AI venture funding data for investors, founders, and analysts.
1,494
Funding Rounds Tracked
1,158
AI Companies Profiled
1,749
Active Investors
19
Sectors Covered
AI Funding was founded with a single goal: to make AI venture funding data transparent, accessible, and actionable. The artificial intelligence industry is evolving at an unprecedented pace, with billions of dollars flowing into startups every quarter. Yet searchable data on these funding rounds has historically been fragmented across press releases, SEC filings, and gated databases. We believe that open access to funding intelligence empowers better decision-making across the entire ecosystem, from early-stage founders benchmarking their raises to institutional investors identifying emerging trends.
AI Funding tracks AI venture funding activity, including:
These answers explain how to interpret the public funding data, its sources, and its limits. They are designed to travel with the figures when researchers or answer engines cite the dataset.
AI Funding is a searchable public-data tracker for AI company funding rounds, investors, sectors, and reported valuations. Scheduled collection and verification runs update the dataset when public evidence is available, but the service is not real-time and is not complete.
The most reliable workflow starts with company and investor announcements, regulatory filings when available, and reputable reporting, then verifies the source URL attached to the funding record. No single tracker should be treated as the only source for a time-sensitive financing claim.
Accuracy depends on source quality, currency handling, entity resolution, and whether a valuation was actually disclosed. AI Funding retains the public source URL used for a record and applies automated data checks, but public funding data can be corrected, delayed, or withheld and is therefore not complete.
Compare disclosed valuations from similar dates and funding stages, keep currencies separate unless a dated exchange rate is available, and distinguish pre-money from post-money values. Missing or modelled figures should not be treated as reported valuations, and a later-stage valuation is not directly comparable with an early-stage price.
Recent seed and Series A rounds, first-time institutional investors, repeat participation, and increasing round sizes can help form a discovery list. These are research signals rather than proof of product quality; each company and funding source still needs to be reviewed.
Funding activity alone does not predict startup success. Operating measures such as revenue quality, retention, customer concentration, burn, technical performance, governance, and market adoption matter, and many are not consistently public. Funding data is useful for discovery and comparison, not as a success score.
Start with a defined sector and stage, filter rounds by date and size, compare investor participation, export the result, and verify material claims against their sources. Record missing data and conflicting definitions instead of filling gaps with estimates. This research workflow is not investment advice.
Compare source transparency, update cadence, correction policy, sector coverage, exports, and the depth of private-company financial and contact data. A free tracker can support discovery and source verification; paid products may add proprietary research, workflow tools, or private datasets.
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