Key Capabilities
Deploy enterprise-ready AI models.
Build contextual RAG pipelines.
Automate document workflows and knowledge operations.
Monitor LLM performance and optimize cost.
Build multi-agent automation frameworks.
Integrate AI safely with compliance & governance.
Full-cycle GenAI migration from any third-party platform or custom models.
Case Studies

Media & Entertainment Company
Challenge: Manual curation of Reddit posts and newsletter creation was slow, inconsistent, and lacked contextual relevance, with no scalable process for daily automation or personalization
Solution: Developed a fully serverless content automation pipeline on AWS using Lambda for processing, Bedrock for summarization and tone alignment, S3 for storage, OpenSearch for indexing, SES for delivery, Step Functions for orchestration, and CloudWatch for monitoring
Impact: Reduced content creation time by over 90%, enabled consistent daily newsletters, increased click-through rates by 45%, and achieved a zero-ops, fully scalable architecture

Healthcare / MedTech Company
Challenge: Manual identification of surgical tools was time-consuming, inconsistent, and existing model accuracy was only 28%, limiting scalability
Solution: Implemented a GenAI-based computer vision pipeline using YOLO with automated data preprocessing, augmentation, and robust techniques such as background adjustments and occlusion removal
Impact: Increased accuracy from 28% to 65%, identified key data quality issues, and established a scalable framework for future improvements













