One four-phase method on every project, a reference architecture that deploys in days instead of being designed from scratch, and three ways to engage us depending on what you need.
Modular and cloud-agnostic. It adapts to what the organization already has instead of forcing a fresh start — which is why the first result lands in weeks.
| Layer | What it solves | Typical tools |
|---|---|---|
| Data sources | Where the information comes from: transactional, application and external | ERP · CRM · SAP · Oracle · APIs · files |
| Integration | Extraction, transformation and quality, without slowing the operation | AWS Glue · Azure Data Factory · GCP Dataflow · SAP Data Services |
| Data Lake / Warehouse | The single repository, in layers (Bronze, Silver, Gold) | Amazon S3 · Azure Blob · BigQuery · Snowflake · Databricks |
| Analytics and AI | Models, prediction and agents on governed data | Athena · Redshift ML · Vertex AI · Azure ML · Amazon Bedrock |
| Visualization | Where the business decides | Power BI · Amazon QuickSight · SAP Analytics Cloud · Qlik |
Governance is not one more layer at the end: it runs through all five. It is what lets you scale into artificial intelligence without losing control of who sees what.
With periodic checkpoints and agreed deliverables at each one. Regardless of project size or engagement model.
Prerequisites, access and kick-off. Scope is confirmed, environments are enabled and both sides agree on who works with whom.
Deliverable: work plan and verified access.
Requirements gathering, design and the business blueprint. We understand the business process before touching the data, and document the target architecture.
Deliverable: blueprint with design and architecture.
Construction, tool by tool. We implement what was designed, with partial validations so nobody reaches the end with a surprise.
Deliverable: the solution built in the development environment.
Integrated testing, deployment, historical loads, documentation and stabilization. It is not done when it works: it is done when it is in production, documented and stable.
Deliverable: solution live, documented and handed to the internal team.
The factory builds, the desk sustains and staffing integrates. Many clients combine two: Data Factory for the initial project and Analytics Desk to keep it running and growing.
When the scope is defined and there is a date: migrations, new platforms, an analytics model that has to get built.
A project managed by Scitis end to end, with agreed scope, schedule and deliverables, a team assigned by phase and a fixed price.
When the platform already exists and has to be sustained and grown, without building an internal team for it.
A managed functional and technical support service with contracted monthly capacity, ticket management, agreed prioritization and an accountable service manager. From one consultant to a full team.
When a specialized profile is missing and hiring would take months you do not have.
Senior consultants who join your team, under your management, your processes and your tools, full or part time.
Capacity is sized per client. We do not publish rates or block-of-hours sizes.
A structured conversation to understand what you need, what you have today and where the biggest return is. You leave with a recommendation of where to start and a scope estimate.
The full analysis: how the company generates data, on what platform, which sources and indicators exist, how mature each area is and what the target architecture should be.
The discovery call is free and commits you to nothing. It ends with a recommendation of where to start and which of the three models fits your case.
Book a discovery call