When prospects first learn about Prophy's AI capabilities, we hear the same question repeatedly: "How is this different from ChatGPT?"
It's a fair question. Large language models have made AI accessible to everyone. But there's a critical difference between tools built for general conversation and systems designed for scientific evaluation.
Our Head of Sales, Alex Bykov, fields this comparison daily. Here's what we've learned from thousands of conversations with publishers and funding agencies.
During a recent trial with a UK publisher, an editor raised his hand during the Q&A session. "I've been using ChatGPT for reviewer suggestions," he said. "It works fine."
His colleagues looked surprised. So did we.
The editor assumed he was working with comparable technology. ChatGPT suggests names based on patterns and frequency across internet-scale data. It makes educated guesses. Sometimes those guesses sound intelligent.
But "intelligent-sounding" isn't the same as defensible.
Here's what ChatGPT cannot do:
Prophy was built differently. We maintain a structured database of 182+ million publication records, 88 million researcher profiles, and 170,000+ scientific concepts. When you upload a manuscript, we extract key concepts using semantic analysis and match them against actual publication history.
You don't get a best guess. You get researchers with documented expertise in your specific topic area, complete with bibliometric data, conflict analysis, and verified contact details.
One prospect shared a story about using ChatGPT for reviewer selection. The system suggested a Nobel Prize winner.
Impressive, right?
Not quite. The laureate wasn't relevant to the manuscript's specific focus. ChatGPT recognized the name from its training data but couldn't assess semantic alignment. It also didn't provide contact information, conflict checks, or any indication of whether this distinguished researcher would actually respond.
Generic LLMs work from pattern recognition. They suggest people who appear frequently in their training data. That often means famous researchers who are already overwhelmed with requests.
Our system works differently. You can filter by scientific age (years since first publication) to find mid-career experts with 5-15 years of experience. These researchers have proven expertise but aren't yet drowning in review invitations. They're more likely to respond.
You can also search by gender and geography to build diverse editorial boards. You can exclude researchers who haven't published in the last two years. You can apply custom filters based on your specific needs.
These aren't features we added because they sounded good. They're capabilities our publishing and funding agency partners told us they needed.
We've watched several prospects get excited about free AI tools, then realize they can't actually use them.
The problem: unpublished manuscripts and grant proposals are confidential. When you upload sensitive documents to ChatGPT or similar platforms, you lose control over where that data goes. Prompts can be stored, reused, even indexed by search engines.
One recent ChatGPT security incident exposed thousands of private chats in Google search results. Despite efforts to remove them, some leaked conversations remain accessible in other search engines.
For publishers handling pre-publication manuscripts and funding agencies evaluating competitive proposals, this risk is unacceptable.
Prophy operates in a closed, secure environment. Documents never leave your premises. We don't retain or reuse uploaded content. We don't train our algorithms on your confidential data.
You can delete files anytime through the interface. Or we can remove them automatically on a scheduled basis—weekly, monthly, quarterly, whatever your governance policies require.
For organizations that need even tighter security, we offer docker container deployment. You can extract concepts locally before sending only anonymized data to Prophy. The full manuscript never enters our system at all.
We also provide API integration with existing editorial and grant management systems. You don't need to log into a separate platform or copy sensitive data between tools.
These capabilities exist because government agencies and public research organizations demanded them. Data security isn't a nice-to-have feature for these clients. It's non-negotiable.
The rise of accessible AI tools has changed our conversations.
Five years ago, we spent time explaining what AI could do. Now prospects already understand the concept. They've used ChatGPT. They've seen what language models can accomplish.
This awareness helps. People are no longer skeptical about whether AI can assist with complex tasks.
But it also creates confusion. After trying general-purpose tools and seeing their limitations, some prospects assume all AI faces the same constraints.
The conversation usually shifts when we explain that Prophy isn't a plugin or adaptation of existing LLM technology. We've spent seven years building specialized infrastructure for scientific evaluation.
ChatGPT was trained to be helpful, harmless, and honest in conversation. Prophy was built to analyze manuscripts, extract concepts, match expertise, detect conflicts, and integrate with institutional workflows.
Different problems require different solutions.
One client put it clearly: "ChatGPT is impressive, but we can't trust it for scientific decisions."
That's the distinction that matters. Generic LLMs create awareness of what's possible. Specialized platforms like Prophy deliver what's needed.
We could tell you Prophy has analyzed 182+ million publications and created 88 million researcher profiles. Those numbers are accurate, but they're not the real story.
The real story is structure.
ChatGPT was trained on unstructured internet data—everything from news articles to forum posts to social media. It learns patterns from massive, messy input.
Prophy was built on verified scientific literature. We maintain an ontology of 170,000+ concepts representing scientific terms, including regional spelling variations, plurals, synonyms, and abbreviations. When you search for "aluminum," we know to include results for "aluminium." When you search for emerging research areas, we understand the semantic relationships between concepts.
This structure enables capabilities generic LLMs cannot provide:
One publisher described it this way: "ChatGPT gives us names. Prophy gives us decisions we can defend."
That difference matters when you're managing hundreds of manuscripts, thousands of grant applications, or building editorial boards that need to represent diverse perspectives.
We're not suggesting generic LLMs are useless. They're remarkable technology with broad applications.
But for scientific evaluation, you need something more specific.
Consider what happens as your volume increases. Maybe you process 50 manuscripts monthly now. What about when that becomes 500? Or 5,000 proposals during peak grant cycles?
Generic LLMs don't scale for this use case. You'd need to verify every suggestion manually, check conflicts separately, find contact information through other channels, and document your decisions for institutional records.
Prophy handles scale because the system was designed for it. The European Research Council uses our platform to generate ranked expert lists for grant proposals. Publishers use it to manage peer review across multiple journals with different editorial standards.
The system works whether you're processing 10 manuscripts or 10,000.
And it works without requiring you to become an AI expert. You upload a manuscript, apply filters that match your editorial policies, and export results with all the supporting data you need.
The technology should solve problems, not create new ones.
If you're evaluating AI tools for reviewer selection or grant evaluation, ask these questions:
These aren't theoretical concerns. They're practical requirements that determine whether a tool will actually work in your environment.
We built Prophy because publishers and funding agencies told us what they needed. Generic AI tools weren't designed to solve these specific problems.
The question isn't whether AI can help with scientific evaluation. The question is whether you're using AI that was actually built for the job.