Workers’ compensation claims were costly, and there was no consistent way to tell which workplaces were most likely to have safety issues.
What we built
A machine-learning model that scores each workplace’s safety propensity from factory size, employee count, machinery size and weight, and audit history — with results delivered in Power BI dashboards.
The outcome
Workplaces ranked by safety risk, so interventions go where they matter most.
AI approach
Predictive modellingRisk scoringBI dashboards
Tech stack
Pythonscikit-learnAWSPower BI
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Eye-care technology company (small business) · USA
Problem type
Customer & market insight
Service area
Insights Engine
Used by
Optometry equipment manufacturers
The challenge
Optometry equipment manufacturers wanted to understand how their products are received across different demographic groups — evidence sales figures alone could not give them.
What we built
A production chat and insights platform over an enriched knowledge base. Retrieval keeps every answer grounded in source data, and sentiment analysis rolls up into brand-sentiment reports and dashboards.
Manufacturers get grounded, demographic-level insight into how their products are accepted.
AI approach
RAG over an enriched knowledge baseSentiment analysisGrounded report generation
Eye-care technology company (small business) · USA
Problem type
Trend & sentiment monitoring
Service area
Insights Engine
Used by
Product and marketing teams
The challenge
The team needed to know which ophthalmology products, brands and companies were trending in social-media conversations — and how people felt about each of them.
What we built
A pipeline that collects posts from relevant social-media communities, scores sentiment towards each product, brand and company, and turns the results into word clouds and charts.
Detailed trend and sentiment insight on ophthalmology products and brands.
