99% of AI Projects Fail at Scale. We Help You Build the 1% that Succeed.
AI hit the datasphere like a storm. But scaling AI/ML and LLMs requires solid ground. We help you build your data foundation to support real progress.
stronger data foundation
from AI-ready data
with mature data architecture
What are the greatest risks and rewards of layering AI/ML into your infrastructure?
We worked with IDC to uncover the core elements companies need to get a headstart with AI data strategy. Here's what they said.



There's more to AI than meets the eye.
AI is a broad and constantly evolving collection of disciplines, including ML, LLMs, Agentic, and others.
We help you make thoughtful choices about the infrastructure, processes, and technologies underpinning your long-term data strategy, starting with the critical data foundations.
Identify high-impact use cases and create a tailored AI/ML roadmap aligned with business objectives. We apply product management principles to identify and develop those high-value cognitive computing use cases.
Once we've homed in on the best combination of practices, methodologies, and tools, we share our observations through a custom implementation plan. No matter if you're working with legacy infrastructure or you're born in the cloud.
Once you've identified high-impact use cases, and are ready to move forward with a roadmap, we help you architect your data foundations to support AI. This work ultimately makes AI into an "add-on" to your environment rather than a complex start-from-scratch endeavor.
We ensure your data is clean and structured to support scalable, high-performance AI/ML solutions in the long term.
Apply AI-based predictive analytics to forecast trends, understand customer behavior, and identify operational risks with accuracy.
From initial design to production launch, we anchor AI/ML-driven forecasts into your daily operations, whether you're using predictive for Sales and Marketing, Finance, Research and Development, or other critical business units.
We ensure your models stay accurate and deliver ongoing value.
Five years ago was the best time to get your data ready for AI. Today is the second best time.
We plug advanced capabilities into your current stack to boost what you already have, without disruption. We embed data governance into the engineering, with robust standards and controls to increase efficiency and lower risk.
What is AI and where can you apply it purposefully? Let's break it down.
Data is scattered, inconsistent, and unreliable, causing delays and blockages across the board, including in analytics and AI projects. We help unify and clean data by building trusted, governed foundations.
Many companies struggle to turn ML prototypes into trusted tools because of messy data, unclear ownership, complex integration, and missing monitoring. We help deliver reliable ML through strong data foundations and governance.
LLMs, and other GenAI tools, deliver impressive outputs but risk hallucinations, privacy breaches, and unpredictable costs without robust grounding and controls. We help secure and optimize your GenAI tools with governance, grounding, and cost management.
Agentic AI promised to automate complexity – like a chatbot that handles FAQs, or helps scientists conduct analyses – boosting efficiency by reducing human effort. But fragmented data and poor system links are the Achilles' heel of true agentic AI. We help implement safe, reliable agents with guardrails and controls.
Our take on the brave new world of cognitive computing solutions

Risk Management for the People Part of AI
Everyone's racing to adopt AI, but they're forgetting the drivers. Learn techniques to make sure your people are in the driver's seat.

Refresher: How to Avoid Common Pitfalls of ML Projects
A practical, scientific approach to selecting use cases, validating them with your data, and automating decisions with real-time signals.

Time Series Forecasting With Cross-Industry Examples
Explore statistical frameworks and advanced ML, with practical, hand's on examples to identify the optimal forecasting strategy.
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Margo L. Hershberger
Transformation & Information Management, Aerojet Rocketdyne

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Timothy Ringle
Business Systems Analyst

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Meghan Ezekiel
Director, Auxiliary Services

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Michael Leong
VP, Finance & Administration, Transcourt Inc.

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James Newsom
Sr. Director, Data Services, Jacuzzi

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Ana Pinheiro
VP Director, Data & Visualization, Takeda Pharmaceuticals

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Darlene Evans-Borinski
Sr. Director, Finance & Accounting, DNC Parks & Resorts at KSC, Inc.

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Chad Miller
VP, Finance, Hood Industries

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Sandra Grogg
Technical Product Manager, Dorman Products









