How do you turn your internal documents, product manuals, or research reports into an AI assistant that delivers real-time, accurate, and source-backed answers?
That’s what RAG (Retrieval-Augmented Generation) is built for — and it’s one of the most powerful AI business models we’ll cover in my upcoming AI + Business Model Innovation Workshop.
Here’s a standout case study:
🔎 Sector.app – Real-Time AI for Fast-Moving Markets Built by our consultant Samuel Chan, Sector.app combines LLMs with live data retrieval to deliver intelligent, source-based answers in industries like crypto, finance, and tech. It’s not just a chatbot — it’s a domain-trained knowledge engine designed for speed, depth, and reliability.
Other notable use cases of RAG systems:
- Lexion.ai – an AI assistant for legal teams that extracts insights from contracts and case law
- Humata.ai – helps teams query large research documents and technical papers in seconds
RAG systems are now being deployed across:
- Legal, compliance, and finance teams
- Manufacturing and R&D knowledge bases
- Healthcare and pharma research departments
If your business has rich data but struggles to access insights when it matters — this model can change that.
I’ll be sharing more about how businesses are using Production-Ready RAG Systems to scale their expertise in my upcoming AI + Business Model Innovation Workshop. Curious how AI trends will impact your business or industry? Let’s talk. 📞 +60126669892 . I’ll help you identify the trends, tools, and opportunities tailored to your industry
