AWS Success Stories with Sansa Technology
Powering Innovation, Scalability, and Resilience through Our AWS Expertise
As an AWS Partner, Sansa Technology empowers organizations to scale intelligently, modernize infrastructure, and deliver AI and data-driven innovation. Explore a few of our recent success stories showcasing the impact of our AWS-led solutions across industries.
Smart Invoice Processing Bot
Automating Invoice Processing with AWS AI – 85% Time Savings for AP Teams
"This AI-driven automation helps our clients scale without scaling cost."
Industry: Logistics Services
Client Profile:
A mid-sized logistics company processing ~5,000 invoices/month using manual entry
Problem:
Manual invoice data entry caused:
- High error rates
- 3–5 day processing delays
- Limited scalability without adding staff
Solution:
We built a Smart Invoice Processing Bot using AWS AI/ML stack:
- Invoices uploaded to S3 trigger an automated pipeline
- Textract extracts key fields (invoice number, date, amount, line items)
- Lambda + Step Functions format and validate the data
- Output sent as structured JSON to an ERP-compatible API
Tech Stack:
- Amazon Textract
- AWS Lambda
- Step Functions
- Amazon S3
- Amazon DynamoDB (for log/metadata)
Results:
- 85% reduction in processing time per invoice
- 60% fewer data entry errors
- Improved audit readiness (data logged and searchable)
- Fully serverless and scalable
Visuals:
- Architecture diagram
- Before vs. after invoice processing screenshots
- Time savings graph
GenAI Knowledge Assistant (RAG Bot)
Reclaiming Hours with GenAI: A Knowledge Assistant Built on AWS
"This assistant feels like having our policies available on demand — without the hassle of search." – Director of Compliance
Client Profile:
A mid-sized healthcare consulting firm with 70+ internal policy documents, SOPs, and compliance manuals.
Problem:
Employees wasted significant time searching for information across shared drives, PDFs, and outdated portals. Critical delays were common during audits and client onboarding.
Solution:
We built a GenAI Knowledge Assistant — a secure, private chatbot that lets employees “ask questions” directly from their internal documents.
Using AWS-native services, we created a Retrieval-Augmented Generation (RAG) system:
- Documents are uploaded to S3 and embedded into a vector index.
- Questions are processed through Amazon Bedrock (Claude), referencing indexed content via OpenSearch.
- LangChain handles retrieval logic and context injection.
Tech Stack:
- Amazon S3 (document storage)
- Amazon Bedrock (LLM inference)
- OpenSearch (vector store for retrieval)
- Lambda + LangChain (RAG orchestration)
Results:
- 90% reduction in “where do I find X” questions
- 70% faster internal onboarding
- Enhanced audit response time
- Enabled non-technical teams to access institutional knowledge easily
Visuals:
- Screenshot of chatbot in action
- RAG pipeline architecture diagram
- Search vs. GenAI response time comparison
Customer Churn Predictor
Predicting Customer Churn with ML on AWS: A 20% Boost in Retention Strategy
"We no longer fly blind — we know who’s likely to churn and can act in time." – VP of Customer Success
Title:
Predicting Customer Churn with ML on AWS: A 20% Boost in Retention Strategy
Client Profile:
A mid-sized B2B SaaS company with 2,000+ paying customers and no proactive churn prevention model.
Problem:
Customer success teams lacked visibility into which clients were at risk of leaving. Retention efforts were reactive and often too late.
Solution:
We built a Customer Churn Predictor using AWS AI and Data services. The system ingests product usage, ticket data, and renewal behavior to generate risk scores.
Key components:
- Historical customer behavior data stored in Redshift
- Data cleaned and prepared with AWS Glue
- Churn model trained using SageMaker Autopilot
- Risk scores visualized in QuickSight with filters by segment, tenure, and feature usage
Tech Stack:
- AWS Glue (ETL pipeline)
- Amazon Redshift (data warehouse)
- SageMaker Autopilot (model building)
- QuickSight (churn dashboard)
Results:
- 20% increase in proactive save campaigns
- 3.5x improvement in targeting high-risk clients
- Executive visibility into churn trends and triggers
- Delivered within 2 weeks using serverless AWS stack
Visuals:
- QuickSight churn dashboard
- Feature importance chart
- SageMaker Autopilot model performance
AI Proposal Writer
Accelerating Sales Cycles with GenAI-Powered Proposal Generation
"We can now respond to RFPs twice as fast — and more consistently." – Sales Lead
Client Profile:
A digital marketing agency writing 15–20 custom client proposals per month.
Problem:
Sales and pre-sales staff were spending hours copying boilerplate, tweaking language, and formatting pitch decks manually.
Solution:
We deployed the Sansa Proposal Writer, which uses:
- Amazon Bedrock for Claude/Mistral text generation
- DynamoDB to store reusable proposal blocks
- Lambda for prompt injection + output formatting
- Optional S3-hosted frontend for sales input forms
Results:
- Cut proposal prep time from 3 hours to 20 minutes
- Improved consistency and win rates
- Created a library of “best-practice” prompts and responses
Ops Forecaster - ML
Predicting Equipment Downtime with Sansa Ops Forecaster
"We’ve never been this ahead of problems — now we’re fixing before failure." – Plant Ops Manager
Client Profile:
A regional manufacturer of auto parts with 30+ machines across 4 facilities.
Problem:
Unplanned maintenance was causing unexpected downtime, halting production, and increasing costs.
Solution:
We implemented the Sansa Ops Forecaster, a predictive maintenance accelerator using:
- Simulated sensor logs ingested via IoT Core
- Data storage in S3 and transformation via Glue
- Model training in SageMaker
- Real-time risk dashboard in QuickSight
Results:
- 25% reduction in unplanned downtime
- Shift from reactive to preventive maintenance
- Management visibility into machine-level risk scores
Scalable Data Lake on AWS
“Sansa’s AWS expertise transformed our data infrastructure. What used to take hours now happens in minutes—our clients are seeing insights faster than ever.”
Industry: IT Services
Challenge: Rapidly growing client data made reporting slow and expensive.
Solution:
Sansa implemented a modern data lake architecture using Amazon S3, AWS Glue, and Athena for serverless querying. We automated ETL pipelines and reduced data latency from hours to minutes.
Outcomes:
80% reduction in reporting time
Costs cut by 60% with pay-per-query model
Enabled near real-time insights and new analytics services
AI-Powered Compliance Engine
“With Sansa’s AI-powered solution on AWS, we automated compliance checks with near-perfect accuracy. It’s been a game-changer for both speed and risk reduction.”
Industry: Healthcare Technology
Challenge: Manual regulatory document processing and high risk of non-compliance.
Solution:
Built an AI-powered document intelligence solution using Amazon Textract, Comprehend Medical, and SageMaker for classification and extraction. Integrated with secure Amazon VPC and encryption for HIPAA compliance.
Outcomes:
90% automation of document review
99% data accuracy
Reduced audit prep time by 70%
Serverless Customer Feedback Analytics
“Sansa delivered a serverless solution that scaled seamlessly with our survey load. Their integration of GenAI on AWS gave us insights we never thought possible.”
Industry: SaaS / Research
Challenge: Inability to handle variable loads for survey analytics and sentiment detection.
Solution:
Deployed a serverless analytics engine using AWS Lambda, Amazon Kinesis, and Bedrock for generative insights from open-text survey responses.
Outcomes:
Auto-scalable platform for millions of responses
5x faster time-to-insight
Added new monetized GenAI features in < 90 days
Cloud Migration for Legacy ERP
“Sansa executed a flawless ERP migration to AWS—no downtime, full functionality, and better performance than our on-prem setup ever delivered.”
Industry: Logistics & Supply Chain
Challenge: On-prem ERP created operational bottlenecks and poor remote access.
Solution:
Executed a lift-and-shift migration to Amazon EC2 and RDS, followed by containerization of ERP services using ECS Fargate and VPN access via AWS Client VPN.
Outcomes:
40% lower TCO
Enhanced uptime and remote usability
Zero disruption during migration