I'm in the final stages of a major research project consolidation for our revenue operations team, and I've been extensively testing Iris.ai as a potential tool to map and deduplicate academic and patent literature against our internal product development whitepapers. The core promise of contextual understanding, rather than simple keyword matching, is what attracted me. However, I am encountering a persistent and critical issue that is fundamentally breaking my workflow: the system appears to be systematically ignoring what I have defined as key terms in my search queries, leading to irrelevant or incomplete result sets.
My process has been methodical. I am not using the simple search bar; I am constructing focused research questions within the "Workspace" feature. For example, a recent query was structured as: "Identify studies on **data migration fatigue** in **enterprise CRM ecosystems**, specifically focusing on **user adoption barriers** post-**Salesforce** or **HubSpot** migration." In this query, I have explicitly bolded the terms I consider non-negotiable anchors. The returned results, however, are heavily weighted towards general change management in software implementation, with a glaring absence of the specific phrases "data migration fatigue" and "CRM ecosystems." It feels as though the AI is performing a form of over-summarization, extracting the broad conceptual theme (change management) while discarding the precise terminology that gives the query its necessary context and scope.
This isn't an isolated case. I have experimented with several variations and syntax approaches:
* Using quotation marks for exact phrases (e.g., "data migration fatigue") yields slightly better results, but the system still seems to deprioritize them in favor of its own thematic interpretation.
* Attempting to use the advanced filters to *require* these terms often returns zero results, suggesting the document parsing and indexing phase may be transforming or ignoring these keywords from the source material as well.
* I have reviewed the "key concepts" auto-extracted by Iris.ai from my uploaded documents, and it consistently omits the very jargon-critical terms central to my field.
From a data cleaning and integration specialist's perspective, this behavior mirrors a common ETL pipeline problem: over-aggressive stop-word filtering or lemmatization that strips out domain-specific terminology. In a business context, distinguishing between "lead scoring" (a specific CRM process) and "scoring" (a general concept) is crucial. If the tool is normalizing language to a point where such distinctions are lost, its utility for specialized professional research is severely compromised.
My core questions for the community are:
* Has anyone else working in technical, niche, or business-oriented fields (like CRM, API development, workflow engineering) encountered this specific issue of key term omission?
* Are there proven query construction methodologies, workspace settings, or document pre-processing steps within Iris.ai that force a stricter adherence to the supplied terminology?
* Is this a known limitation of the underlying NLP model, where conceptual search inherently sacrifices lexical precision, or is there a configuration layer I am missing?
Without a resolution, this presents a significant pitfall. I cannot rely on a research tool that fails to honor the precise language of my domain, as that precision is exactly what separates relevant from irrelevant literature in a professional setting. I am currently evaluating whether I need to revert to a hybrid approach of using Iris.ai for broad theme identification and then applying manual Boolean searches elsewhere, which rather defeats the purpose of an intelligent research assistant.