Assess company prediction signals

Before you begin

Your API key must include the fields and collections requested below. Check your API package if an endpoint or field is unavailable. Complete your first API request to set CRUNCHBASE_API_KEY in bash or zsh. Commands below include response headers; look for HTTP 200, then read the JSON body.

Primary APIs: Organization Search and Organization Lookup

Goal: Retrieve growth insights and prediction records for matching companies to support research. These signals do not forecast investment returns.

Before interpreting the results, review prediction scores and timing.

1. Find companies

Use search filters to identify companies matching your ideal customer profile (ICP), such as industry, geography, revenue range, financial benchmarks, and operating status.

Endpoint

POST https://api.crunchbase.com/v4/data/searches/organizations

API reference and guides

Fields used in this query

field_id	What this field does	operator_id (these are the compatible operators per field)
`last_funding_at`	Identifies the date of most recent Funding Round	`between`; `blank`; `eq`; `gte`; `lte`
`last_funding_type`	Identifies the last funding round type (e.g. Seed, Series A, Private Equity)	`blank`; `eq`; `includes`; `not_eq`; `not_includes`
`categories`	Identifies the types of organization (e.g. SaaS, Android, Cloud Computing, Medical Device)	`blank`; `includes`; `includes_all`; `not_includes`; `not_includes_all`
`growth_insight_direction`	Filters by the direction of the company's growth trajectory	`blank`; `eq`; `includes`; `not_eq`; `not_includes`

Scroll to view all columns →

Run the request

This call is narrowing down companies to Seed and Series A as those that were funded in 2026. The field_ids ask to return information around categories (i.e., industries), the date the company was last funded at and the type, current valuation and the Growth Insight that indicates whether the company is growing or not.

curl --include --request POST \
  --url 'https://api.crunchbase.com/v4/data/searches/organizations' \
  --header "X-cb-user-key: $CRUNCHBASE_API_KEY" \
  --header 'Accept: application/json' \
  --header 'Content-Type: application/json' \
  --data '{
  "field_ids": [
    "categories",
    "identifier",
    "last_funding_at",
    "last_funding_type",
    "valuation",
    "growth_insight_direction"
  ],
  "query": [
    {
      "field_id": "last_funding_at",
      "operator_id": "gte",
      "type": "predicate",
      "values": [
        "2026-01-01"
      ]
    },
    {
      "field_id": "last_funding_type",
      "operator_id": "includes",
      "type": "predicate",
      "values": [
        "seed",
        "series_a"
      ]
    },
    {
      "field_id": "growth_insight_direction",
      "operator_id": "eq",
      "type": "predicate",
      "values": [
        "c400_growing"
      ]
    }
  ],
  "limit": 3
}'

Example response

{
  "count": 191,
  "entities": [
    {
      "uuid": "31bb23c0-d028-43c0-a49c-16908a5867e8",
      "properties": {
        "identifier": {
          "permalink": "lmarena",
          "image_id": "64a0297694304c1b9ee644c9e0fca405",
          "uuid": "31bb23c0-d028-43c0-a49c-16908a5867e8",
          "entity_def_id": "organization",
          "value": "LMArena"
        },
        "valuation": {
          "value": 1700000000,
          "currency": "USD",
          "value_usd": 1700000000
        },
        "last_funding_at": "2026-01-06",
        "growth_insight_direction": "c400_growing",
        "last_funding_type": "series_a",
        "categories": [
          {
            "permalink": "artificial-intelligence",
            "image_id": "930c72a5a7b544999596ac1c0dc8b549",
            "uuid": "c4d8caf3-5fe7-359b-f9f2-2d708378e4ee",
            "entity_def_id": "category",
            "value": "Artificial Intelligence (AI)"
          },
          {
            "permalink": "information-services-f579",
            "image_id": "3561862d356f4b61ad312f13a43abe38",
            "uuid": "f579213a-60af-8bb3-309b-1d80607ad2a9",
            "entity_def_id": "category",
            "value": "Information Services"
          },
          {
            "permalink": "machine-learning",
            "image_id": "e83f580a280f48f39d789180bce8c2d9",
            "uuid": "5ea0cdb7-c9a6-47fc-50f8-c9b0fac04863",
            "entity_def_id": "category",
            "value": "Machine Learning"
          },
          {
            "permalink": "product-research",
            "image_id": "03a37b78c144455fa9b0a62b5c2a0f78",
            "uuid": "f213df87-6356-e51f-245a-cf9b7e788b13",
            "entity_def_id": "category",
            "value": "Product Research"
          }
        ]
      }
    }
  ]
}

2. Retrieve company prediction signals

Retrieve the selected organization’s growth insight and funding, IPO, and layoff prediction cards.

Endpoint

GET https://api.crunchbase.com/v4/data/entities/organizations/{entity_id}

API reference and guides

Retrieve predictions for a selected organization

The lookup uses LMArena as a concrete example. Replace lmarena with an organization UUID returned in Step 1 to investigate your own result.

curl --include --request GET \
  --url 'https://api.crunchbase.com/v4/data/entities/organizations/lmarena?field_ids=identifier,growth_insight_direction,growth_insight_confidence&card_ids=funding_predictions,layoff_predictions,ipo_predictions' \
  --header "X-cb-user-key: $CRUNCHBASE_API_KEY" \
  --header 'Accept: application/json'

Example response

Response examples below are historical, shortened JSON samples; search samples show one entity and card samples show at most one record per card. Other fields or records may be omitted. Counts and values change; count is the total matching records, while entities contains the returned page.

The historical response combines an organization growth insight with funding, IPO, and layoff prediction cards. Inspect each prediction’s generated_on and select the latest record before using its score. Overall probabilities have no fixed time horizon; interpret timing intervals separately. Supporting references provide context and do not establish which signals caused a model result.

{
  "properties": {
    "growth_insight_direction": "c400_growing",
    "identifier": {
      "uuid": "31bb23c0-d028-43c0-a49c-16908a5867e8",
      "value": "LMArena",
      "image_id": "64a0297694304c1b9ee644c9e0fca405",
      "permalink": "lmarena",
      "entity_def_id": "organization"
    }
  },
  "cards": {
    "funding_predictions": [
      {
        "generated_on": "2025-10-04",
        "identifier": {
          "uuid": "213a4da5-af74-4ca1-9b4b-746ebc23d7c6",
          "value": "Funding Prediction 2025-10-04",
          "image_id": "9936fca57e3145ccb3d9f8e15975538b",
          "entity_def_id": "funding_prediction"
        },
        "probability_score": 0.9039,
        "created_at": "2025-10-06T20:11:15Z",
        "partnership_announcement": {
          "uuid": "5ce714d9-fad5-4495-8761-20c95f0d9a9a",
          "image_id": "9936fca57e3145ccb3d9f8e15975538b",
          "entity_def_id": "partnership_announcement"
        },
        "updated_at": "2025-10-06T20:11:15Z",
        "probability_tier": "p200_positive_low",
        "probability_score_timeseries": {
          "months_24_plus": 1.077442552104743e-06,
          "months_00_to_05": 0.9999987543203556,
          "months_06_to_11": 1.059329221048746e-07,
          "months_12_to_24": 6.230417009239019e-08
        },
        "growth_insight": {
          "uuid": "5e308c44-f2eb-45e2-99b9-ec327b844167",
          "value": "Growth Insight 2025-07-14",
          "image_id": "75e16c7af7234747a3af2f4333c8bf33",
          "entity_def_id": "growth_insight"
        },
        "product_launch": {
          "uuid": "a6de206c-98cc-416a-98c8-6f30b0751f51",
          "image_id": "9936fca57e3145ccb3d9f8e15975538b",
          "entity_def_id": "product_launch"
        },
        "organization": {
          "uuid": "31bb23c0-d028-43c0-a49c-16908a5867e8",
          "value": "LMArena",
          "image_id": "64a0297694304c1b9ee644c9e0fca405",
          "permalink": "lmarena",
          "entity_def_id": "organization"
        }
      }
    ]
  }
}

Interpret the response

Evaluate each signal separately

Use the growth insight and the latest prediction from each requested card as separate research inputs. Review missing cards before concluding that a signal is absent.

For each returned prediction card, compare generated_on values and inspect the latest record’s score. Keep the organization’s growth insight separate from its funding, IPO, and layoff predictions when recording your findings.

Best practices

Treat predictions as decision-support signals, not guarantees
Combine prediction data with qualitative context to strengthen conviction
Interpret the returned timing intervals relative to generated_on; the overall probability score has no fixed horizon
Regularly compare predictions to actual outcomes to refine internal thresholds

Check your result

Check the organization UUID and prediction generation date before using a score. An empty result is not a zero probability. Interpret scores and timing using the prediction definitions.

Search requests here use limit: 3 for a small first page. For a complete result set, follow search pagination while preserving the filters and ordering. For request failures, use the shared request and retry guidance.