
A better model can produce a more convincing wrong answer. The model sits at the top. It does not define the layers underneath it.
Why loading the data is not enough.
Loading government data into a warehouse is a solved engineering task. Most failures in this market are not missing-data failures. The record exists, and it could be accurate. The interpretation is what is wrong.
Government systems identify the legal entity that registered or received an award. Investors care about the economic entity. HighGround maintains both levels, resolved through ownership changes at the modification level, and records which one each answer used.
Contract history is kept at the modification grain, not collapsed to latest state, and data vintage is preserved. So you can ask what was knowable last March, and whether a signal actually led the outcome or only looks predictive in hindsight.
One field holds a single modification, one the cumulative obligation, one total potential value. Read the cumulative where the incremental belongs and a routine modification looks like billions in new activity. The system encodes what each field means and which calculation it belongs in.
One company, three contracting streams, a single record.
What is in the dataset.
Your agents, working over the record.
HighGround exposes the graph and its calculations over MCP, so assistants you have already built can ask the same questions an analyst asks and get the same sourced answers.
Schedule them. A morning digest on your watchlist, a re-run of a screen when the budget updates, an alert when a vehicle crosses 80% of ceiling.
The same evidence in your models and warehouse, on your schedule.
Everything in the application is reachable by API, keyed to the identifiers your systems already use. Bulk feeds land the graph in your warehouse; MCP carries the same sourced answers to your agents.
Point in time: every figure as it stood on any prior date, so backtests use what was knowable, not what is known now.