Fragmentation
Data scattered across isolated systems, spreadsheets and APIs. Each area sees only its own piece of the operation.
Gemminus Platforms · Data Platform
Having data is not enough. You need to trust it.
The Gemminus Data Platform integrates your sources, validates quality at the origin and delivers governed, traceable data: the foundation for analytics, Digital Twins and AI Agents that your operation can safely take into production.
- 100%source traceability
- Noplatform lock-in
- +10supported source types
- 2–4 wksto first ready data
AI projects rarely stall on the model. They stall on the data.
Before putting a predictive model or an agent into production, the operation must answer three questions: where does this data come from, is it correct, and can I audit it?
Low reliability
Without validation, inconsistencies contaminate reports and models. The later an error shows up, the more it costs to fix.
Continuous rework
Engineers and analysts spend their time preparing data, not creating value. Every AI project starts from scratch.
AI without grounding
Models and agents fed with unvalidated data amplify errors and make leadership lose confidence in the initiative.
The AI journey starts with data.
Each stage of AI maturity depends on the previous one. The Gemminus Data Platform delivers the foundation that supports them all, without rebuilding the infrastructure for every project.
Reliable data
Integrated sources, contracts and validation by layer. The foundation without which no result is auditable.
Analytics and KPIs
A Gold layer with ready metrics and features. Dashboards and indicators without manual preparation for every request.
Models and Digital Twins
Machine learning, simulation and optimization consume the same governed data instead of divergent copies.
AI Agents
Agents query and act on traceable, validated data through APIs and a catalog, within defined governance limits.
The platform prepares the foundation for agents; the design of each agent and its operating limits are defined case by case, with your team.
From raw data to ready data, with governance at every step.
A single, automatic pipeline takes data from the source to consumption, refining and validating at each step, like a refinery: raw material goes in, finished product comes out, with quality control in between.
Collection
Scheduled capture of files, SQL tables and APIs, with the raw data preserved and recorded from the origin.
Bronze
The data exactly as it came from the source, just organized. The preserved raw material: nothing is lost.
Silver
Clean, validated data: errors handled, nulls resolved and inconsistencies flagged. Here it is already reliable.
Gold
Data ready for dashboards, ML, AI and Digital Twins. Several data products come from the same Silver, without touching the source.
The data contract is the single source of truth.
Each dataset is governed by a declarative, versioned contract: it defines the format, the quality rules and the expected update interval. The same definition creates the structure and validates the data before loading. Whatever fails the contract is blocked before it reaches the dashboard or the model.
What changes with Gemminus.
Most data platforms were built to move and store information. Gemminus was built for something harder and more valuable: making sure you can trust it.
| Criterion | Most data platforms | Gemminus Data Platform |
|---|---|---|
| Quality | Move and store data, assuming it is correct. | Guarantee quality at the origin, with contracts that define what valid data is. |
| Validation | Quality is left to whoever consumes the data. | Validate every piece of data, at every layer, before releasing it. |
| Traceability | Hard to know where the data came from or whether it changed. | Origin, transformations and destination of every piece of data, end to end. |
| Delivery | Reports and BI as the end point. | Data as a product, ready for AI, analytics and Digital Twins. |
| Scale | Grow through rework and rewrites. | Modular scale, without rewriting and without lock-in. |
It is not another repository. It is the reliable foundation on which your decisions and AI models are built.
How Gemminus compares with the alternatives.
Teams evaluating a data foundation for AI usually compare different paths. Each solves part of the problem; Gemminus was designed to close the part that tends to be left out.
| Alternative | What it usually delivers | Where Gemminus makes the difference |
|---|---|---|
| BI and visualization platforms | Dashboards over data someone has already prepared. Reliability depends on whoever prepared it. | Sits under BI: delivers validated, governed data to dashboards, ML and agents. |
| Public cloud data lakes and lakehouses | Powerful storage and compute. Governance, contracts and quality must be built and maintained by the customer. | Brings data contracts, layer-by-layer validation and ready industrial standards, without requiring you to replace your stack. |
| Integration and ETL tools | Move data between systems. Business rules and quality end up scattered across jobs. | Quality rules declared per dataset, versioned and auditable, with end-to-end traceability. |
| In-house built pipelines | Solve the first case, but become scripts that are hard to maintain, document and scale. | A standardized, modular pipeline: each new source or plant reuses the same pipeline. |
| Generic data consultancies | Architecture and design, with little connection to the operational decision. | Data designed to support Digital Twins and optimization, by a team that builds them in industrial operations. |
Comparison by solution category, not by vendor. Gemminus can complement what you already have.
Why Enacom.
A data platform built by the team that builds Digital Twins and optimization models in industrial operations.
Data designed for the decision
The platform was born to feed models that decide, not just to store. Structure, quality and granularity start from the end use.
Industrial operations experience
Steel, energy, logistics and mining: environments where bad data costs output, deadlines and money.
Governance by contract
Declarative, versioned, auditable contracts, aligned with the open standard for data contracts (ODCS).
From the foundation to the Digital Twin
Data, optimization models and interfaces under the same responsibility, with no gap between who prepares the data and who uses it.
What changes for each area.
The same foundation serves those who decide, those who build and those who operate.
IT leadership and management
Governance and control without replacing the current stack: clear standards, traceability, predictable cloud cost and no lock-in.
Data and AI leaders
Less time preparing data and more time on models. Contracts and quality give the team a stable base to train, test and operate.
Operations and business
Indicators and decisions on a single version of the truth, with a known, auditable origin.
What your operation gains.
Less manual effort, more trust in data and more speed between information and decision.
Automated pipeline
No more manual scripts and rework: continuous ingestion, processing and delivery for every source.
Validated, traceable data
Decisions and models on a reliable base, with automatic quality rules and traceable origin.
Faster AI and analytics
Weeks of preparation saved per project: data arrives structured and ready.
Scale without rewriting
New sources, plants and use cases enter the same pipeline. The architecture grows with the operation.
Foundation for Digital Twins
A ready foundation for predictive models, agents and Digital Twins, without rebuilding for each project.
Cloud cost efficiency
Resources run on demand, with no idle infrastructure running around the clock.
In production, with numbers.
Results from Gemminus Lakehouse, the data foundation that supports Digital Twins in a steel operation.
- 1,114+time series managed under data contracts
- 97%+conformity with contracts in the Silver layer
- < 30 minmedian time for data to reach the Silver layer validated
- US$ 170–200monthly cloud cost, versus an estimated US$ 900–1,300 for an equivalent commercial platform
Case presented at the ABM Week 2026 Automation and IT Seminar. Results depend on the volume, sources and architecture of each project.
Transparency about what is already ready.
We separate what runs today from what the architecture is already prepared to receive. You set the pace, based on your project's needs.
Available today
Running in projects
- Scheduled collection of files, SQL tables and APIs
- Declarative data contracts per dataset
- Quality validation in Bronze, Silver and Gold
- Step-by-step logs and execution history
- Gold layer accessible through APIs and an internal catalog
- On-demand processing, with no idle infrastructure
Architecture prepared for
Enabled as the need arises
- Automatic alerts by email and Teams
- Real-time ingestion (streaming)
- Massive-scale distributed processing
- Deeper industrial integrations (OPC-UA, PLCs, MES)
Built for data-intensive operations.
Where data is abundant and the decision depends on trusting it.
Energy & Utilities
Market, generation and telemetry data for pricing, dispatch and asset management.
View industrySteel and Industry
Process, quality and production consolidated, with end-to-end production traceability.
View industryLogistics and Supply Chain
Tracking, inventory and demand in a single view, with ERP and WMS integration.
View industryCivil Construction
An expanding vertical: site, sales and supply chain data for schedule and cost models.
Start small. Scale without limits.
Modular approach: it starts with one plant or priority case and grows without starting over.
Diagnostic (2–3 weeks)
Mapping of sources and priorities, with a bottleneck report and an implementation plan.
Implementation (6–12 weeks)
Connectors configured and first flows validated, with Bronze and Silver data operational.
Go-live
First Gold data ready for AI, analytics and dashboards.
Expansion
New sources, plants and use cases on the same pipeline, continuously.
How to buy.
The Gemminus Data Platform is offered as a service (SaaS), after a customization stage for your operation.
Customization
Configuration of sources, data contracts and priority flows, in a project with defined scope and timeline.
Subscription
Use of the platform as a service, with licensing and ongoing support.
Evolution
New sources, plants and use cases enter the same pipeline, incrementally.
Commercial terms depend on scope and are presented after the diagnostic.
Frequently asked questions
Is the Gemminus Data Platform a BI tool?
No. It is the data layer that sits under BI and AI, making sure the information that reaches them is reliable.
Do I need to replace my current stack? Will I be locked in?
No. The approach is modular and lock-in free. Gemminus integrates with what you already have and grows incrementally, without rewriting.
I already have a data lake or lakehouse. Does Gemminus replace it?
Not necessarily. It adds data contracts, layer-by-layer validation and traceability on top of the base you already use. Feasibility is assessed in the diagnostic.
How do you guarantee data quality?
Each dataset has a contract that defines rules and quality. Data is validated at every layer before release, and a critical failure stops the pipeline instead of contaminating consumption.
How does this prepare the company for AI and AI Agents?
Agents and models are only as reliable as the data they consume. Gemminus delivers validated, traceable data accessible through APIs and a catalog, the foundation for putting AI into production with governance.
Is it streaming or batch?
Today collection is scheduled. The source may be real-time, but streaming ingestion is an evolution the architecture is prepared for.
How do you control cloud cost?
Resources stay off by default and processing is provisioned on demand: it starts, processes and shuts down.
How long until the first ready data?
Typically 2 to 4 weeks, starting with a priority source or use case.
Turn fragmented data into competitive advantage.
Start with a diagnostic of your data operation. We map your sources, point out the biggest bottlenecks and present a concrete plan.