Platform Engineering
Trust Starts at the Platform Layer
As the 2025 Confluent Enablement Partner of the Year, we hold more certified data streaming engineers in the Americas than any other partner. That credential wasn't given to us. It was earned in production environments, for enterprise clients.
The Platform Decision Shapes Everything That Follows
AI initiatives, data pipelines, and application modernization all depend on what's underneath them. We build the platform layer first, so your AI scales, your data flows reliably, and your applications hold under enterprise load.
Cloud Platform (Hyperscalers) →
Azure, AWS, and Google Cloud are where enterprise platform decisions start. We design cloud architectures, migrate workloads, and build the infrastructure your AI, data, and application teams depend on, with advanced partnerships across all 3 hyperscalers.
Data & Analytics Platform →
Snowflake, Databricks, Confluent, and Microsoft Fabric aren't interchangeable. We help you choose the right combination for your workloads, build the lakehouse or warehouse architecture that fits, and operate it in production.
AI Platform & Infrastructure →
The models are only as good as the infrastructure running them. We build and operate the MLOps pipelines, agent orchestration frameworks, and AI runtime environments that keep your AI systems performing at enterprise scale, not just in a demo.
Integration & Application Platform →
Your applications, data systems, and AI tools only deliver value when they work together. We design the integration layer that connects them, with modern observability built in so you know when something breaks before your users do.
Platform Is the Glue That Makes AI, Data, and Applications Work Together
Most AI initiatives, data programs, and modernization projects get the headline work right and fail underneath it. Weak cloud infrastructure, poorly designed data platforms, and underpowered AI runtimes are where promising work stalls. We build the platform layer first, so everything above it has a foundation worth building on.
The right cloud platform is what makes AI deployments production-ready, not just proof-of-concept
The right data platform is what makes machine learning run on your proprietary data, not generic models
The right integration platform is what makes your applications communicate cleanly across the enterprise
Built on Earned Credentials, Delivered in Production
Every partnership on this page was validated through real delivery, not just a logo agreement.
74 Certified Kubernetes Engineers
51 CKA, 4 CKS, and 19 CKAD certified engineers across three specialization levels, built through real delivery engagements for enterprise clients around the world.
Co-Chairs of KubeCon India
Improving practitioners co-chaired KubeCon India in 2025 and upcoming in 2026, with multiple speakers across both editions and contributions to the CNCF Platform Engineering Maturity Model.
Building Enterprise Solutions on Microsoft Azure
Cloud infrastructure, AI, data, and application delivery
Microsoft Azure is the foundation for Improving's largest and longest-running enterprise engagements. From Azure AI and Azure OpenAI Service to Microsoft Fabric and Power Platform, we design and operate Azure environments that support AI, data, and application workloads at enterprise scale.
How do you design an Azure architecture that supports AI workloads at scale?
What does a Microsoft Fabric data platform look like for your organization?
How do you migrate to Azure without recreating the problems of your legacy environment?
Microsoft Azure Solutions Architect Expert — validated expertise in enterprise cloud architecture and AI-ready infrastructure design
AI Readiness Assessment
Cloud Strategy
Team Model Design
90-Day Roadmap
NCLH — Since 2019
40+ person team modernizing a reservation platform serving 2M+ passengers annually
Enterprise Delivery on Amazon Web Services
Cloud infrastructure, ML, DevOps, and data services
AWS is the platform behind some of Improving's most complex cloud-native and ML infrastructure engagements. From AWS Bedrock and SageMaker to enterprise DevOps and data pipelines, we design and operate AWS environments that hold at enterprise scale and meet the demands of regulated industries.
How do you build an ML infrastructure on AWS that operates reliably in production?
What does a cloud-native DevOps practice look like on AWS at enterprise scale?
How do you migrate legacy workloads to AWS without introducing new technical debt?
AWS Solutions Architect Professional — validated expertise in designing distributed systems and enterprise-scale cloud architecture on AWS
Data Architecture
Lakehouse Design
Governance Model
Streaming Pipelines
Berkshire Hathaway Energy — 60% Faster
Legacy-to-cloud migration on AWS; significant reduction in deployment time
AI and Data Delivery on Google Cloud
Vertex AI, BigQuery, and public sector delivery
Google Cloud is the platform behind Improving's AI and analytics work in healthcare and public sector. From Vertex AI and BigQuery to Healthcare API and Looker, we design and implement Google Cloud environments for organizations where data quality, AI reliability, and compliance are non-negotiable.
How do you implement AI search and summarization on healthcare data with Google Vertex AI?
What does a BigQuery data platform look like for an organization managing large clinical datasets?
How do you build on Google Cloud in regulated environments without slowing delivery?
Google Cloud Professional Data Engineer — validated expertise in designing and building data processing systems and ML models on Google Cloud
Custom AI/ML Models
Agentic MVP
MLOps Pipelines
AI/ML Deployment
PHSA — AI-Powered Patient Records
Next-generation semantic search and summarization for Provincial Health Services Authority of BC on Vertex AI
Platforms That Reach Production. And Hold
33 Minutes
Deployment time, down from 4 to 6 hours
29
Distribution centers deployed simultaneously
100%
DSCSA regulatory compliance met on deadline
Automated Deployment Across 29 Distribution Centers
McKesson's manual deployment process for their Drug Serialization Repository took four to six hours per location across 29 distribution centers, making DSCSA regulatory compliance nearly impossible to achieve on time. Improving automated the entire deployment pipeline using GitHub Actions, Kubernetes, Confluent Kafka, and MongoDB, reducing deployment time to 33 minutes per location and hitting the federal compliance deadline.
Our Practitioners Teach What They Build. Watch Them Do It
From cloud architecture to real-time streaming to AI infrastructure, the same practitioners building enterprise platforms for clients teach live sessions every week. Open to everyone. No registration wall.
Platform in Days, Not Weeks Diagramming to IaC With AI
Aman Juneja
Solutions Architect
From Cloud to OmniChannel Fullstack Modernization - Improving Talks Series
Josh Harrison
President, Improving Columbus
From Fragmented Knowledge to the Power of Clean Data
Juan Cruz Fortunatti
Solutions Architect
Ready to Move from Platform Initiative to Platform Impact?
Tell us where you are and we'll tell you exactly how we can help. No generic proposals, no sales pitch, just a direct conversation about your situation.
Michael Slater
VP of Technology
Brian van der Voort
VP of Consulting
Kevin Jourdain
Technical Director