Complex systems
fail in ways
thresholds cannot detect.
Traditional models monitor isolated variables.
Yaripo models relationships, context and behavior in real time.
That is how we detect precursor patterns of critical events before they occur, generating action windows of up to 2 hours.
You don't react. You anticipate.
A real event.
Reproduced at full scale.
This is not a monitoring chart. It is a vector engine + AI that compares
the current state of a salmon farming center against 10 years of industrial memory,
identifying convergence patterns with HAB events (Harmful Algal Blooms).
Result: Early detection with an action window of ~2 hours.
ELIMINATING THE BLIND SPOT:
ANTICIPATING BIOMASS STRESS
BEFORE IT BECOMES MORTALITY
Simulation based on representative data from the Chilean salmon industry · Model built from documented real events · [D1] $1.2M: 120t × $9.86 USD/kg FOB (AQUABENCH/SalmonChile 2025) · [D2] HAB: SERNAPESCA · SalmonChile · INCAR-UdeC · CR2 · [D3] O₂: FAO / SERNAPESCA · [D4] ~2h: operational estimate (INCAR 2021) · [D5] 98.4%: synthetic demo value · Yaripo SpA · [email protected]
No necesita entender los vectores.
Solo necesita
las 2 horas
que Yaripo le
devuelve.
Estimated exposure based on Atlantic Salmon FOB price USD 9.86/kg (AQUABENCH/SalmonChile 2025). Reference HAB events: SERNAPESCA · SalmonChile · INCAR-UdeC · CR2-UChile.
The problem is that your operation produces critical signals that current systems cannot interpret.
What you saw in the demonstration is not a solution specific to aquaculture. It is the application of a general capability: detecting anomalies in high-dimensional spaces with non-linear dependencies. The same problem exists across four distinct industries.
What in aquaculture is an
O₂ drop
before a HAB event,
in mining is a
out-of-pattern vibration
before equipment failure.
In cybersecurity it is an
atypical network behavior
before a breach.
In banking it is an anomalous transaction pattern before
fraud
.
The pattern is the same:
the signal exists. Your system doesn't see it
.
Yaripo applies next-generation AI to build that space. It converts multivariate time series into vector embeddings, indexes them against operational history, and applies real-time similarity search. The result is the same across any industry: action windows before the event materializes.
Failures don't happen suddenly. They announce themselves in advance.
Critical equipment generates vibration, temperature, and pressure patterns that anticipate failures 40 to 180 minutes before the event. Current systems are reactive and only alert after damage has already occurred.
Sophisticated attacks that haven't materialized yet
Network logs, user behavior, and DNS traffic contain semantic patterns that precede successful attacks. Current systems detect what is known, not what is about to happen.
Quality doesn't degrade through one variable — it emerges from their interaction
Processes like digestion, bleaching, and drying generate multivariate deviations that cannot be explained by isolated thresholds. The problem isn't measuring more variables. It's understanding how they interact.
It's not the industry: It's failing to see the signals in time.
How the pipeline transforms data into actionable decisions
We don't describe it as technology. We describe it by the impact it generates on your operation at each stage.
We integrate your existing sources
without operational disruption
. Sensor data, SCADA, industrial systems, and APIs are ingested in their original format.
No migrations required
, no architecture redesign.
Result: unified access to your operation's complete signal.
Time series are transformed into
high-dimensional embeddings
. Each signal window becomes a point, where distance encodes
real operational similarity
, not linear correlation.
Result: your operation shifts from separate metrics to being understood as patterns.
Each state is compared against the complete operational history,
regardless of volume.
Years of data are available to identify similarities with prior events in milliseconds.
Result: the system recognizes patterns that have occurred before.
The system identifies anomalies, matches with historical events, and gradual shifts in operational behavior. It doesn't only detect errors. It detects when something is about to happen.
Result: contextual anticipation, not isolated alerts.
Not just another notification.
Each alert includes:
– Similarity with historical events
– Action window (~2 hours)
– Asset at risk
– Estimated economic impact
Result: prioritized decisions before the impact occurs.
Three capabilities that turn your data into operational decisions
We don't sell platforms. We build the Artificial Intelligence layer that makes your operational data answer questions you never knew you could ask. AI that lives in production, not in a lab.
Vector Intelligence
We transform your operational data into geometric representations that capture non-linear dependencies and hidden correlations . The resulting vector space is queryable in real time against years of history. The distance between points is no longer arbitrary — it is operationally meaningful .
Operational AI
Models that live inside the production process , not in a separate dashboard. Contextual anomaly detection (LOF) , identification of precursor events , and action prioritization based on estimated economic impact . All calibrated with your operation's real data.
Complex Systems Modeling
When signals are multivariate , events have multiple causes , and correlations shift over time, traditional models break down. Yaripo builds topological representations of your entire operating system that capture the real dynamics , not a linear approximation of them.
Four sectors.
One common problem.
The vector architecture adapts to the language of each industry. The data changes. The geometry of the problem does not.
Drilling rigs, haul trucks, and SAG mills accumulate mechanical degradation signals 40 to 180 minutes before a failure. Yaripo with SensorVec™ detects the vector signature of that precursor against the documented history of the equipment and similar equipment under comparable conditions.
HAB events exhibit environmental patterns documented in industry history. An O₂ drop of 0.4 mg/L in 22 minutes, combined with temperature and biomass, has a distinguishable vector signature ~2 hours before mortality begins. The interactive demo on this page reproduces that scenario.
Sophisticated attacks don't exactly match known threats, but their semantic signature — the geometry of their behaviors in vector space — does converge with documented historical attacks. Yaripo with SafeVec™ applies this logic to network logs and identifies threats before they materialize.
Variability in digestion and bleaching processes does not follow individual variable deviations — it emerges from the simultaneous interaction of dozens of correlated parameters. Process embeddings allow comparison of the current state against the history of optimal-quality production and detect divergences before they affect output.
Critical events are not exceptions.
They are the cost of operating without seeing in time.
These are not Yaripo cases. This is the real scale of the problem.
A microalgae bloom (Pseudochattonella) caused mortality of 40,000 tonnes of salmon.
In mining alone, unplanned downtime of critical equipment represents billions in annual losses.
83% of organizations in LATAM experienced more than one breach in 2022.
Millions of SCADA and OSIsoft PI data points are stored but never queried.
HAB events exhibit precursor environmental signals 12 to 72 hours before mass mortality.
High-dimensional models predict a significant share of quality variability in the Kraft process.
Built on
real production infrastructure.
Yaripo's industrial AI stack spans from the world's fastest vector search engine, capable of querying one billion embeddings in milliseconds, to production-grade open source libraries that don't lock your operation into any vendor. The right Artificial Intelligence tool for each problem. No lock-in. No resale commissions.
Yaripo is vendor-agnostic . Selection depends on your latency requirements, data volume, and existing infrastructure — not commercial agreements. We tell you what is the right component for your problem , not the one that pays a commission.
Identify the critical events you are not seeing today.
This is not a generic assessment. We work on your real architecture, identify precursor patterns in your history, and define implementation feasibility.
No commitment. No software pitch. Just an honest technical diagnostic.
To understand your operation and tailor the diagnostic.
Select the area where the problem impacts your operation.
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