Signal AI

aid: signal-ai
name: Signal AI
description: Signal AI (Signal Media Ltd) is an AI-powered reputation and risk intelligence platform that turns the world's
  largest real-time dataset of global news, social, broadcast and regulatory content into actionable insight. Its proprietary
  AIQ framework understands, enriches and surfaces relevant coverage across 226 markets and 120+ languages, ingesting 5.5M+
  articles and labelling 100M+ entities and topics daily. The Signal AI API is an HTTP+JSON API offering programmatic access
  to the platform via Content Search, Content Metrics, Affinity (the Signal AI Knowledge Graph), Events, and Risk Events endpoints,
  secured with OAuth2 client-credentials.
url: https://raw.githubusercontent.com/api-evangelist/signal-ai/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- battery-ventures
x-tier: stub
x-tier-reason: portfolio-lead
accessModel:
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  onboarding: self-serve
  trial: false
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  label: Self-serve signup
  confidence: medium
  source:
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  generated: '2026-07-22'
  method: derived
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-21'
tags:
- Company
- Media Intelligence
- Reputation Management
- Risk Intelligence
- News
- Content Search
- Knowledge Graph
- ESG
- Artificial Intelligence
- Analytics
image: https://login.signal-ai.com/auth/resources/hlcwe/login/signal/img/apple-touch-icon.png
apis:
- aid: signal-ai:signal-ai-affinity-api
  name: Signal AI Affinity API
  description: "The Affinity API endpoints allow API users to leverage the power of the Signal\nAI Knowledge Graph, derived\
    \ from billions of documents and updated regularly.\n\nThe Signal AI Knowledge Graph consists of:\n* **nodes** which represent\
    \ concepts such as entities and topics\n* **edges** represent connections describing relationships between these concepts\n\
    \nUsing the Affinity API, users can retrieve data for hundreds of thousands of\nentities and topics. The sole relationship\
    \ type currently accessible via the\nAffinity API is proximity.\n\n## Proximity\n\nProximity represents how closely entities\
    \ and topics are associated in the news\nand other types of content processed by Signal AI.\n\n### What is proximity?\n\
    \nThe proximity between an entity (e.g. Tesla) and a topic (e.g. Product recall) is a measure of how they are related\
    \ over a period of time (e.g. a certain month) as perceived in the news (almost all sources we ingest into the Signal\
    \ platform). It reflects the likelihood of the entity and topic in question being mentioned together, with 0 meaning the\
    \ two are very unlikely to appear together, and 1 meaning they are most likely to appear together (NB: the scale is not\
    \ linear.) We measure this by observing the mentions of the topic, the (salient) mentions of the entity and their (salient)\
    \ co-mentions. As opposed to relying only on the volume of co-mentions, the proximity score captures how significant these\
    \ co-mentions are based on the overall volume of coverage for both the entity and the topic. **For example, if the entity\
    \ has a high number of co-mentions with a topic, but the topic is very common (e.g Social Media), we may assign a low\
    \ proximity score. On the other hand if there are only a few co-mentions with a very niche topic, we may assign a high\
    \ proximity score.**\n\nThe proximity score is based on the normalized Google Distance (from Information distance theory)\
    \ and it has a value between 0 and 1:\n- A score of 0 means the two concepts are not related and never or rarely co-mentioned\
    \ together;\n- The higher the score is, the more related the concepts are and the higher the chance is of observing significant\
    \ co-mentions.\n- A proximity close to 1 means that if one of the concepts is mentioned in an article, it is most likely\
    \ that the other concept will also be mentioned in the article.\n\nTo illustrate how proximity is implemented, consider\
    \ this conceptual representation of the Signal AI Knowledge Graph below:\n\n![](/assets/img/affinity_example.svg)\n\n\
    In this graph, we see four nodes: two organizations (PepsiCo and American Chemical Society) and two topics (Food and Beverage\
    \ and Chemicals).\n\nWe also see three edges each representing a proximity relationship: (PepsiCo and Food and Beverage),\
    \ (PepsiCo and Chemicals) and (American Chemical Society and Chemicals). We can infer from these edges that PepsiCo has\
    \ a closer association to Food and Beverage than Chemicals. Also we can infer that American Chemical Society has a stronger\
    \ connection to Chemicals than PepsiCo.\n\n### Using the proximity score\n\nThe proximity score can be used in different\
    \ ways:\n\n#### 1. Discovery\n\nWe can identify the closest topics to an entity by ranking them according to their proximity\
    \ score. Note that if we simply use the volume of co-mentions to rank topics, we will end up with the common topics (which\
    \ might be considered noise).\n\nLikewise, we can do the opposite and identify the organizations (or any other type of\
    \ entities) that are closest to a certain topic. Note, if we simply use the volume of co-mentions to rank organizations,\
    \ we will end up with the common ones which are often mentioned in news articles, e.g. BBC, NASDAQ, UK, US\n\n\n#### 2.\
    \ Comparison\n\n- **Comparing Entities**: We can compare two entities on how they are related to certain topics, ie. which\
    \ one is more related. A higher proximity score means more relatedness to a topic. Note that this is different from comparing\
    \ them based on the volume (number of articles on topic). For example one big company may have way more articles on a\
    \ topic than a smaller company, but the smaller company may have a higher proximity. This is  because it has way fewer\
    \ mentions than the big company overall and a few articles on the topic contribute to a higher proximity score.\n- **Comparing\
    \ Topics**: Likewise, we can compare two topics on how they are related to certain entities. A higher proximity score\
    \ means more relatedness to an entity.\n- **Comparison over time**: We can say if the proximity between an entity and\
    \ topic has increased or decreased from one month to another by comparing their proximity score in those months.\n\nNB:\
    \ While the proximity scores are ordered, they are not linear. *This means you can't easily interpret the difference between\
    \ proximity scores i.e. the difference between 0 and 0.1 proximity is not the same as the difference between 0.4 and 0.5\
    \ proximity.*\n\n### Proximity metadata\n\nEach proximity relationship returned contains the following metadata:\n- `proximity-score`:\
    \ the proximity score in the range `[0..1]` as described above\n- `sentiment-score`: for a proximity relationship between\
    \ an entity and a topic, this score represents the sentiment towards the entity with regards to its relationship to the\
    \ topic. The sentiment score ranges between `[-1..1]` and is computed as follows (taking into account salient entity mentions\
    \ only:\n  ```\n  #(positive entity/topic co-mentions) - #(negative entity/topic co-mentions) /  #(entity/topic co-mentions)\n\
    \  ```\n\n### Using proximity\n\nUsing proximity relationships, one can uncover unknowns and insights on a large scale.\
    \ The API offers one endpoint to allow the users to query and explore proximity relationships.\n\nPlease note that only\
    \ the last 15 months of data can be queried."
  humanURL: https://signal-ai.com/solutions/api
  baseURL: https://api.signal-ai.com
  tags:
  - Affinity
  properties:
  - type: OpenAPI
    url: openapi/signal-ai-affinity-api-openapi.yml
  - type: Documentation
    url: https://api.signal-ai.com/docs
  - type: APIReference
    url: https://api.signal-ai.com/docs
- aid: signal-ai:signal-ai-categories-api
  name: Signal AI Categories API
  description: The Categories API from Signal AI — 2 operation(s) for categories.
  humanURL: https://signal-ai.com/solutions/api
  baseURL: https://api.signal-ai.com
  tags:
  - Categories
  properties:
  - type: OpenAPI
    url: openapi/signal-ai-categories-api-openapi.yml
  - type: Documentation
    url: https://api.signal-ai.com/docs
  - type: APIReference
    url: https://api.signal-ai.com/docs
- aid: signal-ai:signal-ai-content-metrics-api
  name: Signal AI Content Metrics API
  description: The Content Metrics API from Signal AI — 1 operation(s) for content metrics.
  humanURL: https://signal-ai.com/solutions/api
  baseURL: https://api.signal-ai.com
  tags:
  - Content Metrics
  properties:
  - type: OpenAPI
    url: openapi/signal-ai-content-metrics-api-openapi.yml
  - type: Documentation
    url: https://api.signal-ai.com/docs
  - type: APIReference
    url: https://api.signal-ai.com/docs
- aid: signal-ai:signal-ai-content-search-api
  name: Signal AI Content Search API
  description: The Content Search API from Signal AI — 2 operation(s) for content search.
  humanURL: https://signal-ai.com/solutions/api
  baseURL: https://api.signal-ai.com
  tags:
  - Content Search
  properties:
  - type: OpenAPI
    url: openapi/signal-ai-content-search-api-openapi.yml
  - type: Documentation
    url: https://api.signal-ai.com/docs
  - type: APIReference
    url: https://api.signal-ai.com/docs
- aid: signal-ai:signal-ai-entities-api
  name: Signal AI Entities API
  description: The Entities API from Signal AI — 2 operation(s) for entities.
  humanURL: https://signal-ai.com/solutions/api
  baseURL: https://api.signal-ai.com
  tags:
  - Entities
  properties:
  - type: OpenAPI
    url: openapi/signal-ai-entities-api-openapi.yml
  - type: Documentation
    url: https://api.signal-ai.com/docs
  - type: APIReference
    url: https://api.signal-ai.com/docs
- aid: signal-ai:signal-ai-events-api
  name: Signal AI Events API
  description: 'The Events API endpoint identifies significant clusters of news coverage about entities and topics of interest,
    allowing users to easily identify news events that could impact them or their business, and offering a qualitative deep
    dive into the quantitative data provided by Metrics and Affinity. By leveraging the 15-month archive, users can easily
    get up to speed on the recent developments involving specific companies, industries or topics.


    Examples of questions this data can answer include:


    * Have there been any events involving Company X in the past day/month/year/15 months?

    * What are the latest developments in the food and beverage industry?

    * Have there been any product recalls recently?


    The data returned includes entities and topics involved, their prominence scores, alongside the number of stories describing
    the event (a measure of "newsworthiness"), and a group of the most descriptive labels.


    The data is generated daily.


    ## Event hashes

    Each event contains a `hash` field. You can look up events by hash using the `/events/{hash}` endpoint. The difference
    between the data returned in this endpoint compared with `/events` is as follows:


    * All story ids for the event are returned not just the top 100

    * The source ids for the event are included


    In APIs it''s more common to use the term "id" rather than "hash". We''ve chosen to use "hash" because these strings can
    change as we receive new data. In the `/events` endpoint the `event-hash-expiry` field indicates when the hash will expire.
    Note that it could still be available after this time.'
  humanURL: https://signal-ai.com/solutions/api
  baseURL: https://api.signal-ai.com
  tags:
  - Events
  properties:
  - type: OpenAPI
    url: openapi/signal-ai-events-api-openapi.yml
  - type: Documentation
    url: https://api.signal-ai.com/docs
  - type: APIReference
    url: https://api.signal-ai.com/docs
- aid: signal-ai:signal-ai-openapi-json-api
  name: Signal AI Openapi.json API
  description: The Openapi.json API from Signal AI — 1 operation(s) for openapi.json.
  humanURL: https://signal-ai.com/solutions/api
  baseURL: https://api.signal-ai.com
  tags:
  - Openapi.json
  properties:
  - type: OpenAPI
    url: openapi/signal-ai-openapi-json-api-openapi.yml
  - type: Documentation
    url: https://api.signal-ai.com/docs
  - type: APIReference
    url: https://api.signal-ai.com/docs
- aid: signal-ai:signal-ai-publication-sources-api
  name: Signal AI Publication sources API
  description: The Publication sources API from Signal AI — 3 operation(s) for publication sources.
  humanURL: https://signal-ai.com/solutions/api
  baseURL: https://api.signal-ai.com
  tags:
  - Publication sources
  properties:
  - type: OpenAPI
    url: openapi/signal-ai-publication-sources-api-openapi.yml
  - type: Documentation
    url: https://api.signal-ai.com/docs
  - type: APIReference
    url: https://api.signal-ai.com/docs
- aid: signal-ai:signal-ai-risk-events-api
  name: Signal AI Risk Events API
  description: The Risk Events API from Signal AI — 3 operation(s) for risk events.
  humanURL: https://signal-ai.com/solutions/api
  baseURL: https://api.signal-ai.com
  tags:
  - Risk Events
  properties:
  - type: OpenAPI
    url: openapi/signal-ai-risk-events-api-openapi.yml
  - type: Documentation
    url: https://api.signal-ai.com/docs
  - type: APIReference
    url: https://api.signal-ai.com/docs
- aid: signal-ai:signal-ai-topics-api
  name: Signal AI Topics API
  description: The Topics API from Signal AI — 2 operation(s) for topics.
  humanURL: https://signal-ai.com/solutions/api
  baseURL: https://api.signal-ai.com
  tags:
  - Topics
  properties:
  - type: OpenAPI
    url: openapi/signal-ai-topics-api-openapi.yml
  - type: Documentation
    url: https://api.signal-ai.com/docs
  - type: APIReference
    url: https://api.signal-ai.com/docs
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
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