Case study · Healthcare · Ophthalmology & optometry
Product-acceptance insights platform for eye-care equipment
Eye-care technology company (small business), USAIn production
Organisation
Eye-care technology company (small business)
Industry
Healthcare · Ophthalmology & optometry
Region
USA
Service area
Insights Engine
Problem type
Customer & market insight
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.
The outcome
Manufacturers get grounded, demographic-level insight into how their products are accepted.
AI approach
RAG over an enriched knowledge baseSentiment analysisGrounded report generation
Tech stack
LLM chatVector retrievalInsight dashboards
Have a similar problem?
Book a free 30-minute AI review — we’ll tell you honestly what would work.
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.
Insurance & manufacturing client (consulting engagement) · USA
Problem type
Risk prediction & prioritisation
Service area
Insights Engine
Used by
Safety and risk teams
The challenge
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.
Workplaces ranked by safety risk, so interventions go where they matter most.
The client wanted a visual expert that could talk with patients, help identify their issues and respond with empathy — answering strictly from approved Chinese medical texts, with no made-up advice.
What we built
Approved medical texts in a vector database with RAG on Azure OpenAI; GPT-4 fine-tuned for an empathetic, doctor-style manner; speech-to-text and text-to-speech for voice; and a Wav2Lip lip-synced video avatar served through a FastAPI web app.
Pilot delivered with 100% precision on generated answers and no hallucinations; validated for production development.
AI approach
RAG on approved sourcesLLM fine-tuningSpeech-to-text & text-to-speechLip-synced video avatar
