I've been tasked with evaluating tools for a new R&D pipeline in a biotech startup. We need to ingest and link thousands of academic papers, patents, and internal reports to identify novel compounds. Speed is critical—we can't have analysts manually sifting through everything.
Someone suggested Iris.ai. The marketing claims it can "extract and systematize scientific data" with AI. Sounds great, but I'm deeply skeptical of any tool that promises to understand complex domain-specific relationships out of the box. My questions are for anyone who's actually pushed it beyond a simple demo:
* **Integration:** Did you manage to get it working headless via API? We need to feed processed documents into our own Snowflake warehouse and downstream dbt models. Was the output structured enough (e.g., JSON with consistent schema) for a real ETL process?
* **Pace & Iteration:** How fast is the feedback loop? If we get a batch of 500 new PDFs on Monday, can the tool's "researcher" or "extractor" modules have something queryable by Tuesday? How much manual "training" or correction was needed per project?
* **Real Output:** Did it actually find non-obvious connections, or just keyword matches? Were the extracted data points (materials, methods, outcomes) reliable enough to build a knowledge graph on?
I'm not interested in a sales pitch. I want to know if this thing can hold up under the pressure of weekly sprints and shifting research goals, or if it's just another brittle prototype that crumbles the moment you scale past 100 documents. What was your actual experience?
garbage in, garbage out
That's a very practical, grounded set of questions. I've seen two teams in my network try to implement Iris.ai for similar high-throughput projects.
On integration, the API is functional but you'll hit a ceiling on schema consistency. The output JSON structure for extracted entities is decent, but the confidence scores and relationship mappings can be inconsistent between document types. You'll likely need a solid normalization layer in your pipeline before Snowflake. One team ended up using it only for the initial "focus" module to filter relevant documents, then switched to a different tool for the actual structured data extraction.
For your pace question: a batch of 500 PDFs by Tuesday is possible, but "queryable" depends entirely on your tolerance for noise. The initial automated processing is fast. The delay comes from the manual review needed to validate connections, especially with patents where the language is highly specific. It did surface some non-obvious connections between compounds and side effects, but they were buried in a larger volume of superficial keyword matches. The value was in narrowing the field for human review, not autonomous discovery.
Your skepticism about domain-specific understanding is well placed. It worked better as a force multiplier for specialized analysts than as a standalone "understanding" engine. Have you looked at how complex your internal report formatting is? That's often the biggest hurdle for clean extraction.
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