Comment by hi_hi
Are you able to share. I would love to see real world success stories of LLM use cases and integrations, beyond the common ones you see often (code gen, story gen, automated summaries, etc)
Are you able to share. I would love to see real world success stories of LLM use cases and integrations, beyond the common ones you see often (code gen, story gen, automated summaries, etc)
That last point (compliance gaps in fintech) sounds fascinating. Is there a place that I could read more about this?
Compliance gaps / legal analysis is a pretty common theme in my community (meaning - it was mentioned 3-4 times by different teams). Here is how the approach usually looks like:
0. (the most painful step) Carefully parse all relevant documents into a structural representation that could be walked like a graph.
1. Extract relevant regulatory requirements using ontology-based classification and hybrid searches.
2. Break regulatory requirements into actionable analytical steps (turning a requirement into checklist/mini-pipeline)
3. Dynamically fetch and filter relevant company documents for each analytical step.
4. Analyze documents to generate intermediate compliance conclusions.
5. Iteratively validate and adjust analysis approach as needed.
6. Summarize findings clearly, embedding key references and preserving detailed reasoning separately.
7. Perform gap analysis, prioritizing compliance issues by urgency.
Of course.
Most of the AI cases (that turn out to be an actual success) focus around a few repeatable patterns and a limited use of "AI". Here are a few interesting ones:
(1) Data extraction. E.g. extracting specs of electronic components from data-sheets (it was applied to address a USA market with 300M per year size). Or parsing back Purchase Order specs from PDFs in fragmented and under-digitized EU construction market. Just a modern VLM and a couple of prompts under the hood.
(2) French company saved up to 10k EUR per month on translators for their niche content (they do a lot of organic content, translating it to 5 major languages). Switched from human translators to LLM-driven translation process (like DeepL but understanding the nuances of their business thanks to the domain vocabulary they through in the context). Just one prompt under the hood.
(3) Lead Generation for the manufacturing equipment - scanning a stream of newly registered companies in EU and automatically identifying companies that would actually be interested in hearing more about specific types of equipment. Just a pipeline with ~3-4 prompts and a web search under the hood.
(4) Finding compliance gaps in the internal documents for the EU fintech (DORA/Safeguarding/Outsourcing etc). This one is a bit tricky, but still boils down to careful document parsing with subsequent graph traversal and reasoning.
NB: There also are tons of chatbots, customer support automation or generic enterprise RAG systems. But I don't work much with such kinds of projects, since they have higher risks and lower RoI.