Success Stories
Our Work
across a wide range of industries.
We partner with a diverse set of industry leaders, technology and solutions providers to help businesses implement the most effective machine learning, data engineering, and data analytics solutions using the industry’s most cutting-edge tools.
Financial Services
Detecting Identity Fraud Using Machine Learning
The Challenge:
A challenger bank faced rampant identity fraud among credit card users, causing substantial financial losses and increased spending on mitigation. Lacking an effective fraud detection model led to inadequate prevention and heightened vulnerability to fraudulent activities.
The solution:
Engineers implemented a machine learning-driven experimentation pipeline and performed multiple factor analyses to detect the best predictive model and boost its accuracy, resulting in a 7.5% increase in fraud detection rates. This significantly relieved financial strain and fortified the company against fraudulent exploits.
Credit Card Default Prediction Model Using Deep Learning
The Challenge:
A Fintech company needed to predict credit card defaults in their expanding portfolio to minimize financial risks. Their objective was to foresee potential customer defaults within six months.
The solution:
Engineers employed deep neural networks, emphasizing temporal patterns in raw data, crafting a Credit Card Default Prediction Model. This model accurately anticipated client default likelihood, curbing potential financial losses for the company.
Customer Lifetime and Net Present Value Predictions
The Challenge:
A consumer lending decacorn aimed to balance risk management with expanding its customer base. They required a system accommodating uncertainties and financial margins while broadening their underwriting population for increased profitability without compromising financial stability.
The solution:
Engineers devised a unified system merging credit card expertise with machine learning models to predict cash-flow patterns and assess the Net Present Value of a customer’s future actions. Leveraging Bayesian Survival Models and GAMs, they tackled uncertainties and fortified financial safety measures. The solution included a monitoring mechanism to enhance model performance and accuracy. This innovation refined credit assessments, boosting customer acquisition and up-selling opportunities, allowing expansion within their customer base while upholding financial security.
Marketing
Real-Time Bidding for Programmatic Advertising
The Challenge:
An advertiser needed to efficiently utilize its monthly advertising budget to maximize reach and impact. They needed a solution to optimize ad spending at an individual impression level, ensuring every dollar spent effectively contributed to increased visibility and engagement.
The solution:
In response to the need to streamline ad spending on an impression-by-impression basis, the engineering team developed a solution that harnessed the power of feature-enriched regressions and Reinforcement Learning to facilitate unique bids on each impression. To ensure maximum efficiency and scalability, they integrated this solution with Beeswax’s sturdy physical framework, which could handle a staggering 85k queries every second.
Enhancing quality and speed of search on a music recommender system
The Challenge:
A music data analytics firm sought to enhance the speed and quality of searches (e.g., artists, song metadata) on their music recommender platform.
The solution:
Implemented customizable indexes on a new ElasticSearch cluster, utilizing transformed and enriched data from diverse sources at the Data Warehouse. This approach bolstered search accuracy and diversified the results delivered through the platform.
Digital marketing analysis to improve campaign performance
The Challenge:
A Social Media Company needed to gauge diverse marketing campaign effectiveness and identify tailored strategies for reaching distinct audiences based on frequency, channels, and content.
The solution:
Healthcare
ETLs for medical appointment automated template reporting
The Challenge:
A top cancer treatment and research institution sought to have immediate access to multiple cancer treatment programs’, data and patient information. They needed access to this data during appointments, enabling physicians to access patients’ medical histories, prior treatments, and exams immediately at point of care.
The solution:
Factored crafted an ETL pipeline to extract data from two RedCap databases, generating editable reports using templated documents. This solution facilitated the creation and ongoing update of reports on an on-premise file system, granting physicians swift access to patient-specific reports during appointments.
Identification and Classification of Audio Medical Recordings
The Challenge:
Manually identifying and categorizing medical encounters from daily audio recordings was a time-consuming and error-prone process. An automated solution was sought to expedite encounter identification, enhancing the efficiency of medical scribes’ session analysis.
The solution:
Factored crafted an ETL pipeline to extract data from two RedCap databases, generating editable reports using templated documents. This solution facilitated the creation and ongoing update of reports on an on-premise file system, granting physicians swift access to patient-specific reports during appointments.
Customer Lifetime and Net Present Value Predictions
The Challenge:
A consumer lending decacorn aimed to balance risk management with expanding its customer base. They required a system accommodating uncertainties and financial margins while broadening their underwriting population for increased profitability without compromising financial stability.
The solution:
Engineers devised a unified system merging credit card expertise with machine learning models to predict cash-flow patterns and assess the Net Present Value of a customer’s future actions. Leveraging Bayesian Survival Models and GAMs, they tackled uncertainties and fortified financial safety measures. The solution included a monitoring mechanism to enhance model performance and accuracy. This innovation refined credit assessments, boosting customer acquisition and up-selling opportunities, allowing expansion within their customer base while upholding financial security.
Retail
Customer and market trend predictions for better forecasting and product planning.
The Challenge:
The solution:
Our team created unsupervised learning models using time series clustering to predict future product trends. Our solution delivered actionable insights, guiding product development, marketing, and launch strategies. Leveraging this trend prediction solution helped the company lead in innovation, launch appealing products, and achieve higher sales and market success.
Warranty Cost forecasting to enhance claim visibility
The Challenge:
A US furniture retailer struggled to handle warranty claims and costs due to insufficient visibility and predictive data across product lines. This affected decision-making on recalls, end-of-production choices, replacements, and post-purchase product management.
The solution:
Our engineering team leveraged their data science, programming, and full-stack development expertise to create a web app for the retailer. This app forecasts warranty and replacement costs for different product lines over 5 and 12-year periods, offering crucial insights into warranty claims and visualizing potential failure probabilities.
Net price optimization for better management of discount strategies
The Challenge:
An American furniture retailer was struggling with a disorganized and inefficient discount calculation process for their various product lines managed manually by their contracts team. This confused client negotiations and impacted the company’s ability to maximize profit margins.
The solution:
The engineers created a Net Price Optimization web app, centralizing contract procedures for the team. This tool facilitated efficient discount calculations during client negotiations across product lines. It also offered added authentication measures for team leaders to modify discount strategies. Accessible to all team members, the app was secured using SSO OIDC authentication, with additional authentication for team leaders to adjust discount functions.
Using LLM to simulate pre-product launches with virtual customers
The Challenge:
A consumer packaged goods company sought assistance in predicting customer response before new product launches to avoid costly mistakes and assess consumer reaction.
The solution:
To forecast customer responses to new products, engineers utilized LLMs and prompt engineering to create virtual consumers with specific traits. They simulated various market scenarios and gathered feedback from these digital customers on potential future products, effectively predicting responses before any real product launch. This method notably improved the efficiency of market research simulations and cut down associated expenses.
Others
- Cancer Research & Treatment
- Computer Vision for Fiber-Optic Cost Prediction
- Advanced NLP Research
- Fleet Optimization
- Last Mile Delivery Optimization
- Life Insurance Mortality & Severity
- Visualization & Analytics Dashboards
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