Artificial intelligence models designed for the reality of Peruvian industry: computer vision, predictive maintenance and process analysis. No endless pilot projects.
Every project follows three well-defined phases with concrete deliverables. The client decides whether to continue at the end of each phase — no long-term commitments until value is proven.
We understand the production process, identify where a real problem solvable with AI exists, and validate the availability and quality of the necessary data.
We build a functional model on a real data subset, measure the result against the defined KPI, and present it to the client's technical and business team.
We integrate the model into the client's production environment, document the architecture, and transfer knowledge to the internal team for autonomous operation.
Capabilities we build to generate measurable value in industrial operations, production processes, and commercial management. We always start with the diagnosis, not the technology.
AI to automate the identification, categorization and standardization of tools, supplies and components in the workshop or warehouse. No manual searches or inconsistent labeling across departments.
Reduced supply search time and complete stock traceability from the moment of system entry.
Configuration of voice assistants that connect the different areas of a workshop — warehouse, production, administration — to check stock, log progress, generate alerts and access technical information without putting down tools.
Seamless cross-department communication without manual friction and automatic logging of operational activity in real time.
Identification of the process variables that most impact the final result (quality, yield, energy consumption). Correlations that are not visible with traditional analysis.
Quantified cause-effect map between process parameters and production KPIs — the foundation for continuous optimization.
Overlay of digital information onto the physical process for operator guidance, assembly control and real-time traceability. Our currently active research and development line — applied first in our own processes before bringing it to clients.
Reduction of assembly errors and automatic documentation of the production process without manual intervention.
Models that monitor supply consumption, project replenishment needs and generate alerts before shortages affect operations. Applicable to workshops, warehouses and production lines.
Elimination of stoppages due to material shortages and reduction of capital tied up in excess or misclassified stock.
Automation of management processes, sales pipeline analysis, customer segmentation and channel optimization. AI applied not just to production — also to how a company sells, serves and grows.
Greater visibility of the commercial cycle with actionable data for lead prioritization, retention and channel expansion.
We don't sell AI — we solve problems with AI. The difference is that we start by understanding the problem, not by proposing a solution.
A structured 60-minute conversation to map the process, identify the concrete problem and evaluate data availability. At the end you have clarity on whether AI makes sense for your case — even if you don't work with us.
If the diagnosis confirms feasibility, we present a technical proposal with the exact PoC scope, the required data, the success KPI, timeline and cost. No commitments for future phases.
Exploratory analysis of real data, cleaning, feature engineering and model architecture selection. The client has full access to this process.
Model training on real data, cross-validation, error analysis and tuning. Delivery of technical report with performance metrics and deployment recommendation.
Integration into the production environment, monitoring dashboard, complete technical documentation and team training. The goal is for the client to operate the system autonomously.
No. The initial diagnosis is free. If there is feasibility, the PoC proposal is designed to be a bounded project that demonstrates value before committing to larger investment. The starting point is understanding the problem — not signing a 6-month project.
It depends on the problem and the type of data available. In the diagnosis we evaluate exactly this: what data you have, its quality, what volume would be needed and whether there are alternatives when history is short. In some cases, transfer learning or data augmentation techniques allow working with smaller datasets than expected.
It is a real possibility and that is why each phase has an explicit decision point. If the PoC does not reach the defined KPI, we deliver an honest technical report explaining why and what would be needed to achieve it. The client is never obligated to continue if the results do not justify the investment.
In most industrial cases, models are trained exclusively on the client's internal data, which best reflects the specific operational reality. Data is never shared with third parties or used to train other models.
For the diagnosis and PoC, we only need access to the data and time from someone who knows the process (they don't need to be a data engineer). For production, ideally someone with basic Python or SQL knowledge for daily operations, though this is defined on a case-by-case basis.
The PoC delivers measurable results in 4 to 6 weeks from when we have access to the data. Full deployment to production takes between 3 and 5 months in total, depending on the complexity of integration with existing systems.
The 60-minute diagnosis is completely free. It is not a sales meeting — it is a structured technical conversation to understand your process and tell you honestly whether AI can help you.