Selected work.

An enterprise software provider needed AI capabilities embedded across its product — not a bolted-on chatbot. Workflows spanned multiple services, data sources, and strict reliability requirements.

We designed and built AI-powered backend services and intelligent workflows: LLM orchestration, event-driven automation, and scalable APIs integrated with existing cloud infrastructure.

Production AI features shipped inside the core product — reliable under enterprise load, observable end to end, and extensible as models and requirements evolve.

Legacy enterprise systems constrained scalability and carried growing operational cost. The business could not tolerate downtime during any transition.

We led an incremental migration to AWS serverless architecture — Lambda, SQS, S3, API Gateway — with event-driven design and staged cutovers de-risking each step.

Improved scalability and operational efficiency, elastic cost profile, and a system the client's own team can extend — with zero downtime through migration.

Enterprise communication workflows ran on aging services that were fragile to deploy and expensive to maintain — slowing every team that depended on them.

We designed cloud-native backend services around the existing workflows, modernizing the architecture while preserving behavior the business relied on.

Measurably better reliability, faster and safer deployments, and a maintainable codebase — deployment velocity became a strength instead of a risk.

Engineering teams were losing time to manual processes, inconsistent environments, and slow release pipelines — delivery speed was capped by tooling, not talent.

We built internal engineering platforms, automation tooling, CI/CD pipelines, SDKs, and developer infrastructure — treating internal tools with product-level rigor.

Accelerated software delivery and higher engineering productivity — releases went from events to routine, and new services start from a paved road.