Talk to film. Twenty sources, one honest score.
Choosing a film means opening four tabs and trusting none of them. Fi1m aggregates more than twenty review sources into a single blended score, writes its own reviews, and gives you a discovery interface you talk to: what you've seen, what you want to see, what you're in the mood for tonight. It's a platform for people and an MCP for their agents, so your assistant can use it too.
Three years of groundwork and a broken discovery experience
The Fi1m team had spent three years on the problem: review scores that disagree, aggregators that flatten them, recommendation engines that optimise for whatever a platform wants you to stream. Discovery had become a chore. The frontier report tracked two things arriving at once: models good enough to read and reconcile criticism at scale, and MCP as the way agents would consume services. Fi1m was the concept that used both.
A blended score, a critic of its own, and a conversation
Reviews from more than twenty sources, aggregated and weighted into a single blended score that shows its working. Fi1m's own reviews alongside them, so the platform has a voice rather than just an average. A discovery interface you talk to, which builds and maintains lists of what you've seen and what you'd like to see as you go. And an MCP server exposing the same catalogue and lists to agents, so “find us something for tonight” works from whatever assistant you already use.
Multi-model reading, deterministic scoring, agent-first API
Models read and normalise criticism from each source; the blending itself is deterministic and explainable, so a score can be traced back to who said what. A different model writes Fi1m's own reviews, and another handles conversation and list-keeping. The MCP server and the web interface sit on the same API, so agents and people get identical answers. It all runs on Edge infrastructure.
From four tabs to one question
Twenty thousand films indexed and scored in beta, with the full launch in October 2026 after an eight-month build. Picking a film is a sentence rather than a search, and the list of what you meant to watch finally lives somewhere that remembers it. For Fi1m, three years of thinking became a product with two front doors: one for people, one for their agents.
Where to watch, and who to watch with
Handed to Edge Expert Services to run. On the roadmap after launch: availability across streaming services folded into the answer, shared lists and group decisions for households that can never agree, and television. Labs stays on as R&D partner.
Built for people and their agents.
Fi1m is the first Labs product designed from day one to be consumed over MCP as much as through a browser. That's the pattern we expect most consumer products to follow, and it's why the frontier report keeps coming back to it.
- Client
- Fi1m, film.one
- Sector
- Media & entertainment · Discovery platform and MCP
- Status
- Beta, launching October 2026
- Build
- Three years of groundwork, eight-month build
- Labs' role
- Report → concept and spec → build → handover
- Now run by
- Edge Expert Services
- Stack
- Multi-model review ingestion · Explainable blended scoring · Conversational discovery · MCP server · Edge Compute, Storage and CDN
Give your product a second front door.
Your customers' agents are about to start using your service on their behalf. If you'd like that to work well, it's a Labs conversation.
Talk to Edge Labs