Delivered by leaders who have implemented AI, analytics and automation solutions across banking, financial services and global corporate services.
Ingest data and load into AWS. Ingest Module can be hosted on prem to push to S3. Alternately deploy it in AWS cloud
Build your data transformation pipeline using the rich drag and drop GUI and PySpark
Configure transformed output as data product that can be leveraged in other pipelines.
Deploy to different environments or create new versions at the click of a button. Let the tool handle all the operations in the backend.
Run your pipelines on serverless spark that auto scales based on load. Save on cost and improve efficiency
Organize teams around data domains/business units to manage the various sets of products.
AI Sutra product is a AWS hosted solution that is deployed into your Virtual Private Cloud (VPC) using Terraform/Cloudformation.
View and manage organization wide data mesh in the mesh view. Manage Scheduling of the pipeline runs
Track usage of the Spark server in EMS by each run and analyse at product level
Define your AI vision and build a prioritised roadmap tied directly to business outcomes — lower costs, faster operations, reduced risk. We help you cut through the hype and invest where AI delivers real returns.
Build enterprise-grade knowledge assistants that surface accurate, cited answers from your internal documents, policies and regulations. Graph-RAG architecture enables multi-hop reasoning across complex, interconnected knowledge domains.
Automate document-heavy workflows — from board minutes and contracts to invoices and compliance reports. Combining OCR, LLMs and human-in-the-loop validation to reduce manual effort by up to 80% while maintaining accuracy and audit trails.
Deploy AI agents that orchestrate multi-step business processes end-to-end — from data ingestion and enrichment through to approval workflows and downstream system updates — freeing your teams to focus on high-value work.
Design and deliver self-serve data products on a federated data mesh architecture — enabling business domains to own, publish and consume trusted data at scale, without creating centralised bottlenecks.
Establish responsible AI frameworks covering model explainability, bias detection, regulatory compliance and ongoing monitoring — ensuring your AI systems remain trustworthy, auditable and aligned with ethical and legal obligations.