Flov One 1.0
We're putting the finishing touches on Flov. Leave your email and be the first to try the model.
Built for real work. The decisions your team makes by hand every day — classify, prioritize, route, verify — written down as criteria and applied at scale.
Can't find your use case or want to know more? Talk to us and we'll define it together.
The layer that decides inside your own AI stack.
What context goes in, which model answers, what gets blocked. You define the criteria in plain text and get a typed, traceable decision at every step.
Your team's criteria applied to every change.
The rules that matter in a repo aren't syntax, they're conventions: what can be touched, what needs a test, which migration breaks a contract. Written in plain text and evaluated on every PR.
Structured reading of corpora, evidence and signal.
Turn corpora, interviews and scattered documentation into variables you can work with. Qualitative signal enters your models and forecasts as just another column.
Everything that comes through the door, typed on arrival.
Tickets, transcripts and documents arrive unstructured. Classifying them on the way in decides priority and route, and leaves only the ambiguous for human review.
What people write about work, turned into data.
Applications, engagement surveys, reviews and internal requests are free text nobody reads in full. The same criteria applied to everyone, with the evidence cited and reviewer bias out of the equation.
Intent detected before moving a single resource.
ICP fit, purchase intent, brand risk: every input is scored against your criteria and reaches the right team already prioritized.
Alerts and artifacts read the way your analyst would.
The SOC doesn't drown in events, it drowns in triage. Every alert, email or finding is read against your criteria and arrives prioritized, with the evidence and confidence made explicit.
Decisions the very moment the data arrives.
Streams, calls and live sessions don't wait for a nightly batch. Every event is classified on the fly, with low latency and the same typed decision as the rest of the system.
Written criteria, applied to every case file.
The policy is already written; the problem is applying it the same way to every file. Severity and confidence come out explicit, and anything doubtful is escalated with the evidence already extracted.
Your case isn't on the list? Describe the criteria in plain text and get a typed, traceable decision on your own data.
Accuracy across key areas against reference models. Hover each axis to see the detail.
Accuracy by academic category
Accuracy by academic category
Accuracy by academic category
Accuracy by academic category
Accuracy by academic category
Accuracy by academic category
Time to the full response, measured from the request, in the same region and with the same prompt for every model.
Median of 10,000 requests per model with a 1,000-token prompt · October 2026
jev-1.13
gpt-6-luna-decisions
microsoft-decision-1