GreenM

We developed a new real-time analytics portal that processes patient visit data and survey results for further analysis and reporting. This product was designed so that:

• frontline employees can improve their service of nurses, physicians, etc.; 
• experience managers can track and manage the institution's performance; 
• hospital leadership can monitor patient satisfaction levels, increase patient experience, and timely react to incidents through feedback analytics and alerts. 

MAIN CHALLENGES

• Figuring out the business domain, available data structure, and sources
• Quickly switching from IT-dependent resource provisioning to self-service
• Migrating all the key customers to the new environment and setting automated deployments
• Maintaining a balance between infrastructure governance and freedom to provision resources for engineers
• Supporting the rapidly growing platform and improving major non-functional requirements

WHAT WE DID

• Developed a real-time analytics portal for patient visit data and survey results
• Implemented a data pipeline for data ingestion and storage
• Migrated existing infrastructure to AWS, enabling a self-service platform
• Refactored critical services for a serverless approach
• Created a scalable data lake pipeline and BI portal using AWS-managed services
• Enhanced platform availability with a multi-account AWS setup and automated deployments

RESULTS

• Automated Operations: Streamlined operational workflows, reducing manual effort and operational costs
• Improved Performance: Optimized data processing and reduced latency, enhancing the overall user experience
• Advanced Analytics: Provided actionable insights through enhanced data visualization and analytics capabilities, aiding better decision-making
• Increased Scalability: Enabled seamless scaling of the platform to accommodate a growing user base and large datasets
• Simplified Integration: Facilitated easy integration with third-party applications and services, increasing the platform’s functionality

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Results given by any Generative AI product are not that perfect all the time. There is always room for improvement and better client-specific responses. So, we locally deployed an LLM Llama-3 for our AI sentiment analysis tool to boost the output quality and vanish data privacy concerns.

MAIN CHALLENGES

• Data privacy concerns
• High costs for cloud LLM
• Lack of quality responses
• Right dataset preparation

WHAT WE DID

• Deployed a private LLM Llama-3 using Python and PyTorch
• Ensured data privacy by keeping processing local
• Enhanced AI sentiment analysis tool for better output quality
• Prepared and integrated datasets with bad and good responses for fine-tuning

RESULTS

• Improved Response Quality: Achieved a quality score of 382 for Llama-3:8b tuned
• Cost Efficiency: Reduced processing cost to $0.005 per 1,000 tokens
• Enhanced Performance: Configured 8 billion parameters for complex responses

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In today’s data-driven business environment, organizations deal with loads of data stored, handled, and processed in complex databases. However, accessing this valuable data typically requires technical expertise in writing SQL queries.

So, we created an intuitive NLP-to-SQL system that simplifies database interaction by:  

• converting natural language inputs into SQL queries, and 
• making data access easy and effortless for non-tech-savvy users with the help of AI. 

MAIN CHALLENGES

• Accessing and interpreting large volumes of complex data
• Need for real-time data analysis and insights
• Limited user expertise in data querying tools
• Requirement for a user-friendly tool for non-technical staff

WHAT WE DID

• Created an intuitive NLP-to-SQL system to enable conversational data queries
• Integrated vector embeddings for database schema representation and optimized PostgreSQL for efficient vector similarity search
• Optimized the AI model to understand domain-specific language for better query accuracy.
• Implemented the AI tool with internal data systems, allowing seamless access to insights.
• Provided an easy access to data and analytics for non-tech-savvy users

RESULTS

• Improved Data Accessibility: Enabled real-time, conversational data interaction
• Enhanced Efficiency: Data retrieval time has been reduced by 40% and 90% resource savings in insights access
• High Adoption Rate: 70% of non-technical staff regularly use the NLP solution for data analysis
• Cost Efficiency: Lowered data analysis costs by 30% through automation

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Customer

Our client is a company that offers the leading platform for improving patient experience.

Challenges

• High Market competition. Latency and high costs of all stages in Patient Surveying process.
• No ability to satisfy demand for information of all parties-external & internal.
• No basics to introduce modern features like AI.

Goals

• Create one Platform enabling organizations with instant knowledge on what matters most to each person they serve.
• Streamline inhouse innovation by empowering internal
• Research & Data Science teams with Data

Solution

• Single source of truth for all departments.
• Self-service Analytics for internal users.
• New Real-time Analytics portal with sophisticated security and subscription system.

Result

• Right users get the right information in the right time.
• Improved patient loyalty-delighted customers and higher business market share.
• Strong position on the market due to fast adoption of the latest trends.

Challenges

• Inefficient Sales and Customer Support due to lack of insights to how customers use solution.
• No consistency between Different Departments data.
• High cost and low quality of customers Service reports.

Goals

• Create one Multi-tenant customers Web Platform to see the Big Picture.
• Enable Internal departments with tools for easy reporting.
• Proactively manage customers addressing risks and offering new licences and devices.

Solution

• Single source of truth for all departments combining variety of data for all customer.
• Self-Service Analytics to support all company departments.
• Set up of Troubleshooting workflows.

Result

• Improved business outcomes-customer base growth, contracts extensions.
• Introduction of Benchmarks and data-driven Best Practices alone with Consulting services.
• Innovation initiatives for different internal departments who can now play with data.