Noida, 201309,
India
MothersonSumi INfotech & Designs Limited (MIND)
MothersonSumi INfotech & Designs Limited (MIND), a prominent reseller, system integrator, and consulting company, that plays a vital role in numerous system integration and digital transformation initiatives. MothersonSumi INfotech & Designs Limited (MIND) collaboration with software players such as Amazon Web Services (AWS) empowers organizations to embrace disruptive technologies and accelerate their journey to the cloud, thus reshaping their business models.
| Reseller and SI | Vendor | Application | Category | Market |
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| MothersonSumi INfotech & Designs Limited (MIND) | Amazon Web Services (AWS) | Amazon CloudWatch RUM | Application Performance Management | ITSM |
| MothersonSumi INfotech & Designs Limited (MIND) | Amazon Web Services (AWS) | Amazon DynamoDB | Database Management | IaaS |
| MothersonSumi INfotech & Designs Limited (MIND) | Amazon Web Services (AWS) | Amazon Lambda | Apps Development | PaaS |
| MothersonSumi INfotech & Designs Limited (MIND) | Amazon Web Services (AWS) | Amazon S3 | Cloud Storage | IaaS |
| MothersonSumi INfotech & Designs Limited (MIND) | Amazon Web Services (AWS) | Amazon Textract | Intelligent Document Processing | Content Management |
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Product | Category | When | Insight |
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Max Healthcare Institute | Healthcare | 15000 | $548M | India | Amazon Web Services (AWS) | Amazon DynamoDB | Database Management | 2021 |
In 2021, Max Healthcare Institute implemented Amazon DynamoDB as a central data store within a serverless document processing pipeline to automate supplier invoice ingestion and extraction. MothersonSumi INfotech & Designs Limited, MIND, partnered on the engagement, addressing challenges including multiple invoice templates and high licensing costs that had previously driven a 29 person back office and accounts payable effort across Max Healthcare’s 16 facilities and related Max@Home and Max Labs operations.
The implementation used a serverless architecture built on Amazon S3, AWS Lambda, AWS Step Functions, Amazon Textract, Amazon A2I, Amazon API Gateway, and Amazon CloudWatch, with Amazon DynamoDB providing the Database Management backbone for extracted document data. The pipeline includes a daily scheduler to ingest scanned documents into S3, Step Functions orchestration per document, image pre processing to improve readability, Textract based form and raw data extraction, post processing for template based field validation, and an augmented human review loop via Amazon A2I for low confidence items.
Operational integration centered on feeding validated supplier and invoice data into Amazon DynamoDB, from where records are periodically extracted for entry into downstream ERP and payment systems. The scope of the deployment covered back office and accounts payable business functions across the hospital network, with document ingestion, extraction, validation, human review, and downstream reconciliation articulated as discrete functional workflows.
Governance and runbook controls were implemented through Step Functions orchestration and CloudWatch monitoring, with MIND leveraging its APN relationship for Amazon Textract to accelerate production readiness. The project delivered the stated outcome of achieving the required level of extraction accuracy with minimal operational overhead and faster time to production while centralizing extracted document state in Amazon DynamoDB for Database Management and downstream financial processing.
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Max Healthcare Institute | Healthcare | 15000 | $548M | India | Amazon Web Services (AWS) | Amazon S3 | Cloud Storage | 2021 |
In 2021, Max Healthcare Institute implemented Amazon S3 as the Cloud Storage anchor for a serverless document processing architecture to address high-volume supplier document intake across its 16 healthcare facilities and associated businesses. The deployment targeted back-office and accounts payable workflows that had engaged 29 resources for manual data entry, and it was scoped to support supplier invoicing processes that feed downstream payment systems across NCR Delhi, Haryana, Punjab, Uttarakhand, and Maharashtra.
The implementation centered on an orchestrated, serverless pipeline using Amazon S3 for ingestion, AWS Step Functions for per-document orchestration, AWS Lambda for compute, Amazon Textract for OCR extraction, and Amazon A2I for human review. Pre-processing image cleaning routines were applied to improve readability, Textract extracted both form and raw data for post-processing, and post-processing logic validated mandatory fields to raise extraction confidence before records moved downstream.
MothersonSumi INfotech & Designs Limited (MIND) led the implementation and used its APN collaboration on Amazon Textract to configure the solution. Ingestion is scheduler driven, scanned documents are placed into an Amazon S3 bucket which triggers the Step Functions workflow, and extracted data is persisted into Amazon DynamoDB. Processed records are periodically extracted from DynamoDB for ERP integration to enable on-time supplier payments, and Amazon CloudWatch is used for operational monitoring and observability.
Governance was implemented as a confidence based workflow, routing low confidence extractions into an augmented human loop via Amazon A2I for manual verification, while higher confidence items proceed automatically into the downstream system. The solution standardized template identification and data validation rules to reduce undifferentiated operational activity, and the serverless architecture minimized infrastructure management overhead during rollout.
The Amazon S3 centered Cloud Storage implementation delivered the required extraction accuracy with reduced operational overhead and faster time to production as reported by Max Healthcare, enabling more reliable supplier document handling and integration into accounts payable processes.
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Max Healthcare Institute | Healthcare | 15000 | $548M | India | Amazon Web Services (AWS) | Amazon Lambda | Apps Development | 2021 |
In 2021, Max Healthcare Institute implemented Amazon Lambda as the compute backbone of an Apps Development solution to automate supplier document OCR and downstream data flows for back-office accounts payable. The program targeted a high-volume intake environment where a 29-person team previously processed thousands of scanned supplier documents monthly across Max Healthcare’s 16 facilities in NCR Delhi, Haryana, Punjab, Uttarakhand, and Maharashtra. The initiative is scoped to supplier invoice and document handling that feeds on-time supplier payments and downstream ERP processing.
The deployment is architected as a serverless pipeline centered on AWS Lambda functions orchestrated by AWS Step Functions, with Amazon S3 used for ingestion and Amazon Textract used for document extraction. Pre-processing image cleaning routines are executed before Textract runs, both form and raw extraction outputs are captured, and post-processing logic validates and normalizes extracted fields. Amazon Lambda hosts the processing logic and triggers, Amazon API Gateway exposes any programmatic endpoints, and Amazon CloudWatch provides observability and operational logging.
Human review is integrated through Amazon A2I to surface low confidence extractions into an augmented human loop, while validated outputs are persisted into Amazon DynamoDB. Documents uploaded to S3 trigger step function workflows that apply confidence-based routing, where high confidence data is propagated to downstream systems and lower confidence items enter the A2I review flow. The processed data is stored in DynamoDB and extracted periodically for ERP integration and downstream accounting consumption.
Implementation and delivery were executed in partnership with MothersonSumi Infotech & Designs Limited MIND, leveraging MIND’s APN incubation engagement with Amazon Textract to accelerate time to production. The serverless design reduced operational overhead by minimizing infrastructure management and allowed a faster rollout cadence. The solution provided the required level of accuracy, reduced operational overhead, and achieved faster time to production as reported by the project stakeholders.
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Healthcare | 15000 | $548M | India | Amazon Web Services (AWS) | Amazon CloudWatch RUM | Application Performance Management | 2021 |
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Healthcare | 15000 | $548M | India | Amazon Web Services (AWS) | Amazon Textract | Intelligent Document Processing | 2021 |
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