We connect data from diverse sources into a single Platform, delivering a complete view of your context. Machine Learning applied to analysis, prescription and decision-making for the best management of your physical assets.
Industrial companies have plenty of data, but it is fragmented and lacks a shared vocabulary. The ERP knows one piece, the maintenance system another, SCADA another. Much tacit knowledge still lives in field teams' heads.
Integration connects sources. Datamint connects meanings.
Your data gains context
An operational knowledge map that connects your data around each asset with meaning, not just integration. The asset stops being a registry item and becomes a living entity in the system, with relationships, states and consequences.
AI applied where it hurts
Machine learning, computer vision and predictive models applied to real problems: failure risk, visual inspection, maintenance, compliance, cascading impact and prioritization.
From reaction to prediction
We move from reaction and periodic cycles to a predictive logic that anticipates risks before they become reality, with gradual autonomy and verifiable AI: data with determinable origin.
Datamint is not another dashboard, another sensor, or another maintenance system. It is the semantic and decision layer, underpinned by a semantic asset model, that takes shape in two complementary products: one to anticipate, one to audit.
The asset stops being a registry item and becomes a physical, operational, financial and normative entity. Your tacit knowledge becomes a computable knowledge graph.
The intelligence kernels combine machine learning, computer vision, rules and operational context to solve concrete problems, not generic models.
Weak signals become actionable alerts with explainable AI. Gradual autonomy: from suggesting actions to executing them safely and auditably.
Monitoring · Prediction · Decision
datamint predict · track · respond
Anticipate failures before they happen. Full visibility of each asset, from sensor to decision.
Governance · Traceability · Compliance
datamint audit · comply · trace
Every decision recorded, tracked and auditable — compliance without effort.
Modules that combine machine learning, computer vision, a map of meanings and operational rules to solve concrete problems, not generic AI models.
Computer vision identifies leaks, corrosion, PPE and valves. The camera becomes an operational sensor.
Computer VisionPredictive machine learning models estimate degradation with probable cause and feature importance.
Machine LearningThe knowledge graph shows what happens when an asset changes state and who is affected in sequence.
Knowledge GraphDecide by criticality, risk and productive impact, not by order of arrival.
Priority ScoringPart recommendations by real risk, not just historical consumption. Less urgency, less immobilized capital.
Risk-based InventoryThe norm leaves the PDF and becomes connected to the asset, operation and decision, with auditable traceability.
ComplianceVibration and temperature anomaly with probable impact on critical line.
The value is not just responding faster. It's preventing the problem from happening. Weak signals become actionable alerts via explainable AI. We cross your data to identify risks before failure and offer gradual autonomy — from suggesting actions to executing them safely and auditably.
Start with one clear problem, one asset class, or one unit. The foundation you build becomes reusable infrastructure for every next module.
Integrate sensors, SCADA, ERP, CMMS, cameras, manuals and maintenance history.
Build context: location, criticality, dependencies, procedures, risks and permissions.
Run AI models, computer vision, rules and decision kernels across your operations.
Prioritize maintenance, dispatch teams, log evidence and track execution.
The sector numbers show where the pain is. These three broad benchmarks show the order of magnitude of the problem in any asset-intensive operation.
An 8-hour shift can reach US$ 1M in operational losses.
Source: ABB Value of Reliability Survey, 2023.Estimated range for well-implemented predictive maintenance programs.
Source: U.S. Department of Energy · O&M Best Practices.Unplanned downtime equivalent to a significant share of annual revenue.
Source: Siemens · The True Cost of Downtime 2024.ROI comes not just from spending less on maintenance, but from producing more, stopping less, and making better use of invested capital. These are market reference benchmarks; real impact depends on maturity, criticality, and data from each operation.
Your operation does not need another sensor or dashboard. The problem is not a lack of data, it is a lack of context. We were born to close the gap between what your company already generates and what it can actually decide.
The difference between a demo and production is a deep understanding of your real operation, not promises. That's what we work on.
Direct answers for operations, maintenance, engineering, digital transformation and industrial leadership.
A semantic foundation (or semantic model) is a structure that defines what things mean within a system — not just what they are, but how they relate to each other and what they imply.
Datamint connects industrial data, sensors, systems, cameras, documents and physical assets to transform industrial operations into intelligent, explainable and auditable decisions.
Ontology is the formal model that describes what each thing means in your operation: what an asset is, how it relates to others, which signals indicate risk, which procedures apply, who is responsible for acting. It is the common vocabulary that makes ERP, SCADA, cameras, manuals and teams "speak the same language" about the same asset.
Why it matters: without ontology, data is merely connected but has no meaning. With ontology, decisions are contextualized, traceable and auditable. It is the difference between "having information" and "knowing what to do with it."
No. The platform acts as a semantic and decision layer on top of existing systems. It connects sources, understands operational meaning and guides action.
Yes. Datamint can consume telemetry, maintenance history, operational records and images already available. When new sensors make sense, they come in as another source.
No. There is assisted activation because industrial environments are complex, but the proposal is a product: platform, connectors, reference semantic models and reusable modules.
Autonomy is configurable. The platform can observe, explain, recommend, open orders or trigger workflows. Execution depends on permissions, criticality, procedure and client approval.
Start with a concrete pain: an asset class, a plant, a unit or a recurring failure type. The first module proves value and creates the foundation for expansion.
Tell us about the pain, the asset class or the unit. In 30 minutes, we show how Datamint's semantic layer applies to your operation and where the fastest gain is — no commitment.
You don't need to start big. We start with a clear pain and expand from results.
