APOGEE

Strategic Intelligence

Higher-order analysis combining multiple intelligence types - competitive landscape mapping, coalition building recommendations, early warning systems, and appropriations tracking.

Strategic Intelligence

See the summary view → for a quick overview of all Strategic Intelligence capabilities.

4 capabilities that combine multiple intelligence types into higher-order strategic analysis. These go beyond "what happened" to answer "what should I do about it."

Competitive Landscape Mapping

Unified view of the full influence landscape around a policy issue or specific bill - who's lobbying, who's funding, who's allied, and who's opposed - assembled from lobbying disclosure filings, PAC contribution records, cosponsorship networks, organizational stance data, and news coverage into a single competitive map. This is the "big picture" view that government affairs teams need before engaging on a policy issue.

Today, building a competitive landscape requires manually cross-referencing SOPR lobbying filings, FEC contribution data, cosponsorship lists, press release positions, and news coverage across multiple databases. This capability chains all of those data sources through the knowledge graph to produce a structured map: organizations on each side of an issue, the members they're connected to, the money flowing between them, and the lobbying firms doing the work.

How it works: Composite query that starts from a bill or policy topic and traverses lobbying, organizational, PAC, member, and stance relationships in the knowledge graph, grouping entities by position (support, oppose, neutral) and mapping financial and lobbying connections between them.

Data: Knowledge graph combining SOPR lobbying filings, FEC PAC contributions, press release stances, cosponsorship records, and news coverage. All data exists; requires composite query orchestration.

Map the full influence landscape around AI regulation - lobbyists, PACs, allies, opponents Try it → Who are the key players on both sides of the drug pricing debate? Try it → Show me the competitive landscape for organizations lobbying on trade policy Try it →

Coalition Building Intelligence

Recommendations for building legislative coalitions around a specific bill or policy objective. Combines network analysis (who works with whom via co-sponsorship alliance analysis), stance tracking (who supports what via position tracking), influence ranking (who matters most via member influence ranking), and broker detection (who bridges factions via legislative broker detection) to identify the most promising paths to building majority support.

Instead of guessing which undecided members to target, this capability uses graph analytics to rank outreach targets by: likelihood of support (based on sponsorship profile similarity), strategic position (bridge members who can bring allies), and influence weight (members whose support would signal to others). For lobbyists and advocacy organizations, this transforms whip count planning from intuition-based to data-driven.

How it works: Multi-objective optimization across network position, stance history, and influence metrics. The cosponsor prediction algorithm identifies likely supporters; the broker detection algorithm identifies strategic targets; the influence ranking prioritizes high-value outreach. Results are combined into a ranked outreach list with reasoning.

Data: All data exists in the knowledge graph across cosponsorship networks, stance records, PAC contributions, and centrality scores. Requires multi-signal scoring and optimization logic.

If I'm building support for a bipartisan tech bill, who should I target first? Try it → Which undecided members are most reachable on climate legislation? Try it → Map the optimal coalition path for passing the NDAA cyber operations amendment Try it →

Early Warning System (In Development)

Unified threat and opportunity scoring that monitors across all data sources for signals relevant to specific policy interests. Combines media surge detection (sudden coverage spikes), lobbying spike detection (new lobbying registrations), bill momentum scoring (accelerating legislation), stance shifts (position changes), and committee scheduling (upcoming hearings and markups) into a single prioritized alert feed.

Today, policy professionals monitor these signals manually across separate tools and information streams. An early warning system consolidates monitoring into a single view: "here are the signals this week that matter to your portfolio, ranked by urgency." A bill gaining three cosponsors, generating a lobbying spike, and appearing on next week's committee schedule is a higher-priority alert than any of those signals in isolation.

How it works: Composite scoring that runs media surge, lobbying spike, bill momentum, and schedule monitoring queries for user-defined policy areas, then ranks and prioritizes results by composite signal strength. A surge in one dimension gets a baseline alert; convergent signals across multiple dimensions get escalated priority.

Data: All monitoring data exists across news, lobbying, cosponsorship, and schedule sources. Requires user-level policy interest configuration and composite scoring logic.

Alert me to anything moving on healthcare policy this week Try it → What emerging threats should the renewable energy industry watch? Try it → Are there any early signals of legislative action on cryptocurrency? Try it →

Appropriations & Budget Intelligence

Dedicated intelligence for appropriations bills, spending levels, and earmark tracking - going beyond tracking appropriations as regular legislation to extract specific funding levels, compare proposed allocations across cycles, identify earmark patterns by member and district, and track the progress of spending bills through the complex multi-step appropriations process.

Five modes: status provides a fiscal year overview by jurisdiction, search runs semantic search within appropriations bills, compare shows House vs. Senate version differences, funding returns AI-extracted funding line items by agency and program with confidence scoring, and earmarks surfaces Community Development Spending (CDS) data from House and Senate disclosures with member matching and bill linking.

Appropriations bills are fundamentally different from authorizing legislation. They determine actual funding levels, move through a unique procedural process (12 separate appropriations bills, continuing resolutions, omnibus packages), and contain earmarks that direct spending to specific projects and districts. Standard bill tracking tools treat them like any other bill, missing the funding-level data and earmark details that matter most to organizations tracking federal spending.

Data: Congress.gov appropriations bill text, AI-extracted funding items, House CPF consolidated earmark disclosures, and Senate CDS DataTables.

What's the proposed funding level for NIH in the current appropriations bill? Try it → How do defense spending levels compare to last year's appropriations? Try it → Which members have requested earmarks for transportation projects? Try it → Show me earmarks requested by members of the Appropriations Committee Try it →

Ready to try these capabilities? Get started with Apogee or try the chat client.