Most company data APIs were built for sales reps clicking through a CRM, not for AI agents querying structured fields at scale. That gap matters more than it used to. An agent qualifying accounts or tracking buying signals needs clean, categorized, timestamped data it can reason over reliably, and the providers built around that use case look very different from the ones that aren’t.<\/p>\n
TLDR:<\/strong><\/p>\n A company intelligence API is a structured data interface that returns information about businesses in a machine readable format instead of raw web pages. Instead of parsing a wall of HTML and guessing at what changed, you query a company by domain or ID and get back JSON with clean fields: headcount, technology adoptions, job openings, funding rounds, and news events, each tagged with categories and timestamps. A program, a CRM, or an AI agent can act on that response right away.<\/p>\n This matters for AI sales agents reshaping B2B prospecting<\/a> because agents cannot reliably reason over messy, unstructured text at scale. An agent qualifying leads or drafting account research needs to know, with precision, whether a company closed a Series B last quarter or used job openings data for sales prospecting<\/a> to identify a director of sales role last week. Feed it a scraped blog post and hope it extracts the right fact, and you introduce errors, added latency, and inconsistent output every time the source page changes its layout.<\/p>\n Structured, source-backed data fixes that at the root. Every field maps to a fixed schema, so an agent can query “companies hiring for engineering roles in the last 30 days” and get a consistent answer no matter how the underlying job board formats its listings. Source transparency, where each data point links back to where it was found, also gives agents (and the humans reviewing their output) a way to verify a claim before acting on it. A raw web crawl cannot offer that on its own.<\/p>\n Below is the criteria we used, based on publicly available documentation and vendor claims, not hands-on testing.<\/p>\n Breadth of signal types.<\/strong> Does the API cover a single category, such as employee headcount, or does it span job openings, technographics, news events, financing, and business connections? A wider signal range means fewer integrations to build and more context for a single agent query, which is a core advantage when doing B2B data enrichment<\/a> across a CRM.<\/p>\n Structured data quality and normalization.<\/strong> Raw scraped text is not the same as normalized, categorized data. We looked at whether events, technologies, and job titles are tagged into consistent categories with confidence scores, or left as loose strings that a downstream system has to interpret on its own.<\/p>\n Delivery method flexibility.<\/strong> Some vendors offer only a REST API. Others support flat files for warehouse loads, webhooks for real-time triggers, and an MCP server for conversational agent access. The more delivery options available, the easier it is to fit the data into an existing workflow instead of building one around it.<\/p>\n Historical depth and timestamp transparency.<\/strong> An agent tracking change over time needs Self-serve accessibility versus sales-gated onboarding.<\/strong> Can a developer sign up, grab an API key, and start querying within minutes, or does access require a sales call and a contract. This affects how quickly a team can prototype an AI agent workflow before committing budget.<\/p>\n Weighting these five factors together produces a ranking that favors providers who treat structured, well-timestamped data as a product, over those who bolt an API onto an existing sales-led business. A vendor can score well on breadth alone and still fall short if its data lacks confidence scores or if timestamps only reflect the last update and not the actual first and last observation of a signal.<\/p>\n Best Overall Company Intelligence API: PredictLeads is a B2B data provider that turns public company activity into structured, source-backed datasets, delivered through a single API rather than a patchwork of scrapers and one-off feeds. It tracks 123 million or more companies globally, and instead of forcing you to stitch together separate vendors for hiring data, tech stack data, and funding data, it packages all of that into one schema an AI agent can query directly.<\/p>\n PredictLeads combines a native MCP server, 12 signal datasets, source-transparent technographics, and self-serve API access in a single integration: a strong foundation for AI agent workflows that need structured company data.<\/p>\n MixRank is a B2B data provider focused on technographic intelligence and company discovery, tracking which technologies companies use by crawling mobile apps, web properties, and ad networks. Its dataset spans millions of companies and is used primarily for competitive analysis, technology-based prospecting, and building target account lists segmented by tech stack.<\/p>\n Good for:<\/strong> Teams that need technographic segmentation as a primary filter for account targeting, particularly for identifying companies running specific web technologies or mobile stacks.<\/p>\n Limitation:<\/strong> MixRank’s signal coverage is narrower than providers with multi-dataset APIs: it does not offer structured News Events across 37 categories, source-transparent Technology Detections linked back to individual subpage or DNS record URLs, business connections data, website evolution tracking, or a native Model Context Protocol (MCP) server for AI agent access. Access requires a sales-gated onboarding process, which adds lead time before you can prototype a workflow. Historical depth and timestamp transparency at the record level are also not publicly documented.<\/p>\n Bottom line:<\/strong> MixRank is a reasonable starting point if technographic filtering is your only requirement and you do not need a broader signal set. If your workflow combines hiring data, news events, and tech stack signals in a single query, a multi-dataset provider gives you more coverage without building separate integrations.<\/p>\n Coresignal is a data-as-a-service provider offering public web data on companies, employees, and job postings through a suite of REST APIs. It aggregates and refines more than 4.5 billion data records covering 75 million or more companies, 865 million or more employee profiles, and 461 million or more job postings.<\/p>\n Good for:<\/strong> Teams that need broad employee profile coverage alongside company firmographics, or that want to filter across company, employee, and job records using a natural language search query rather than writing structured API filters from scratch.<\/p>\n Limitation:<\/strong> Coresignal covers company, employee, and job data but does not offer structured News Events across categorized event types, source-transparent Technology Detections linked back to individual subpage or DNS record URLs, Business Connections data, Website Evolution tracking, or a native Model Context Protocol (MCP) server for AI agent access. Bulk delivery and enterprise data tiers require a sales-gated onboarding process, which adds time before you can run a full workflow test. Historical depth and record-level timestamp transparency are not publicly documented.<\/p>\n Bottom line:<\/strong> Coresignal is a reasonable choice for teams that need wide coverage of people and job records alongside company firmographics, but its signal set stops short of the buying-signal categories and delivery options that AI agent workflows need for trigger-based account qualification or multi-dataset queries in a single call.<\/p>\n People Data Labs (PDL) is a B2B data provider focused on person and company records at scale, offering enrichment APIs that cover employee profiles, contact data, and firmographic attributes across hundreds of millions of records. Its primary value is breadth: PDL aggregates data from public sources, user contributions, and licensed datasets to deliver identity resolution and profile enrichment for people and companies in a single REST API.<\/p>\n Good for:<\/strong> Teams that need high-volume person and company enrichment, particularly identity resolution across email, name, and domain, or building contact lists where employee profile depth is the primary requirement.<\/p>\n Limitation:<\/strong> People Data Labs is focused on static firmographic and profile records, not time-series buying signals. It does not offer structured News Events across categorized event types, Technology Detections linked back to source URLs, Job Openings data with O*NET classifications, Business Connections data, Website Evolution tracking, or a native Model Context Protocol (MCP) server for AI agent access. For workflows that need to track what a company is doing right now, beyond what it looks like on paper, PDL’s dataset lacks the signal layer that drives trigger-based outreach or account qualification.<\/p>\n Bottom line:<\/strong> People Data Labs is a strong choice for enriching CRM records with firmographic and contact data at scale, but it is not built for the multi-signal, buying-intent workflows that AI agents and GTM teams need when they want to act on what is happening at a company today.<\/p>\n Crustdata is a real-time B2B data provider built for AI agents, delivering company and people data through live web crawling and event-subscription APIs. It provides a 1B+ people, 60M+ companies<\/a> real-time B2B data API to power sales, recruiting, and investment workflows.<\/p>\n Good for:<\/strong> AI agent builders and GTM tech teams that need real-time people and company signals, particularly job changes and funding events, with event-driven webhook delivery.<\/p>\n Limitation:<\/strong> Crustdata’s company dataset refreshes monthly for standard data, and its coverage of structured signals, such as categorized news events across 37 categories, source-transparent technographic detections across 54,000 or more technologies (see best BuiltWith alternatives<\/a> for a broader comparison), website evolution tracking, or business connections data, is narrower than PredictLeads. Crustdata also does not offer a native first-party MCP server endpoint or historical records going back to 2018.<\/p>\n Bottom line:<\/strong> Crustdata works well for AI agents that need real-time people signals and event subscriptions, but PredictLeads offers a deeper multi-signal dataset with a native hosted MCP server, longer historical depth, and structured buying-signal categories that go well beyond people and company profiles.<\/p>\n Sources<\/strong><\/p>\n Clearbit was a B2B data enrichment provider that HubSpot acquired in 2023 and rebranded as Breeze Intelligence, folding its company and contact enrichment capabilities directly into the HubSpot CRM platform. The product enriches company and contact records with firmographic attributes such as industry, employee count, annual revenue, and technology usage, drawing on a dataset covering hundreds of millions of contacts and tens of millions of companies. Access is now bundled into HubSpot’s paid tiers and is no longer sold as a standalone API, which means enrichment credits are consumed inside the HubSpot interface or through HubSpot’s own API, not through an independent endpoint a developer can call directly.<\/p>\n Good for:<\/strong> Teams already running HubSpot as their primary CRM who want enrichment to happen automatically inside their existing workflow without a separate vendor contract or API integration.<\/p>\n Limitation:<\/strong> Breeze Intelligence is tightly coupled to HubSpot, so it is not available as a standalone API for developers building external pipelines or AI agent workflows. It does not offer structured News Events across categorized event types, source-transparent Technology Detections linked back to individual subpage or DNS record URLs, Job Openings data with O*NET classifications, Business Connections data, Website Evolution tracking, historical records going back to 2018, or a native Model Context Protocol (MCP) server. Pricing is credit-based and gated behind HubSpot tier access, which adds friction for teams prototyping outside the HubSpot ecosystem. Onboarding is not self-serve in the traditional API sense: you need an active HubSpot subscription to use it.<\/p>\n Bottom line:<\/strong> Breeze Intelligence is a practical enrichment layer if HubSpot is already your system of record, but it is not a general-purpose company intelligence API. If your workflow requires querying structured buying signals, tech stack detections with source URLs, or multi-dataset lookups outside a CRM, you need a provider built around that use case.<\/p>\n The table below lines up each provider against the capabilities that matter most for AI agent workflows: does it run a native Model Context Protocol (MCP) server, does it structure event data, and does it offer enough delivery flexibility to plug into an agent pipeline without custom scraping.<\/p>\n\n
What Are Company Intelligence APIs?<\/h2>\n
How We Ranked These Company Intelligence APIs<\/h2>\n
first_seen_at<\/code> and last_seen_at<\/code> style fields beyond a current snapshot. We noted how far back each provider’s data goes and whether timestamps are exposed at the record level.<\/p>\nBest Overall Company Intelligence API: PredictLeads<\/h2>\n
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https:\/\/mcp.predictleads.com\/<\/code>, letting AI agents query datasets conversationally with no custom integration code, documented at docs.predictleads.com\/mcp_integration<\/a>.<\/li>\nMixRank<\/h2>\n
What They Offer<\/h3>\n
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Coresignal<\/h2>\n
What They Offer<\/h3>\n
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People Data Labs<\/h2>\n
What They Offer<\/h3>\n
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Crustdata<\/h2>\n
What They Offer<\/h3>\n
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Clearbit (Now HubSpot Breeze Intelligence)<\/h2>\n
What They Offer<\/h3>\n
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Feature Comparison Table of Company Intelligence APIs<\/h2>\n