How Infosys / HCL / Wipro delivers AI Platform Engineering
The offshore IT services model applied to AI platform engineering produces a specific failure: AI systems built by teams who are optimizing for cost-per-engineer rather than model performance in production. The distinction matters enormously when the system is deployed in a regulated environment.
AI platform engagements at offshore IT firms follow a pattern: a requirements gathering phase, a model selection phase, a development phase, and a testing phase that surfaces the integration problems between them. The engineers who trained the model are not the engineers who integrated it. The engineers who integrated it are not the engineers who tested it.
Regulated AI deployment — healthcare AI, financial AI, government AI — requires compliance architecture that most offshore teams are not qualified to deliver. NIST AI RMF, FDA Software as a Medical Device guidelines, EU AI Act requirements — these are not documentation exercises. They require engineers who have built AI systems under these constraints before.
How we deliver AI Platform Engineering
We build AI systems in regulated environments where the compliance architecture is designed alongside the model architecture, not after. ALICE enforces AI governance requirements at every deployment — model versioning, audit trail generation, drift monitoring.
Our AI platform teams include engineers who have deployed AI systems in clinical environments (FDA SaMD pathways), financial environments (model risk management under SR 11-7), and government environments (NIST AI RMF alignment).
Infosys / HCL / Wipro vs. The Algorithm
Where AI Platform Engineering matters most
Compliance-Native Architecture Guide
Design principles and a structured checklist for building software that is compliant by default — not compliant by retrofit. For teams building in regulated industries.