Enterprise AI, from strategy to production
Phoenix, AZ · Data & AI Platforms
Data platforms and ML for Phoenix companies, from equipment and yield data to servicing and fraud analytics, secure by design.
Data & AI Platforms in Phoenix
Phoenix has become a major semiconductor manufacturing hub, alongside aerospace and defence, large financial service operations centres, health systems and fast-growing logistics. Many of these run big operations teams where automation pays back quickly.
Where it fits
Predict tool failures before they stop production.
Plan labour and space with better forecasts.
Built for Arizona rules
Controlled technical data kept away from tools and people it may not reach.
Controlled unclassified information kept inside assessed environments, AI tools included.
Safeguards and a business associate agreement for anything touching patient data.
A written security program covering every system that handles customer financial data.
Security safeguards and prompt notice if personal data leaks.
Working together · Mountain Time
Your morning is our evening, so we meet live at the start of your day and build while you sleep.
How we work
We map your data sources, flows, owners and quality issues.
We design a platform that fits your cloud, scale and budget.
We deliver pipelines, models and dashboards in working increments.
We set up monitoring, alerts and governance your team can run.
Only if your policies allow it. We can build the platform on-site or in an approved private environment.
MLOps is the set of practices and tools for deploying, monitoring and updating machine learning models reliably, much as DevOps does for software. It keeps models accurate as data changes.
We work with the major cloud providers and with on-premise environments, and design the platform around the tools and contracts you already have.
Not always. We often build the data foundations a specific AI use case needs first, then extend the platform as more use cases follow.
Let's talk
We help you navigate Enterprise AI, from first use case to production.
Let's connect