Dashboards & Reporting
Dashboards and reporting the business actually uses
Self-service analytics on AWS: dashboards with Amazon QuickSight and Amazon Q, KPIs defined with the business, automated reporting and predictive models where the case calls for it.
Decisions from data, not gut feel
The problem
- You have data but no insight: dashboards nobody looks at, or that don't answer the business questions.
- You want to predict (churn, demand, fraud, scoring) but you have no ML team.
- The models you tried never reached production: they stayed in a notebook.
- Every team asks you for reports by hand and your data team is stuck firefighting.
What's included
- Self-service dashboards and analytics with Amazon QuickSight and Amazon Q.
- ML models for prediction: churn, demand, fraud, scoring and more.
- MLOps: from notebook to production, with monitoring and retraining.
- Analytics embedded inside your own products.
- Metrics and KPIs defined with the business, not just with IT.
Get the model to production, not to a drawer
The value isn't in training a model: it's in operating it. We apply MLOps so your models reach production, get monitored and get retrained, with the same discipline we use to run infrastructure.
How we work
Discovery
We define with the business the questions to answer and the decisions we want data to enable.
Plan & Quote
We design the solution (analytics, model or both) and a scoped engagement at a clear price.
Execution
We build the dashboards or the model, validate them with real data and iterate.
Hand-off & MSP
We leave everything in production and monitored. We can operate and evolve it with you.
Stack & technologies
Self-service analytics · models in production
FAQ
Do I need a data science team for this?
No. We bring the ML and MLOps skills, and we train your team so they can maintain and evolve the solution.
What can ML predict?
Typical cases: customer churn, demand, fraud detection, risk scoring, predictive maintenance. We start from the case that moves your business.
What's the difference between analytics and generative AI?
Analytics and ML answer with numbers and predictions over your data. GenAI generates content and converses. Sometimes they combine: for example, Amazon Q to query your data in natural language.
How do you keep the model from going stale?
With MLOps: we monitor its performance in production and retrain it when the data shifts.
