Explained in
plain English.
You upload research papers. You tell it what to look for. It reads every paper and gives you a clean spreadsheet. Here’s exactly how.
200 papers. 8 questions each.
1,600 cells to fill by hand.
Most research teams do this manually — reading each paper, highlighting the relevant section, then copying the answer into a spreadsheet. One at a time. It takes weeks. It is exhausting. And it is full of small mistakes that compound into big ones.
Tell it what questions
to answer from each paper
Think of it like building a checklist. What do you need to know from every research paper? The age range of patients? What treatment was tested? Whether it worked? You just type your questions in — one by one. No coding. No technical knowledge required.
You can add as many questions as you need, in any structure, and change them at any time.
When you click “Generate AI Pipeline,” two things happen automatically
Your questions aren’t all equal — some are independent, some need earlier answers first. The AI groups related questions into extraction tasks, then arranges them into stages. Independent tasks run in parallel (at the same time). Dependent tasks run sequentially (in order). The result is an optimized pipeline plan.
From the pipeline plan, eviStreams generates actual Python code — one specialist class per extraction task. Each class knows exactly what fields to find and how. When you upload a paper, these classes execute in stage order and merge their results. You never write a line of code.
This all takes a few seconds. Then you upload your papers and it runs.
Upload your papers.
All of them.
Drag and drop your PDFs — ten papers or five hundred, it makes no difference. The custom pipeline eviStreams built for your questions now runs on every single paper automatically. You do not read a single word.
Here’s what the computer found
Once the pipeline finishes, eviStreams shows you a table of everything it extracted — one row per paper, one column per question. Every cell has a value pulled directly from the source text. This is your raw data, ready to check.
| Paper | Patient age group | Treatment received | Success rate | Status |
|---|---|---|---|---|
Smith et al. 2021J. Oral Implantology | Adults 45–65 | Dental implant surgery | 87% | Extracted |
Chen & Liu 2020Clin. Oral Res. | Seniors 60+ | Antibiotic therapy | 72% | Extracted |
Kumar et al. 2022Int. J. Oral Sci. | Adults 30–50 | Bone graft + implant | 91% | Extracted |
Patel & Wong 2019Periodontology | Mixed 25–70 | Medication only | Not reported | Review needed |
Review what it found.
Fix anything. Download.
You and your team go through each answer. Most will be exactly right. Some you’ll want to adjust. When two reviewers disagree on an answer, eviStreams flags the conflict so you can resolve it together — then you download everything as a spreadsheet, ready for Excel or any research tool.
Nothing is saved until you approve it. You are always in control of the final data.
Still have questions?
Plain answers to the things people ask most.
Ready to try it?
Takes about 5 minutes to get started.
Create a free account →Used by researchers at Penn Dental Medicine, University of Pennsylvania.