About ScreenGeni.us
ScreenGeni.us helps media platforms make large catalogs easier for audiences to search and explore. Its engine runs inside a partner’s product and connects with its catalog, audience experience and revenue model. It searches by meaning across audio, text and images instead of relying only on keywords. People can refine results in natural language without restarting. A taste tool learns preferences through comparisons and can start before it has any behavior history.
ScreenGeni.us gives a Wilmington, Delaware address, and its product was built in Minnesota. Founder and chief executive officer Daniel Sieberg previously worked at Google, CBS News and ABC News. He is an Emmy-nominated journalist and two-time technology author. Its other cofounders have enterprise engineering, data and machine-learning search backgrounds. Their prior employers include HSBC, Dow Jones and Moody’s. The business carries commercial liability, errors and omissions, and cyber insurance through The Hartford.
Funding Rounds (SEC Form D)
| First sale | Filed with SEC | Amount sold | Investors | Avg. check | Terms | Filing |
|---|---|---|---|---|---|---|
| Jul 7, 2026 | Nov 9, 2026 | $85,000of $350K target | 2 | $42,500 | Simple Agreement for Future EquityRule 506(b) | Form D ↗ |
First sale is the date the first investor in the offering committed to invest. Companies must file Form D within 15 days of that date, so filing dates run later.
Why Investors Backed ScreenGeni.us
ScreenGeni.us sells AI discovery infrastructure that media companies embed in their products. It first targets publishing, streaming and audio. The same engine has also run on retail, resale and cultural collections. Because it can work with different AI models, the system can switch suppliers without being rebuilt.
The problem it targets
ScreenGeni.us argues that media catalogs are large but their discovery tools are weak. Publishers, streaming services and audio platforms already have audiences and ways to earn revenue. Yet their users struggle to find relevant material. As general-purpose AI models become cheaper and easier to swap, it expects value to move toward integration, routing and the interface that reaches consumers.
What the product does
Each deployment uses a map of concepts and relationships for its catalog, called an ontology. A data store for meaning-based search, called a vector store, backs every item in that map. The engine shares a map of user preferences, called a taste graph, across deployments. It also shares ways to organize catalog information and process different media formats. An evaluation system measures relevance against outcomes.
Progress so far
An 11-day public run processed more than 10,000 live consumer searches. Results arrived in under two seconds, with no outage, and Net Promoter Score improved. Seven external engagements are underway, alongside 15 active discussions and more than 20 inbound requests. That pipeline came with no spending on outbound marketing. The Minnesota Star Tribune is a paid customer, and its deployment was completed on time and within scope.
We wrote this summary from public statements by ScreenGeni.us and its investors. It is not investment advice.
Leadership Team
Executive officers and directors listed on ScreenGeni.us's Form D filed Nov 9, 2026.