Data & AI.
DATA Protection.
Active governance, observability, compliance, and guardrails ensure that only reliable data feeds AI and analytics.
PRACTICAL APPLICATION
Financial institution avoids R$ 2 million in regulatory fines through automated compliance and real-time auditable data trails.
Active Governance.
Lifecycle management, lineage, and automatic data qualification for AI and analytics.
Observability.
Continuous monitoring of data metrics with anomaly detection before any impact occurs.
Compliance.
Masking of sensitive data, automated audits, and on-demand regulatory reports.
Guard Rails.
Preventive barriers that block unqualified data before it reaches pipelines and models.
DATA Quality.
Data harmonization, data pipelines, strategic integration, and vectorization transform raw data into actionable insights.
PRACTICAL APPLICATION
Industry 4.0 reduces machine downtime by 40% by migrating from D+2 pipelines to real-time streaming with predictive volume alerts.
Harmonization.
Standardizes data across departments to ensure consistent metrics and decision-making.
Update.
Batch and streaming pipelines tailored to actual business needs.
Integration.
It connects systems with an end-to-end view, going beyond simple information exchanges.
Vectorization & RAG.
Convert PDFs, logs, and documents into structured data for AI and semantic search.
AIOps & Automation.
Failure prediction, self-healing, intelligent alert triage, and autonomous infrastructure optimization.
PRACTICAL APPLICATION
Telecom eliminates 92% of false positives and reduces MTTR by 75% with intelligent triage and auto-healing based on predictive models.
Predictive Shield.
Predice fallas con hasta 48 horas de antelación mediante el análisis de patrones históricos y datos del IoT.
Auto-Remediation.
Smart playbooks resolve up to 80% of incidents without human intervention.
Alert Triage.
Classifies and prioritizes critical alerts, eliminating up to 90% of false positives.
Resource Optimizer.
Expanded FinOps: Strategic recommendations and autonomous AI-driven auto-scaling.
MLOps.
Experimentation platform, model lifecycle, ethical governance, and resource scaling for AI in production.
PRACTICAL APPLICATION
Lawtech reduces prototyping time by 70% through a standardized environment, automatic versioning, and ethical validation pipelines.
Experiment Lab.
A collaborative environment for testing and validating ML prototypes with automatic versioning.
Model Lifecycle.
Monitor drift, bias, and performance in production with automatic retraining.
Experiment Governance.
Full traceability, ethical validation, and compliance for all deployments.
Resource Scaler.
Optimize GPUs, TPUs, and cloud resources with demand forecasting and intelligent auto-scaling.