WebClues is a trusted data science company for startups and enterprises building scalable models, pipelines, and custom analytics solutions.
Strategize, audit, and scale your data initiatives with end-to-end expert-led consulting services.
Structure, clean, and pipeline your data for scalable, analytics-ready infrastructure.
Forecast demand, detect churn, or optimize pricing using proven predictive modeling techniques.
Unlock insights from massive datasets using cloud-native tools and distributed computing frameworks.
Build interactive visual reports that turn real-time data into executive-level decisions.
Design intuitive visual layers to explore, explain, and share insights from complex datasets.
Translate the problem into a data science task with defined goals and metrics.
Find and connect internal or external data sources relevant to the defined use case.
Build early-stage models to validate assumptions and refine the approach.
Deploy the final system with APIs, dashboards, and retraining logic where needed.
01 | Intelligent Forecasting | Predict future trends using historical data. |
02 | Operational Efficiency | Optimize workflows through data-driven automation. |
03 | Customer Insights | Understand behavior and improve personalization. |
04 | Risk Detection | Identify anomalies and minimize business threats. |
05 | Strategic Planning | Drive growth with real-time, actionable insights. |
Developed a data model that calculated a custom engagement score using real-time metrics like likes, shares, comments, and watch time. This helped platform owners identify trending content, optimize reach, and improve user targeting.
Designed a streaming analytics pipeline to detect early patterns of viral content using user activity data. The system helped decision-makers act faster by identifying what was gaining momentum in real time.
Created a collaborative filtering model using ALS (Alternating Least Squares) to suggest personalized content based on past interactions. This led to increased user engagement and time spent on the platform.
A retail brand needed help predicting product demand across stores. We built and trained ML models on their historical data to improve inventory accuracy and reduce stock outs during peak sales.
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