The Frustration: So Much Data, Still So Many Unknowns.
You’re tracking lice counts. Treatments. Growth. Mortality. You’ve got dashboards full of numbers. And yet major decisions still feel like guesswork.
You’re not alone.
Many of the farms we work with say the same thing: "We’re collecting more data than ever, but it’s not helping us stay ahead." When mortality flags show up, it’s too late. When treatments don’t work as expected, you're back to trial and error. And when regional benchmarks don't match your own results, you're left wondering if the models even apply to you.
The problem isn’t your effort. It’s the isolation.
You don’t need more charts. You need clarity: what’s likely to happen next, what’s working under conditions like yours, and what to do about it.
That’s not what raw data provides. It’s what intelligence delivers.
But intelligence isn’t built from a single site. It’s not just your past cycles. It’s how your outcomes compare to hundreds of others facing similar water temperatures, stocking densities, and treatment windows. That’s how patterns emerge. That’s how the system learns.
Without comparison, your data has no context. And without context, there’s no prediction. Just reaction.
We’ve heard it time and again.
What exactly happens when you contribute your farm data to Manolin’s platform?
Here’s the answer:
We anonymize it. Before it enters any model, every identifier (site name, company name, region) is stripped.
We isolate analysis. Models don’t care who you are—they care about trends, sequences, results.
We never resell or share. Your data is never passed to third parties or competitors. Not now. Not ever.
We designed the system for skeptics—assuming every farm would want maximum protection and minimum exposure. That’s why our architecture separates identifiers from outcomes. That’s why model training is fully de-linked from source farms. And that’s why trust is built into the process, not bolted on afterward.
This isn’t theoretical. Farms contributing to the network have:
Adjusted treatment timing based on predicted lice resistance windows, improving results with fewer chemicals.
Identified disease risks before signs, based on early risk indicators picked up across regional farms.
Benchmarked growth and mortality trends against similar stocking densities - leading to stocking and harvest changes that directly improved yields.
When you contribute, you don’t just make the system smarter. You get insights that are already smarter because they reflect farms like yours.
Every cycle, every site, every unexpected outcome adds signal to the system. The more farms participate, the sharper the predictions.
Risk scores get more accurate.
Benchmarking becomes more relevant.
Forecasts match real-world conditions—not just ideal models.
You’re not uploading data into a void. You’re making the engine work better for your region, your conditions, and your fish.
You’ll find a data platform. But it won’t be fine-tuned to you.
Your risk predictions will reflect the general picture, not your site’s biology. Your benchmarking won’t include similar cycles. Your early warnings may come too late.
Meanwhile, other farms are feeding the system and getting the benefit. The models are being shaped by their cycles—not yours.
Holding back your data might feel safe. But in practice, it means you’re flying with less visibility.
You’re already putting in the effort - daily entries, treatment logs, mortality tracking. That data shouldn’t just sit in your records.
When you connect it to Manolin, you’re not giving anything up. You’re gaining a clearer, faster, more confident way to make decisions.
Because intelligence doesn’t happen in isolation. It happens when farms like yours help the system see more clearly, and get sharper results in return.
Want to see what your farm’s data could unlock? We’d be glad to walk you through it.