Case Studies
Client names are withheld out of respect for confidentiality; the situations, decisions, and outcomes are described as they happened. Happy to talk through any of these in detail on a call.
A private destination-club and vacation-rental operator with no in-house technology leadership. The business ran on an aging Rails monolith, pricing data had degraded to the point that it was actively suppressing conversion, and the vendor portfolio had accumulated without review. Technology decisions were being made by whoever happened to be closest to the problem.
Pricing and availability inconsistent across distribution channels. No trustworthy reporting layer. Integration debt across three separate channel and property-management platforms. Critical system access concentrated in a small number of individuals with no documented custody.
Took the technology leadership seat reporting to the CEO. Built a modern data lakehouse with dashboards and automated data-quality monitoring, then designed and deployed pricing, data-quality, and conversion agents on AWS Bedrock — in production against live revenue data. Cleaned up channel and PMS integrations, rationalized vendors, and restructured access custody. Ran the modernization of the legacy platform alongside an IT operating-model transition.
A financial-sector client needed pricing that responded to market conditions in something closer to real time — without abandoning the governance and auditability their environment required.
Pricing decisions were slow, backward-looking, and disconnected from the feedback data that would have improved them. Any solution had to be defensible to risk and audit, which ruled out the fast-and-loose approaches on offer.
Designed a dynamic pricing platform on AWS combining serverless architecture, microservices, MLOps, and real-time feedback analytics — with model lifecycle governance built into the design rather than bolted on after.
A global energy major's downstream business was carrying decades of accumulated platform debt, including mainframe systems that remained in service largely because retiring them looked harder than paying for them.
An application portfolio no one could fully account for, overlapping capabilities across business units, and legacy platforms whose annual cost was invisible because it had always been there.
Directed legacy platform modernization and mainframe decommissioning, and designed the transition architecture that supported M&A integration. Rationalized the application portfolio against actual business capability rather than org-chart ownership.
The same downstream business needed analytics that spanned refining, logistics, sales, and finance — functions that had never shared a data foundation.
Operational data trapped in function-specific systems. Every cross-functional question required a manual reconciliation exercise, which meant most of them were never asked.
Deployed Palantir Foundry on AWS and led delivery of more than 100 analytics use cases across the downstream value chain, with automation replacing recurring manual work.
Two global packaging and paper manufacturers, each running dozens of plants assembled over decades of organic growth and acquisition. Neither could answer a basic question with confidence: what applications are we actually running, which ones do the same job, and what would it cost us to stop running the redundant ones?
Plant-level autonomy had produced parallel systems for the same business capability across sites. Acquisitions arrived carrying their own ERP and their own integrations, and were never fully absorbed. Ownership of the estate was distributed to the point that no single view of it existed.
Led enterprise architecture and application portfolio rationalization: built a capability-based map of the estate, identified genuine functional overlap as distinct from superficial similarity, and sequenced a retirement and consolidation roadmap against plant operating constraints. Established architecture governance that plant leadership could work with rather than route around.
A global services firm needed a cloud engineering practice that could both sell and deliver — across AWS, Azure, and GCP, for clients in several industries at once.
Cloud capability existed in pockets but had no coherent go-to-market, no capability model, and no consistent delivery approach across client engagements.
Led the cloud and enterprise architecture practices end to end: go-to-market strategy, capability development, and client delivery. Served as trusted advisor to client CTOs and CIOs on EA roadmaps, modernization, technical-debt reduction, and agile transformation.
Success patterns
In almost every engagement, the exciting initiative is blocked by an unglamorous data integrity problem. Fixing that first is what makes everything downstream possible — including AI.
The difference between an AI strategy and an AI capability is whether something is running against real data with governance around it. Everything else is a slide.
Vendor overlap, duplicate capability, and platforms nobody has reviewed in years routinely fund the modernization that follows. The savings are usually already in the building.
A plan without an executive owner reverts to whatever the loudest vendor suggests. Naming that owner is part of the Blueprint, not an afterthought.
In regulated environments, the fastest teams are the ones whose controls are designed in. Governance-by-exception is what slows delivery to a halt.
Key-person concentration and access custody almost never surface in a status report. They surface when someone deliberately goes looking.
The Production AI Blueprint applies the same approach to your systems and gives you a sequenced roadmap, with the governance and controls to execute it, that you own outright.