Aquaculture Data Intelligence

The Intelligence
for Aquaculture farms and suppliers.

Manolin accurately reconstructs fish data across systems, sites, splits, merges, and transfers. Farms use that context to investigate fish health risk. Suppliers use it to evaluate product performance in commercial conditions.

Explore Your Application See How Manolin Works

01 Population-traced data 02 Aquaculture-specific models 03 Operational and field evidence

Data Systems Status // Excellent Signal monitor // live

The data problem

Operations create data everywhere.

Production, feeding, health, environment, transport, and site operations all generate records. Each system captures a different part of the biological story.

Different systems hold different pieces of the same history.

As records leave their source systems, inconsistent identifiers, formats, and timestamps prevent them from arriving as one coherent history.

Reconstruction becomes the work before the work.

Teams spend time matching fragments, resolving gaps, and determining which events belong to the same fish population before analysis can begin.

Structure turns fragments into an analytical foundation.

When entity lineage and events are normalized together, the historical context needed for comparison and investigation becomes usable.

Then the investigation can actually start.

The questions farms and suppliers need to answer become much clearer when they begin from one structured history rather than disconnected records.

manolin // continuity tracefish-production data system

site + pen wellboat operations feed records environment health + treatment

site_24 / cage_08

boat_transfer / time

feed_lot / dosage

oxygen / temp / current

evt_treatment / mech

same history // different records

normalized fish historyinvestigation-ready

ENTITYgroup_17 → site_24 → cage_08linked

EVENTSfeed + treatment + transfer + healthaligned

CONTEXTenvironment + operations + generation retainedready

For farms // operational investigation

Why did treatment performance diverge between two sites?

What changed this generation compared with the last?

Which conditions shaped the mortality event?

For suppliers // field evidence

Did the product drive the outcome, or were other variables involved?

Where does the signal remain consistent?

What should R&D investigate next?

The data problem // 01

Operations create data everywhere.

Production, feeding, health, environment, transport, and site operations all generate records. Each system captures a different part of the biological story.

fish-production data sources

site_24 / cage_08 boat_transfer / time feed_lot / dosage oxygen / current treatment / mech

The data problem // 02

Different systems hold different pieces of the same history.

As records leave their source systems, inconsistent identifiers, formats, and timestamps prevent them from arriving as one coherent history.

record fragmentation

site_24 / cage_08 boat_transfer / time feed_lot / dosage oxygen / current treatment / mech

same history // different records

The data problem // 03

Reconstruction becomes the work before the work.

Teams spend time matching fragments, resolving gaps, and determining which events belong to the same fish population before analysis can begin.

manual reconstruction

site_24 / cage_08 → group_17 ?

treatment_event → source timestamp ?

feed_lot → exposure window ?

mortality → generation context ?

The data problem // 04

Structure turns fragments into an analytical foundation.

When entity lineage and events are normalized together, the historical context needed for comparison and investigation becomes usable.

normalized continuity

normalized fish historyentity + event model

ENTITYgroup_17 → site_24 → cage_08linked

EVENTtransfer → feed → treatment → observationaligned

TIMEsource timestamps reconciledordered

CONTEXThealth + environment + operations retainedready

The data problem // 05

Then the investigation can actually start.

The questions farms and suppliers need to answer become much clearer when they begin from one structured history rather than disconnected records.

investigation-ready history

normalized fish historyready

ENTITYgroup_17 → site_24 → cage_08linked

EVENTSfeed + treatment + transfer + healthaligned

CONTEXTenvironment + operations + generation retainedready

For farms // operational investigation

Why did treatment performance diverge between two sites?

What changed this generation compared with the last?

Which conditions shaped the mortality event?

For suppliers // field evidence

Did the product drive the outcome, or were other variables involved?

Where does the signal remain consistent?

What should R&D investigate next?

The shared intelligence layer

A coherent population record before any model runs.

Manolin’s differentiation starts underneath our software. The platform preserves the biological history behind the data, then applies aquaculture-specific analysis to questions that are otherwise difficult to answer reliably.

01 // Connect

Bring the systems together.

Integrate production, feeding, environmental, health, laboratory, and operational records without requiring a new hardware layer.

02 // Reconstruct

Preserve the fish history.

Link data to traceable populations rather than temporary cage locations as fish are split, merged, transferred, and moved.

03 // Contextualize

Compare the right baselines.

Evaluate performance with the conditions, interventions, and prior events that shaped the population outcome.

04 // Investigate

Move from signals to action.

Give farm and supplier teams a clearer path from a detected change to the next investigation, report, or decision.

Fish health Quality management Farm operations

Watershed // farm intelligence

Investigate fish health risk without rebuilding context across systems.

Watershed preserves the biological context behind farm data. Teams can see where risk is changing, compare treatment and mortality outcomes on traceable data, and pull the evidence behind reporting and audit questions faster.

Identify where fish health risk is changing Know how disease, lice, mortality, and welfare signals across sites are trending and where you're operations are most at risk.

Compare interventions against more coherent baselines Evaluate treatment and mortality outcomes across generations, transfers, splits, and merges with the prior context intact.

Prepare reporting and audit evidence from the same record Reduce hours of manual work required to reconstruct site, generation, and intervention histories for internal reporting and regulatory review.

Explore Watershed

Manolin // Watershed Population intelligence interface

Site map // area intelligence live

Female parasite pressure +18%

Disease models // selected site 03 active

Site Fjord-03 Current disease-model outlook

Moritella viscosa Low risk

Tenacibaculum High

PD Low risk

Population record updated 06:30

Sites monitored 18 4 regions

Active generations 42 population traced

Trace continuity 97.4% high confidence

Built around your existing systems

Connecting the data already running operations.

Manolin does not require a new hardware layer or a replacement for the systems your teams already use. It connects the records between them and reconstructs the data context behind the analysis.

Production systems

Biomass, transfers, splits, merges, site movements, and harvest records.

Feeding systems

Feed delivery, appetite, composition, growth, and production performance.

Fish health records

Mortality, disease, lice, welfare observations, and intervention histories.

Environmental data

Temperature, oxygen, salinity, weather, and regional pressure context.

Laboratory and veterinary data

Samples, diagnoses, field notes, and the clinical context behind decisions.

Product and operational records

Treatments, vaccines, feed products, genetics, and site-level activity.

Discuss your data environment

Frequently asked questions

Understand what's behind the platform.

These are the most common questions teams usually ask when they first evaluate the platform.

Our technology

What is Manolin?

Manolin is the platform for aquaculture data intelligence. It connects fragmented farm data, reconstructs the history behind each fish population, and gives teams a more coherent foundation for investigation, reporting, and product-performance analysis.

What does population tracing mean in aquaculture?

Fish populations move through splits, merges, transfers, changing sites, and changing conditions. Population tracing preserves the record behind those movements so teams can compare outcomes against the right biological history rather than only the current cage location.

Does Manolin replace our existing farm systems?

No. Manolin is designed to work with the systems already running the operation. It connects production, feeding, health, environmental, laboratory, and operational records into a population-level traced intelligence layer.

What is the difference between Watershed and Harpoon?

Watershed is the farm-intelligence application. It supports fish-health risk investigation, intervention comparison, reporting, and audit readiness. Harpoon is the supplier-intelligence application. It connects commercial farm data to product usage so R&D and product teams can evaluate observed performance and prioritize the next study.

How does Manolin handle farm and supplier data confidentiality?

Manolin structures analysis around data anonymization, controlled access, clear permissions, and the appropriate level of aggregation for the use case. Supplier analysis is designed to support evidence generation without exposing farm-level information outside the agreed scope.

How is Manolin different from dashboards, BI tools, or general AI assistants?

Dashboards and AI assistants can summarize the records they receive. Manolin adds the aquaculture-specific infrastructure required before that analysis begins: population tracing, data cohesion, domain context, and models built around the biological system.

For farms // Watershed

Investigate fish health risk with the fish history intact.

Connect fish records, compare outcomes, and prepare a clearer evidence trail for the questions your team needs to answer.

Explore Watershed

For suppliers // Harpoon

Build more defensible product evidence from the field.

Evaluate observed performance across comparable farm sites and identify the next question your team should investigate.

Explore Harpoon

Populations evaluated	1,248
Farm regions	12
Context variables	38
Follow-up studies	04

The Intelligence
for Aquaculture farms and suppliers.

Operations create data everywhere.

Different systems hold different pieces of the same history.

Reconstruction becomes the work before the work.

Structure turns fragments into an analytical foundation.

Then the investigation can actually start.

Why did treatment performance diverge between two sites?

Did the product drive the outcome, or were other variables involved?

Operations create data everywhere.

Different systems hold different pieces of the same history.

Reconstruction becomes the work before the work.

Structure turns fragments into an analytical foundation.

Then the investigation can actually start.

Why did treatment performance diverge between two sites?

Did the product drive the outcome, or were other variables involved?