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Always pass website URL or UUID for company and not the display name The company parameter expects a full website URL (e.g. "https://canva.com") or an Akta UUID. A bare display name ("Canva") will not resolve correctly. If you only have a company name, use the Company Search API to look up the website URL or UUID first:
Always pass an industry code for industry and not the industry name The industry parameter expects one or more Akta industry codes (e.g. "IMAHAHAN"), not a free-text sector name like "Warehouse Automation". If you’re starting from a topic or sector name, resolve it to a code first with the Industry Search API:
If the query matches several closely related industries, consider passing more than one code (comma separated) to widen coverage rather than relying on the single top match. Use the open-ended query parameter for signals with no company or industry anchor company and industry are both optional. For themes, narratives, or commodities that don’t map to a single company or sector such as say a price move, a policy shift, a macro trend, pass a free-text query on its own instead of forcing it into an industry code:
Avoid combining a broad query with an unrelated company or industry filter. Since all active filters must match, an overly specific combination can silently return zero results. If you want to scope a topic to a specific company’s coverage, prefer combining query with company, not industry, unless the topic and industry genuinely overlap. Use entity_person_list and entity_event_list to track a person or event, not query If you want every article mentioning a specific named person or event such as an executive, founder, regulator, conflict, or policy rollout then use entity_person_list or entity_event_list rather than putting the name in query. The entity filters match against resolved, structured entities extracted from each article, so they’re more precise than a text search and won’t pick up unrelated articles that happen to share a keyword:
Common names can still match multiple people with the same name. If you see unrelated results, narrow further by adding company, industry, or countries to disambiguate. Use sentiment_list, type_list, and news_score_list All three filter parameters use the _list suffix. The forms sentiment, type, and news_score are not valid parameters and are silently ignored if passed. This is the most common integration mistake.
Understand what each filter does and combine them deliberately The filter parameters work independently and additively which means all active filters must match for an article to be returned:
  • sentiment_list : filters by the AI-assigned article sentiment (positive, negative, neutral)
  • type_list : filters by the event type tag (e.g. Equity Fund-Raising, Layoffs). Download the full list of type codes to see all accepted values
  • news_score_list : filters by relevance tier (High, Medium, Low). Use High for daily monitoring briefings where signal density matters; use all for research pulls where recall is more important than precision
Combining them narrows the result set aggressively. For example, sentiment_list=negative + news_score_list=High returns only high-relevance adverse articles which could be useful for portfolio risk monitoring but may miss lower-profile negative signals if used for comprehensive diligence:
Set start_date explicitly. The default lookback is capped at 6 months Without a start_date, the API defaults to 6 months ago for non-enterprise plans. For research and historical analysis on enterprise plans, always set the date range explicitly so the window is predictable and reproducible. Combine company and industry to scope multi-sector companies For companies that operate across multiple sectors (e.g. Amazon in retail, cloud, and logistics), passing an industry code alongside company narrows results to articles classified under that specific sector. This reduces noise from unrelated coverage. Resolve the code with the Industry Search API first:
Use full_text for LLM pipelines — fall back to ai_summary when empty full_text contains the complete article body and is the right input for embedding, RAG, and summarisation pipelines. For paywalled or restricted articles it may be empty — always fall back to ai_summary so your pipeline never receives blank content:
Use entities for structured cross-referencing, not string matching on full_text When you need to know which people, locations, products, or events an article touches on, read the entities.person, entities.location, entities.product, and entities.event arrays directly rather than pattern-matching against full_text or ai_summary. The entity arrays are already resolved and deduplicated, so they’re more reliable for building watchlists, alerting, or graph-style relationship tracking. Exclude low-value sources with blacklisted instead of filtering client-side If certain publisher domains consistently add noise to your pipeline (e.g. low-quality aggregators or syndication mirrors), pass them to blacklisted so they’re excluded server-side. This is more efficient than fetching and discarding articles after the fact, and it doesn’t count against your effective limit.