Rethinking Research with AI: My Journey from Idea to SaaS Prototype

When I first decided to build a SaaS product, I began with one question: how can I merge my biomedical expertise with the power of LLMs?

As I mentioned in previous articles, biomedical scientists like me spend a tremendous amount of time searching, reading papers, and writing research reports — tasks that LLMs are naturally well-suited to assist with. It felt like the perfect use case. So, I began exploring tools like n8n to create automated workflows. After seeing early results, I shared my concept on Reddit’s r/molecularbiology to gather feedback.

Let’s just say… it sparked a lively debate.

Reddit’s Response: Valid Concerns & Real Insights

Most of the feedback was critical. Here were the top three concerns:

1. LLMs aren’t trained on medical-grade data
2. Young scientists need to develop their thinking skills — not outsource them
3. Uploading grant data may pose privacy risks

Let’s address them:

• Model training: I agree, however, fine-tuning on high-quality biomedical data is expensive and not feasible for me at this stage.
• Critical thinking: Also fair. But instead of targeting junior PhDs, I then shifted my audience toward industry professionals and research institutions, where the focus is more on cross-disciplinary innovation. For example, a scientist who studies RNA biology might need such a tool to help him/her grab basic knowledge in viral vectors (virology)and nanoparticles (biochemistry), so he/she knows which delivery system is better for this novel RNA molecule.
• Data privacy: Solvable. I ensure the backend doesn’t store user data.

Discovering Similar AI Research Tools

While exploring this idea, I found several tools with similar goals — automating scientific writing with short prompts. Here are the three closest in concept:

1. Stanford STORM

• Strengths: Generates wiki-style review papers quickly, great for entering new fields. Uses multiple prompts and agents for multi-perspective writing with well-annotated citations.
• Limitations: Only supports one follow-up question. Limited to 7-page outputs. Doesn’t support PDF uploads for in-depth analysis. Biomedical content is thinner compared to other disciplines like math modeling.

2. Connected Papers

• Strengths: Visualizes citation networks with a beautiful bubble map. Excellent for discovering related literature.
• Limitations: Only shows relatedness — doesn’t help generate writing content.

3. SciSpace

• Strengths:

(1) Powerful AI tools for summarizing and querying over 300 million scientific papers.

(2) Supports uploading PDFs for custom Q&A (great for “Chat with PDF” style research).

(3) Offers citation formatting tools and multilingual support.

(4) Can highlight gaps or limitations in papers.

(5) Academic writing assistant for rephrasing and paraphrasing.

(6) Can detect if content was AI-written

• Limitations:

(1) Free version has a strict usage cap (5 AI actions/day).

(2) Paid version is pricey: $72/month or $504/year.

(3) Content tends to be too generalized — summaries aren’t detailed enough for proper grant writing.

(4) The AI Writer only produces high-level outlines; deeper content costs additional AI actions.

Other Notable AI Tools in the Space

• Research Rabbit — Dynamic visual literature networks with advanced filtering
• Semantic Scholar — Summarizes papers and tracks citations
• Elicit — Finds and summarizes relevant research with AI assistance
• Iris.ai — Helps generate hypotheses and discover new papers
• Scite.ai — Analyzes whether citations support or contradict the paper

Each of these tools brings something unique. Depending on your research goal — be it hypothesis generation or citation validation — you’ll find one that fits your workflow.

My Takeaway: We Deserve Better Tools

The sheer volume of criticism on Reddit made one thing clear: many scientists have tried these tools — and were disappointed. Not because they threaten our thinking ability, but because they simply aren’t good enough yet.

So instead of dismissing AI in science, we should be improving these tools and pushing for better integration into research workflows. The future of science should be faster, smarter, and more collaborative — and AI will play a big part in that. I’ll be sharing more soon about another exciting project called Virtual Lab. Stay tuned!