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Use the AI Baseline API to ask natural-language questions about S&P 500 SEC filings and receive grounded answers, metadata summaries, rendered dashboards, or evidence-only responses.

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Base URL

https://api-beta.ai-baseline.xyz/v2/sandp_500/
Every request must include your API key: 
-H "X-API-Key: $AI_BASELINE_API_KEY"

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Endpoints

EndpointPurpose
POST /queryQuery S&P 500 filing evidence and return the requested response sections.
GET /metadata-filtersList the metadata-filter fields and operators available for S&P 500 filings.

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Minimal Query

curl -X POST https://api-beta.ai-baseline.xyz/v2/sandp_500/query \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $AI_BASELINE_API_KEY" \
  -d '{
    "query": "What supply chain risks did Apple disclose in its 10-K filings?"
  }'
This request defaults to mode: "basic" and effort: "medium". It returns answer and summary by default.

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Response Shape

/query returns a response with a stable request_id and optional top-level sections:
SectionContains
answerGenerated answer grounded in retrieved filing evidence.
metadata_distributionMetadata distribution keyed by stable metric names, with readable display names inside each metric.
analysisGenerated analysis of the retrieved sources and metadata.
metadata_dashboardRendered ASCII summary of the metadata distribution.
evidenceGrounding text selected by include.evidence_format; rendered evidence includes a source-key footer, while XML evidence omits it.
summarySelected mode, effort, filters, warnings, and evidence-path count.
retrieval_planRetrieval query plan for agentic_research.

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Example Response Sections

Responses always include request_id. Other top-level sections are returned when enabled by include or by the selected query mode. The examples below are abbreviated to show their structure.

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request_id

{
  "request_id": "req_2xYk9mA3..."
}

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answer

{
  "answer": {
    "text": "Apple disclosed supply chain risks related to supplier concentration, manufacturing disruptions, logistics constraints, and component availability."
  }
}

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metadata_distribution

This abbreviated example shows two metrics. The ... entry marks omitted metrics and is not returned literally. 
{
  "metadata_distribution": {
    "evidence_paths": 8,
    "sec_ticker": {
      "display_name": "Ticker Symbols",
      "values": {
        "AAPL": 100.0
      }
    },
    "sec_form_type": {
      "display_name": "SEC Form Types",
      "values": {
        "10-K": 100.0
      }
    },
    "...": {
      "display_name": "Additional metric",
      "values": {}
    }
  }
}

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analysis

{
  "analysis": {
    "text": "The retrieved evidence is concentrated in Apple's 10-K risk-factor sections, with repeated references to third-party suppliers and manufacturing dependencies."
  }
}

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metadata_dashboard

The API returns metadata_dashboard as a string. Returned JSON field: 
{
  "metadata_dashboard": "\n```text\n          ### METADATA DISTRIBUTION ###\nEvidence Paths: 8\n\n              [ EVIDENCE STRENGTH ]\nHigh           ████████████░░░░░░░░  62.5%\n...\n```"
}
Same value, expanded: 
          ### METADATA DISTRIBUTION ###
Evidence Paths: 8

              [ EVIDENCE STRENGTH ]
High           ████████████░░░░░░░░  62.5%
Medium         ███████░░░░░░░░░░░░░  37.5%
Low            ░░░░░░░░░░░░░░░░░░░░   0.0%

                [ TICKER SYMBOLS ]
AAPL           ████████████████████ 100.0%

                [ SEC FORM TYPES ]
10-K           ████████████████████ 100.0%

                 [ FISCAL YEARS ]
FY-2025        ████████████████████ 100.0%

               [ FISCAL YEAR ENDS ]
09-27          ████████████████████ 100.0%

                [ SEC ITEM TAGS ]
10K-Item-1A    ████████████████████ 100.0%

                 [ GICS SECTORS ]
Information Te ████████████████████ 100.0%

             [ GICS SUB-INDUSTRIES ]
Technology Har ████████████████████ 100.0%

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evidence

The API returns evidence as a string. With evidence_format: "rendered", the formatted evidence includes a trailing <source_key> quick reference. relevance_score is a system-derived integer from 0 to 10 that indicates how closely the evidence chain matches the query. confidence_score reflects the system’s confidence in the specific fact captured by that entry. An entry can cite multiple sources: all cited sources support the shared <consensus> claim, while each source can contribute distinct <observed_epistemic_statuses> and <additional_context>. Returned JSON field: 
{
  "evidence": "<source_registry>\n  <source id=\"AAPL_10K_2025_ITEM-1A\" ... />\n</source_registry>\n\n<entity_glossary>\n  ...\n</entity_glossary>\n\n<evidence_chain relevance_score=\"9\">\n   ...\n</evidence_chain>\n\n<source_key>\n  ...\n</source_key>"
}
Same value, expanded: 
<source_registry>
  <source id="AAPL_10K_2025_ITEM-1A" type="10-K" date="2025-10-31" sec_fiscal_year="FY-2025" sec_fiscal_year_end="09-27" sec_form_type="10-K" sec_gics_sector="Information Technology" sec_gics_sub_industry="Technology Hardware, Storage &amp; Peripherals" sec_item_tag="10K-Item-1A" sec_ticker="AAPL" />
</source_registry>

<entity_glossary>
  <entity>
    <name>Apple Inc. manufacturing and logistical outsourcing risk</name>
    <description>Risk theme describing Apple Inc.'s dependency on third-party outsourcing partners for component manufacturing, product assembly, and logistical services.</description>
  </entity>
</entity_glossary>

<evidence_chain relevance_score="9">
   <entry confidence_score='High' sources='AAPL_10K_2025_ITEM-1A'>
      <observed_epistemic_statuses>
      * FACT [AAPL_10K_2025_ITEM-1A]
      </observed_epistemic_statuses>
      <consensus>
      Apple Inc. disclosed a dependency on outsourcing partners for component manufacturing, product assembly, and logistical services, often located outside of the U.S.
      </consensus>
      <additional_context>
      * Risk: significant uncertainties [AAPL_10K_2025_ITEM-1A]
      * Risk: reduced direct control over production and distribution [AAPL_10K_2025_ITEM-1A]
      </additional_context>
   </entry>
   <entry confidence_score='Medium' sources='AAPL_10K_2025_ITEM-1A'>
      <observed_epistemic_statuses>
      * PREDICTION [AAPL_10K_2025_ITEM-1A]
      </observed_epistemic_statuses>
      <consensus>
      Apple Inc. anticipates that future disruptions in component availability could impact operating results.
      </consensus>
      <additional_context>
      * Risk: inability to obtain components on commercially reasonable terms [AAPL_10K_2025_ITEM-1A]
      </additional_context>
   </entry>
</evidence_chain>

<source_key>
  AAPL_10K_2025_ITEM-1A: sec_fiscal_year=FY-2025, sec_fiscal_year_end=09-27, sec_form_type=10-K, sec_gics_sector=Information Technology, sec_gics_sub_industry=Technology Hardware, Storage &amp; Peripherals, sec_item_tag=10K-Item-1A, sec_ticker=AAPL
</source_key>

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summary

{
  "summary": {
    "domain": "sandp_500",
    "mode": "research",
    "effort": "medium",
    "submitted_metadata_filters": {
      "sec_ticker": "AAPL"
    },
    "applied_metadata_filters": {
      "sec_ticker": "AAPL",
      "sec_form_type": "10-K"
    },
    "evidence_count": 8,
    "warnings": []
  }
}

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retrieval_plan

{
  "retrieval_plan": {
    "queries": [
      {
        "query": "Apple 10-K Item 1A supply chain risks",
        "label": "Apple supply chain risks",
        "applied_metadata_filters": {
          "sec_ticker": "AAPL",
          "sec_form_type": "10-K"
        }
      }
    ]
  }
}
Start with the quickstart, then use the API reference for complete request and response schemas.