[devmor]

Using RAG is definitely a relief factor after reading that you're using AI and NLP for aggregate analysis, but I'm curious how much manual review this actually saves?

Since the model summaries would still need to be validated against the source results manually, your business' actual viability as a product hinges on whether customers perceive a significant time savings in the data provided via these channels over historical aggregation methods (like keyword analysis that you mentioned) and level of false positives.

What do you measure as the largest impact here? Is there a large time savings, is it additional discovery from blindspots that other methods don't cover? Both? Are there additional benefits you see to this model beyond automation and expanded discovery?

[Bisen]

Some of our customers said they spend around 3hrs a day on navigating new vulnerabilities alone. Step 1: wading through info from some easy and some hard to access sources; Step 2: trying to bring together all the most relevant information. E.g. just detecting your payroll provider got ransomed is just one step, then you have to research the group, any indicators that you might also be infected etc. then step 3: what do I do next? e.g. adding relevant hashes to virus total.

We not just help with relevant detection, but also automate some of the next two steps as well. Bringing the total weekly time saved down to a few minutes a day.

[Bisen]

The discover is another important value add - AI Agents can monitor beyond human scale across more data sets to do the initial triaging for you.