01 / AI systems
Make intelligence operational.
Create AI capabilities that have a governed data foundation, a meaningful workflow, and a clear place in the operating model.
Talk about this projectThe problem
Most AI programs begin with a model. The work that makes them useful begins with the system around it.
AI and retrieval architecture
Internal AI delivery environments
Data integration and governance
Workflow-ready evaluation and adoption
Related portfolio case studies
Evidence that makes the path concrete.
Each case study retains its engagement-specific industry, reported outcome, technology context, and attributed quote from the supplied portfolio.
CONSUMER ELECTRONICS / ENTERPRISE MOBILITY
Custom AI API & RAG system for Apple-platform workflows
Engineered a secure, highly-performing Retrieval-Augmented Generation (RAG) framework directly integrated into iOS and macOS native workflows. The system allows enterprise users to query localized technical documentation and dynamic corporate knowledge repositories in real time while enforcing absolute data privacy and zero cloud leakage.
99.4% — Query accuracy across proprietary domains.
FINANCIAL SERVICES / ENTERPRISE IT
Internal enterprise AI platform built on Azure AI Foundry
Architected an enterprise-wide Azure AI Foundry hub designed to standardize custom model fine-tuning, prompt management, and automated security guardrails. The platform provides internal engineering teams with seamless access to generative AI capabilities while enforcing unified governance, PII filtering, and strict cost controls.
60% Faster — Deployment time for internal AI models.
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Bring one decision or workflow where the knowledge exists but the organization cannot reliably use it.
A short conversation can help you understand the systems involved, the options you have, and a practical place to start before you commit to a larger scope.
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