A patient returning to the hospital unexpectedly within a certain time frame after discharge.
Financial fraud involves deceptive practices aimed at gaining monetary benefit illegally, often through misrepresentation, manipulation, or false information.
Maintenance costs represent expenses incurred to sustain or repair equipment, machinery, or systems, ensuring their proper functioning and longevity.
Transportation and logistics costs cover expenses for moving, storing, and distributing goods across various locations efficiently.
Online conversions denote desired actions (e.g., purchases, sign-ups) users take on a website, reflecting successful engagement or transactions.
Subscription growth signifies the increase in the number of recurring customers paying for services or products over time.
Bank loan analysis assesses borrower's creditworthiness, repayment ability, and risk factors to determine eligibility and terms for loan approval.
Productivity refers to the efficiency and output of tasks or activities accomplished within a specific period or resource framework.
We believe companies create valuable data every minute and we are committed to helping our clients unravel the patterns and insights only AI can do!
AI-ML
AI - Indexing
GPT
Feedback loop
INDUSTRY | CASE STUDY |
CHATGPT (GPT-4o) |
XG BOOST/RANDOM FORREST AUTO-ML |
H20.AI
V:3.46.04 |
ALTERYX V:2023.26++ |
SRIYA.AI |
---|---|---|---|---|---|---|
HEALTHCARE | > 30 DAYS HOSPITAL UNPLANNED READMISSIONS | 64.54% | 62.62% | 57.21% | 53% | 99.8% |
NATIONAL AMBULANCE ACTIVATION (NEMSIS) | 75.75% | 68.58% | 68.61% | 65% | 98.56% | |
EMERGENCY TRIAGE | 89.9% | 82.9% | 80.45% | 84% | 99.76% | |
SUPPLY CHAIN | INVENTORY OPTIMIZATION | 75.11% | 78.9% | 87.83% | 86% | 99.76% |
BACKORDER REDUCTION | 78.74% | 94.42% | 95.02% | 99% | 99.10% | |
INDUSTRIAL | CEMENT R&D | 95% | 97.56% | 77% | 100% | 100% |
PREDICTIVE MAINTAINENCE | 60.85% | 62.32% | 56.12% | 57% | 97% | |
TELECOM | WIRELESS CHURN REDUCTION | 58% | 67% | 62.47% | 59% | 98% |
HR | HR CHURN REDUCTION | 84.13% | 85.96% | 85.22% | 81% | 99.12% |
MARKETING | ADOBE PHOTOSHOP RELEVANCE | <10% | 51.28% | 67.65% | 62% | 99.32% |
FINANCIALS | Current & Late Payments | 85% | 84.52% | 84.23% | 84% | 98.25% |
PREDICT DEFAULT PAYMENTS | 94.2% | 93.87% | 70.2% | 72% | 99% |
Based on my evaluation of Sriya.ai's product, its predictive model shows a lot of potential. Look forward to exploring more.
Arindam Mitra — PhD, Verizon, Inc
Sriya product and it's predictive capabilities hold a lot of promise for the industrial giants like GE in the areas of Healthcare, Aviation and Energy.
Manoj Mehta — GE Corporate
Looking forward to partnering with Sriya AI to explore the potential of AI/ML in developing enhanced projection models for coastal flooding. Their pilot work for us was very successful in predicting Coastal Flooding in Charleston, SC and Savannah, GA using a multi modal approach. This work will help communities plan and better prepare for future flood events in conjunction with global sea level rise.
A.I.E.C.I.S., Inc.
Thank you for the results from ML implementation on our datasets. I am happy to note that even with much smaller datasets than that is used for traditional ML-AI work, Sriya's approach has been able to come up with very informed predictions about the output of both the lime and cement synthesis using our novel calcination and cementing approach. Both the predictions as well as the recommendations for further optimization are accurate. I am also happy to note that your simulations are able to predict the results in a superior manner to other ML algorithms that you have evaluated as well.
Narayanan
We used Sriya.ai to help us improve predictive power for 30-day delinquencies and charge-offs at Capchase. It is an important problem for Capchase to solve since we are able to lower our loss rates and increase our revenue if we have an accurate assessment of credit risk. The problem in our domain is that the dataset for training is small (as opposed to consumer lending), and therefore, traditional techniques do not work as well. Sriya's technology worked very well on our small dataset to give us results with high accuracy and precision (low false negatives and false positives), which will help us reduce our loss rates while being able to make an offer to more clients. In addition to the simplicity of the output of the algorithm (1 output score called SXI), they provided us with decision trees with actionable insights. Some of those insights were counter-intuitive at first glance (e.g. larger companies default at a higher rate than smaller companies), but made sense when applied through a broader lens of our offering (i.e. we get adverse selection when larger companies turn to us versus borrowing from large banks). I would highly recommend Sriya's solution to anybody looking to extract actionable insights from datasets, no matter how small or large - I think they have the potential to change the trajectory of the financial services industry.
Ishwar — VP, Capchase