Choose only the Data, Analytics / BI or AI area you need most right now

Data Audit

Fast X-ray of your database and pipelines

SQL, model and pipeline audit to detect bottlenecks, risks and clear opportunities for improvement.

Designed for a contained effort and a very specific scope.

• Review of key queries and table structure
• Performance and data risk map
• Report with 5-10 prioritized improvements

Data Control

Make your data platform stop causing surprises

We harden your platform with basic observability, alerts, quality controls and minimum documentation so operations feel predictable.

• Basic monitoring for loads and critical queries
• Data quality checks at key points
• Simple incident resolution playbook

Data Scale

From a platform that survives to a platform that drives growth

We redesign key model and pipeline components so they can support growth, new use cases and higher volume.

Ideal for a visible change in one or two key domains.

• Refactor of critical models and pipelines
• Design for new volumes and use cases
• Technical and functional documentation for your team

BI Audit

Do your dashboards tell the story the business needs?

Audit of semantic models, dashboards and metrics to identify what is used, what is noise and what is missing.

Designed for a contained effort and a very specific scope.

• Review of key dashboards and adoption
• Map of critical metrics and definitions
• List of quick wins for usability and trust

BI Control

Stable dashboards with no fights over numbers

We align the semantic layer, clean up metrics, and leave a core dashboard set the business can use every week.

• Single definition for key metrics
• Refresh and security configured correctly
• Basic enablement for business users

BI Scale

From reactive reporting to analytics that anticipates

We design a BI environment that supports ad-hoc analysis, business exploration and new dashboards without constant IT dependency.

Ideal for a visible change in one or two key domains.

• Modeling for data self-service
• Dashboard templates for different roles
• Best-practice guide for analysts

AI Audit

Separate noise from real AI opportunities

Review of AI ideas and use cases, required data and current maturity to identify what is worth testing.

Designed for a contained effort and a very specific scope.

• Inventory of internal ideas and use cases
• Data readiness and technical feasibility check
• Ranking of 3-5 cases with real potential

AI Control

AI use cases in production without surprises

We move from isolated experiments to controlled AI use cases: security, cost, response quality and accountability.

• Definition of goals and metrics per use case
• Security and compliance guardrails
• Basic usage and quality monitoring