AI Strategy & GenAI Solutions
- Use cases scoped and evaluated against the Five-Factor Framework
- Matched to native platform AI, an independent specialist, or a custom agent
AI & Digital Transformation
Where the AI layer gets decided, not just deployed.
Strategy, GenAI solutions, and intelligent automation, built on an automation and machine learning practice that predates the current wave.
What we do
Four capabilities. One evaluation standard behind all of them.
Every use case starts with the work, the data, and the operating owner. The technology path follows from that evidence.
Intelligent Automation
- RPA extended with reasoning, not just task execution
- Bots that work through exceptions instead of stopping at them
Machine Learning & Data Science
- Applied ML for prediction, classification, and pattern recognition
- Built on the governed foundation from our Data & Analytics practice
Digital Experience & Security
- Customer-facing products and the architecture that protects them
- Brought in when an AI or automation initiative touches the customer
Where this shows up
Automation and intelligence, built for how the work actually happens.
The strongest proof is operational: fewer manual steps, a governed data path, and a workflow the business can still own.
Custom Packaging & Distribution · EPS
Request the case study
More than 80% less manual reporting effort.
An Azure analytics warehouse unified NetSuite and Salesforce data in one Power BI layer, replacing a reporting process built around manual exports.
How we work
Discover, evaluate, engineer, operate.
The site-wide delivery method, applied here specifically to AI and automation.
Scope the use case
Define the workflow, outcome, data boundary, risk, and accountable owner before choosing an AI path.
Benchmark the options
Compare native, independent, and custom options against real work rather than generic demonstrations.
Build governance in
Connect data, systems, human review gates, auditability, and rollback before production.
Run it with ownership
Monitor, improve, and extend the AI layer under the same managed services discipline.
The same method, run from this practice
Every use case clears the same five factors.
Capability alone is not enough. A recommendation must also fit the data boundary, security posture, economics, and long-term operating model.
Does it operate inside how the work is actually done?
Can it read and write inside the approved perimeter?
Does it meet access, audit, and residency controls?
What does production cost at real scale?
Who runs it after go-live, and under what SLA?
Start with the use case. We'll tell you which option fits it.
A focused discovery turns a broad ambition into a scored decision across your systems, data, security, economics, and operating ownership.