The AI grounding layer for business
Most AI today is a great engine running on the wrong fuel: public web data is why AI makes things up. Bigdata.com replaces it with verified news, filings, transcripts, and expert interviews your tools can actually trust - structured, cited, and permissioned for research. Precision retrieval delivers only the relevant, cited, source-linked excerpts, fully auditable. One trusted data layer for every AI research agent.
170+ premium licensed sources
12M+ entities tracked and aligned
20+ years of history
The world’s leading business and financial content, ready for AI agents
Every insight in Bigdata is grounded in licensed, authoritative sources.
Monitor global narratives with comprehensive, real-time web data. 5 rolling years of history.
Access stories from Benzinga, Financial Times, MT Newswires, Risk.net, Al Jazeera, & more. Content since 2000.
Uncover insights from regulatory filings covering over 50 countries. Coverage since 2010.
Power your models with deep historical financials and estimates, including 30+ years of history.
Go direct to the source with corporate communications from global IR sites. Continuously updated.
Sell-side and independent research, including tier-1 brokers.
Access unique perspectives from leading industry specialists via Knowledge Ridge. Growing Weekly.
Analyze market commentary from influential finance shows, powered by Podchaser. Transcribed in real-time.
Gain an edge with jobs data, ESG scores, supply chain intelligence, and sentiment on all news. Continuously expanding.
Data partners
The tokenization of content
Your agents retrieve, license, and pay for premium content in the same unit AI is bought and sold: the token. Precision retrieval sends the model only the excerpts that carry the answer: cited, entity-resolved, and enriched through RavenPack’s 25-year knowledge graph.
Every answer is source-linked and audit-ready, so research teams can trace any claim back to the document that produced it.
The same query, run against whole documents versus only the cited excerpts:
Public web data can't be trusted for serious research.
Every team building an AI tool already has scraped web pages, LinkedIn data, and generic company profiles. That's not what's missing. What's missing is world-class journalism, filings, transcripts, podcasts and regional sources your competitors can't reach, all connected so your model actually understands what it's reading, not just matching words. To compound the issue, AI agents struggle to align company information across multiple data sources. That's what decides whether an AI answer is grounded or guessed.
“Every partner conversation eventually comes back to the same question: what happens when the model doesn't actually know something? Grounding the answer in real, sourced, traceable data is the whole game. That's the problem we built this platform to solve.”
One data layer for every job on your team.
Know who to sell to next
Score your current accounts, or find new buyers hiding in public filings. Get an account plan generated automatically before every call. A warehouse automation company runs this across roughly 20,000 accounts today, generating plans for its CEO, CFO, and sales team.
See market shifts before they're news
Scenario planning and competitor tracking built on real signals, not stale web summaries. A global automaker uses it for sales forecasting; a manufacturer uses it to track currency and shipping risk.
Know how you're actually being covered
Weekly briefs and earnings content for teams who need real coverage, not just headlines. Used for small-cap investor relations and peer benchmarking.
Vet your suppliers properly
Local pricing and background checks on suppliers, including small, private companies that don't show up in standard data. Built for supplier risk screening.
Screen for risk automatically
Due diligence and negative-news monitoring, built into your workflow.
Build it into your own product
Fact-check your AI's output, or power your own tools with real data instead of scraped web pages.
How Atom Group uses Bigdata.com to power precision IP scouting
Atom Group, a premier IP transfer and patent licensing platform, uses Bigdata.com as a specialized research analyst embedded in its technology-scouting workflows, massively reducing the time to build company profiles while grounding every finding in traceable source documents.
Works where your agents work
Natively connected to Claude, ChatGPT, and Microsoft Copilot, and connectable to any agent via MCP or API. No setup, no glue code.
Claude
Bigdata MCP connects directly to Claude, giving research analysts access to governed, cited financial intelligence inside the AI they already use: no switching context, no copy-paste sourcing.
ChatGPT
For teams using ChatGPT Enterprise, Bigdata's MCP layer replaces open-web retrieval with a permissioned, source-ranked intelligence feed, auditable and compliance-ready from day one.
SharePoint Copilot
Surface Bigdata intelligence inside Microsoft 365 workflows. Analysts querying Copilot in SharePoint or Teams get answers grounded in licensed financial content, not the public internet.
Pay for what you use
You pay only for what you actually use, from a fraction of a cent per query.
How does pricing work? Is it per seat or by usage?
By usage. You pay only for what your team actually consumes, starting from a fraction of a cent per query.
What sources are included?
Premium news, filings, earnings calls, podcasts, and expert interviews across 12M+ tracked entities and 20+ years of history.
How good is coverage for my region?
Strongest in global, English-language sources today, and expanding. If your work depends on a specific region or language, ask us before you start a trial. We'd rather tell you now than have it come up midway through.
Is there a free trial?
Yes. Start instantly, no paperwork, or book a working session if you'd rather have a guided evaluation for your team.
Does it work with the AI tools we already use?
Yes, Claude, ChatGPT, and Copilot are all supported. There's also a direct integration option if you're building your own tool.
Can we share this data with our own clients?
Yes. Your own summaries, scores, and analysis are yours to share. What you can't do is republish full articles word for word. Linking back to sources is encouraged.
How do you stop AI from making things up?
By grounding every answer in real, sourced content instead of raw scraped text, and by identifying companies and people precisely so your model isn't guessing who or what it's reading about.
Why do I sometimes see the same story twice?
That's intentional. It's actually a signal that a story is getting unusual attention. You can filter it down if you'd rather not see duplicates.
How is this different from just using an AI chatbot with web search?
Web search gives you what everyone else already has. This gives you the sources your competitors can't reach, properly identified and cited, so your model isn't guessing.
How do I keep costs under control?
A live usage dashboard shows exactly what you're spending, as you spend it.
Ground your next build in something real.
A short call to map your use case and data needs.
Book a working session
Developers
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