Representative examples of the work we do. Each engagement delivers working production systems—not just recommendations or slide decks.
Moving from scattered experiments to a centralized, governed AI capability that serves multiple teams and applications.
The organization had multiple teams experimenting with LLMs independently—different models, no governance, no shared infrastructure, and no path from prototype to production.
We designed and built a centralized AI platform with model serving, prompt management, evaluation pipelines, RAG infrastructure, and governance controls—deployed on Kubernetes with full observability.
Teams ship AI capabilities through a governed, reliable platform. Consistent model access, automated evaluation, and centralized cost tracking across all AI workloads.
Transforming how engineering teams build, test, deploy, and operate applications across multiple environments.
Slow environment provisioning, inconsistent deployment processes, fragile CI/CD pipelines, and limited observability. Developers waiting on ops for routine tasks.
A production Kubernetes platform with GitOps deployment, automated environment provisioning, CI/CD pipelines, developer self-service portal, and full observability stack (metrics, logs, traces).
Developers self-serve environments and deploy independently. Deployment frequency increased significantly. Reduced MTTR through observability and automated incident detection.
Building an intelligent knowledge retrieval system that connects LLMs to the organization's internal documentation and data.
Critical organizational knowledge scattered across wikis, documents, Slack, and email. Employees spending significant time searching for answers that existed somewhere in the organization.
A RAG system with document ingestion pipelines, vector storage, semantic search, context-aware retrieval, and an AI-powered chat interface—with access controls matching existing permissions.
Employees get accurate, sourced answers from organizational knowledge in seconds. The system respects document-level access controls and provides citations for every response.
Replacing manual cloud provisioning with automated, governed Infrastructure as Code across AWS and Azure.
Infrastructure provisioned manually through console clicks. No consistency between environments. Security drift undetected. Cloud costs growing without visibility or allocation.
Terraform-based IaC with modular architecture, CI/CD for infrastructure, policy-as-code compliance checks, cost allocation tagging, and drift detection—spanning both AWS and Azure environments.
All infrastructure defined in code, deployed through pipelines, and continuously validated against security and compliance policies. Cloud costs reduced through right-sizing and governance.