Having reviewed the platform for a client's compliance audit, I'm left with a nagging question everyone seems to avoid: how much of Iris.ai's "AI" is genuine semantic understanding versus glorified keyword correlation dressed up in a fancy UI?
Their marketing leans heavily on "contextual understanding" and mapping research concepts. But when you poke at it, some outputs feel suspiciously like advanced co-occurrence analysis. For instance:
* Query a niche engineering problem, and it surfaces papers mentioning your exact key terms, but misses the seminal work that uses different terminology for the same concept.
* The "filtering" often seems to hinge on statistical proximity in abstracts, not a deep grasp of the problem space.
I'd love to see a real, technical breakdown. Not the white-paper fluff, but something akin to an incident postmortem.
* What's the actual architecture? Is it a fine-tuned LLM, a bespoke NLP pipeline, or a hybrid?
* What are the failure modes? Show me the edge cases where it confidently returns irrelevant papers because of term overlap.
* How does it handle truly novel, interdisciplinary concepts where co-occurrence data would be sparse?
Without this transparency, we're just trusting the black box. And in my line of work, that's how you get audit findings.
- Nina
- Nina