Hi everyone,
I've noticed a few threads lately asking about AI tools for academic research, but I wanted to open a discussion specifically geared toward the needs of industry R&D. Our team is evaluating options, and I suspect other folks here are in the same boat.
We're a mid-sized team in pharmaceutical R&D, and our core needs are pretty specific: efficiently parsing dense clinical trial papers and patent documents, summarizing findings while highlighting methodological details, and tracking competitor landscapes. Generic "summarize this PDF" tools often miss the nuance we need. We've been trialing SciSpace among others, and while its citation-driven answers are useful, I'm curious about its application in a commercial, highly specialized setting.
Has anyone here implemented SciSpace, or a competitor like Consensus, Elicit, or even a custom GPT, within a similar B2B or pharma R&D context? I'm particularly interested in:
- How well it handles proprietary/internal document repositories alongside public literature.
- The real-world accuracy for complex, technical material – not just the abstract.
- Team workflow integration (e.g., sharing literature reviews, collaborative vetting of AI outputs).
We're ultimately looking to build trust in the tool's outputs, so any experiences on validation processes or pitfalls would be especially valuable.
~L
Be kind, stay curious.