Machine learning that ships to production.
Production machine learning models, not just notebooks.
Overview
Placeholder overview: we build and deploy machine learning models — from training through monitored production use — integrated with the systems your team already runs on.
What's included
Key capabilities
Model development & training
Model deployment & serving
Ongoing monitoring & retraining
The impact
What this typically looks like
How we work
Our process
Our process: Discover → Design → Build → Support
- 1
Discover
Understand current systems, data and goals.
- 2
Design
Plan the target approach and architecture.
- 3
Build
Implement and integrate the solution.
- 4
Support
Validate, roll out and support adoption.
Tech stack
Tools & platforms
- Python
- TensorFlow
- Scikit-learn
- Azure
Proof
Related case studies
A unified analytics view across sales and operations
Reporting was spread across spreadsheets and disconnected tools, so leadership couldn't get a single, trusted view of performance.
40%
Reduction in manual reporting time
Connected financial reporting for a growing business
Finance closed the books using manual spreadsheet consolidation, which delayed reporting and introduced errors.
3x
Faster monthly reporting cycle
A single, trusted data warehouse for enterprise reporting
Reporting pulled from a dozen source systems with no shared definitions, so every team trusted a different number.
12
Source systems consolidated
Modernizing analytics with Microsoft Fabric
Legacy, siloed data warehouses were slow to query and expensive to maintain across multiple production sites.
50%
Faster dashboard load times
A modern lakehouse architecture replacing five legacy warehouses
Five regional data warehouses had grown independently over a decade, each with its own definitions, refresh schedules and access model — reconciling them for a single group-wide report took days.
5→1
Regional warehouses consolidated into one lakehouse
Migrating from on-premises SQL Server to a governed cloud warehouse
An aging on-premises SQL Server warehouse was expensive to maintain, slow under load and had no documented data lineage — every schema change risked breaking a downstream report.
100%
Legacy on-prem infrastructure decommissioned
A governed semantic model on Microsoft Fabric for enterprise-wide reporting
Every department had built its own Power BI measures for the same metrics, so a single term like "on-time delivery" meant something different in every report.
1
Certified semantic model replacing dozens of duplicated measure sets
Four connected dashboards giving a telecom provider one source of truth
Sales, network build-out, finance and field operations each tracked performance in separate spreadsheets, so leadership had no single, current view of the business.
4
Connected dashboards replacing dozens of spreadsheets
FAQ
Frequently asked questions
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