Data Engineering laying the foundation for AI/ML solutions

What we offer

1. Agentic GenAI workflows on AWS

   We build production workflows where the model does the work: it reads your systems, decides the next step, acts, and logs everything it did. Models run on Amazon Bedrock inside your VPC. Anthropic, Meta, Mistral or Amazon, whichever fits the task - connected to your tools through MCP.

2. Intelligent document processing

   End-to-end document automation solutions using custom-trained LLMs and Amazon Translate, Amazon Textract enabling automated data extraction, classification, translation and processing while maintaining security and compliance requirements.

3. Edge AI & computer vision

   We develop and deploy computer vision applications for manufacturing, retail, and security use cases, implementing efficient edge processing using AWS Greengrass, Nvidia Jetson and custom ML models that can operate in real-time with minimal latency.

4. GenAI enablement for engineering teams

   We rolled GenAI into our own SDLC across 70 engineers: coding, review, QA, documentation. We set up the same for your team - tooling, guardrails, and the metrics to prove it works.

5. Custom AI models for connected products

   Recommendation engines, predictive models and classification for IoT products, built on real device and usage data. Supervised learning, deep learning and reinforcement learning where the problem calls for it. SageMaker, Amazon Personalize, and custom models where off-the-shelf falls short.

6. Data platforms & MLOps

   We build the data layer your AI runs on: lakes, warehouses and pipelines on AWS and Google Cloud, from S3 and Redshift to BigQuery, Snowflake and dbt. Storage tiering is designed up front, so cost per terabyte falls as the platform grows. Models are deployed, monitored and retrained in production, not left to drift.