Model-agnostic by design
The engine treats foundation models as interchangeable suppliers, routed per task and swapped without re-architecture. When better models ship, our costs fall and our results improve.
Models generate intelligence.
ScreenGeni.us turns intelligence into discovery.
The right result. In under two seconds.
01 · The Last Mile
Foundation models are getting better, cheaper, and harder to tell apart.
That is a tailwind. As intelligence commoditizes, value moves to the layer that turns it into outcomes: integration, orchestration, and the surface a consumer actually touches.
ScreenGeni.us is the last mile between foundation AI and the media consumer.
02 · The Skeptic's Section
This is not a wrapper.
Everyone rents the same intelligence. What follows is what we own. Six points. Each one is architecture.
Retrieval is the product, not a plug-in
Semantic retrieval runs over a knowledge graph in which every node is backed by its own vector store, queried by meaning. We built that retrieval layer ourselves.
Domain ontologies, built per vertical
A film catalog, a food scene, and an editorial archive do not share a schema. Each deployment runs on an ontology built for its domain, which is why results read as native to the catalog.
Orchestration, integration, embedded UX
The engine ships inside the partner's product: their catalog, their surfaces, their monetization. The hard part is not the model call. It is everything around it.
Taste and context, tied to outcomes
Taste Intelligence profiles a user by contrast, sharp on day one with no behavior history. Every signal feeds an evaluation harness, so relevance is measured against outcomes.
Compounding data, workflow lock-in
Every deployment deepens the taste graph and the vertical ontologies that every future deployment inherits. Discovery data compounds, and the integration becomes part of the partner's workflow.
03 · The Engine
One engine, re-deployed per vertical.
Taste Intelligence
Learns a user's taste by contrast. Sharp on day one, before any behavior history. No cold start.
Semantic Search
Matches on meaning across audio, text, image, and structure. Not keywords.
Conversational Discovery
Refines and re-ranks in natural language without restarting the query.
Shared across every deployment · taste graph and vertical ontologies · metadata schema · multimodal stack · taste-profiling method · evaluation harness
04 · Proof
event · 11-day public run
response · sub-2-second
uptime · no outage
nps · improved
10,000+
live consumer searches
pipeline · earned, not bought
7 external engagements in motion
15 active discussions
20+ inbound requests
$0 outbound marketing spend
Minnesota Star Tribune is the named customer. A paid deployment, delivered on time and on scope.
01 · Now
MediaTech is where discovery is worth the most and works the worst.
Publishing, streaming, audio, FAST. Large, valuable catalogs with weak discovery. First-party audiences under clear engagement pressure. Ad, subscription, and affiliate monetization already in place. And a buyer who already exists: Product, Monetization, Digital.
02 · Moat
Every deployment makes the next one sharper.
The taste graph and vertical ontologies deepen with each partner. Discovery data accrues to the engine, not to the model. Integrations become workflow. And as foundation models improve, the engine gets cheaper and better at once.
03 · Later
The same engine, other catalogs.
It has already run retail, resale, and cultural collections. Media is the entry point. Discovery infrastructure is the company.
MediaTech is the beachhead. Discovery infrastructure is the company.
Contact
See it on your catalog.
The fastest way to evaluate the engine is a working demo on your own catalog. Tell us what your audience cannot find.