[ Financial Intelligence for Hedge Funds ]

ACCESS THE BRAIN OF
[HEDGE FUNDSprogrammatically]

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Try:
[ Use Cases ]

Ask any research question.
Get sourced, structured answers.

CATALYSTS

“What are AAPL's upcoming hard catalysts?”

ACTIVIST

“What is the activist angle on NKE?”

MARGINS

“Why are EBITDA margins at HOG declining?”

8-K SCAN

“Summarize the last five 8-Ks for MSFT.”

BALANCE SHEET

“Show balance sheet trends for AAPL over 3 years.”

STRATEGY

“What is management's turnaround plan at NKE?”

POST /ask
{
"ticker": "NKE",
"question": "What is the activist angle?"
}
// Response: answer + sources[] with SEC URLs
[ Data Sources ]

Primary sources only.
Every answer is cited.

SEC EDGAR

8-K10-K10-Q

Full-text ingestion with source URL preservation. Every filing, every chunk, every answer cites the exact SEC document.

Financial Modeling Prep

Earnings CallsTranscripts

Complete earnings call transcripts with management remarks, analyst Q&A, and guidance discussions.

SEC XBRL · CompanyFacts

Structured Financials

Machine-readable balance sheets, income statements, and cash flows from the SEC XBRL companyfacts API.

Source Citation Example
Source: AAPL 10-K filed 2024-11-01, SEC EDGAR: https://www.sec.gov/Archives/edgar/data/320193/...
[ Catalyst Intelligence ]

Hard catalyst vs. soft catalyst.
Machine-tagged. LLM-explained.

Hard CatalystConcrete events with definable value path
mergerspin-offtender-offerCEO changebuybackdebt-financingstrategic-alternatives
eight_k_events response
"hard_catalyst": true,
"catalyst_confidence": 0.92,
"hard_catalyst_reason": "Board approved $50B buyback..."
Soft CatalystDirectional optimism, routine updates
customer-winpartnershipguidance-updateproduct-launchregulatory-approval
Full taxonomy
Corporate actions · Capital allocation · Governance · Operational · Special situations — 40+ event tags applied to every 8-K automatically.
[ Fundamental Extraction ]

10-K and 10-Q, fully extracted.
Every period. Every metric. Cited.

Business DescriptionUnit EconomicsSegmentsCompetitorsRisks SummaryManagement StrategyRevenue by YearEBITDA by PeriodMargin TrendsDebt ScheduleLiquidity AnalysisCapital Allocation

Every numeric value is tagged with its period, fiscal year, and source. XBRL data from SEC CompanyFacts API takes precedence; LLM extraction as fallback for narrative fields.

revenue_by_year item
{
"value": 383285000000,
"unit": "USD",
"period": "2023-09-30",
"fiscal_year": 2023,
"form": "10-K",
"source_url": "https://www.sec.gov/..."
}
[ Transcript Intelligence ]

Earnings calls, fully analyzed.
Activists. Margins. Management tone.

Recent News.recent_news
Company Priorities.company_priorities
Catalyst List.catalysts_list
Activist Case.activist
Analyst Skepticism.skepticism
Management Tone.management_tone
Margin Discussion.margin_discussion
Capital Allocation.capital_allocation
Turnaround Plan.turnaround_plan
Activist Case

The model constructs the activist case even when no named activist exists: operational underperformance, capital misallocation, governance issues, underutilized assets, and potential strategic actions an activist would demand.

Transcript Catalysts

Each catalyst gets a hard/soft classification, timeframe, confidence score, detailed analyst discussion, and a verbatim quote from the transcript as evidence.

[ Example API Calls ]

Simple REST API.
Production-ready responses.

Request
POST /ask
{
  "ticker": "NKE",
  "question": "What is the activist angle?"
}
Response
{
  "answer": "Nike faces significant activist pressure...",
  "sources": [
    {
      "source_type": "SEC filing",
      "form_type": "8-K",
      "filing_date": "2024-09-15",
      "source_url": "https://www.sec.gov/..."
    }
  ]
}
[ Get Started ]

Build your agent today.

Ingest any public company. Ask any question. Get structured intelligence with full source citations.

/api
Base URL
Service Role
Auth
JSON REST
Format
Self-hosted · RunPod inference · Supabase pgvector · Redis queue