One of the biggest bottlenecks in engineering and startup research isn’t finding information anymore. It’s turning hundreds of papers, reports, and PDFs into something you can actually use. Let’s say you’re evaluating a new AI architecture, researching battery technology, or trying to understand a competitor’s approach. You can spend days digging through papers, manually copying numbers into spreadsheets, and trying to figure out which results are actually relevant. And while ChatGPT can help summarize things, it still has a habit of confidently making up citations, metrics, and technical details when you push it into niche domains.
SciSpace is trying to solve that problem by turning research into a structured workflow instead of a manual process. Rather than searching for papers one by one, reading everything yourself, and building spreadsheets from scratch, SciSpace can search across research databases, pull relevant papers, extract specific data points, and organize everything into a format you can actually work with. I’m going to walk through a practical workflow for researching a technical topic with AI, extracting structured data directly from research papers, verifying where every number came from, and exporting the results into a clean data set you can use for analysis. We’ll use SciSpace to power the entire process, from discovery all the way through data extraction.
Starting with a topic, not a single paper
When you first land on the platform, the goal isn’t to search for individual papers one by one. Instead, we’re going to use it to explore an entire technical topic. Let’s say I’m researching AI coding agents. This is a space that’s moving incredibly fast right now, and there are papers, benchmarks, and new architectures being published constantly. So I’ll enter a query asking SciSpace to find research related to AI coding agents, compare the major approaches being used, and surface the most relevant papers.
Rather than manually jumping between Google Scholar, PDFs, and research databases, SciSpace pulls everything into a single workflow. After a few moments, we get a collection of relevant papers along with summaries, citations, and links to the source material. From here, we can start exploring individual papers, build comparison tables, or dig deeper into specific areas that look interesting.
One thing I also like is that SciSpace integrates directly with ChatGPT through their SciSpace GPT. So, if you’re someone who already spends a lot of time inside ChatGPT, you can continue the workflow there as well. Instead of using a standard ChatGPT conversation, you can use the SciSpace GPT to ask research questions, compare approaches, and pull in evidence-backed information connected to the underlying research. The nice part is that both experiences connect back into the same overall workflow. Whether you start inside SciSpace or inside ChatGPT, the goal is the same: find relevant research quickly and turn it into something you can actually analyze. And that’s where things get really interesting, because instead of stopping at summaries, we’re going to take these papers and convert them into a structured data set.
Extracting structured data instead of just reading papers
Most people stop at finding papers. They’ll read a few abstracts, maybe skim through some PDFs, take a few notes, and eventually end up with a folder full of documents they probably won’t revisit. The problem is that research papers aren’t very useful on their own. What you actually want is the information inside them organized into a format you can compare.
So, instead of reading these papers one by one, we’re going to let SciSpace extract the data for us. I’ve already shortlisted a handful of papers related to AI coding agents, and now I’m going to create a comparison table. By default, you’ll see some basic information, but what’s really powerful is that we can create our own custom columns using plain English. For example, let’s add a column asking what agent architecture the paper uses. Then I’ll create another one asking what benchmark was used for evaluation. And for the third column, let’s ask for the reported performance or success rate.
What’s happening here is pretty interesting. Instead of simply summarizing the paper, SciSpace is actually reading through the full text and extracting the specific information we’re asking for. So, rather than digging through dozens of pages looking for a single metric, the platform is pulling those details into a structured table automatically. After a few moments, you can see the table start filling in. We now have the architecture, benchmark, and performance data from multiple papers all sitting side by side in one place.
This is the kind of task that would normally take hours of manual reading and note-taking, especially if you’re comparing 10, 20, or even 50 different papers. Here, we’re getting a structured overview almost immediately. And this is also where the workflow becomes useful for founders and engineers. Whether you’re evaluating competitors, researching a new technology, or trying to understand the state of the market, the goal isn’t just finding information. The goal is turning unstructured research into data that you can actually work with.
Verifying every number at the source
Now, the obvious question is, how do we know these values are correct? Because if we’re going to use this information for real decisions, we need a way to verify everything. Fortunately, SciSpace gives us a way to do exactly that.
Let’s take one of these extracted values as an example. Maybe it’s a benchmark score, a performance metric, or a specific architecture mentioned in the paper. Instead of just showing us the answer, we can click directly into the cell. And when we do that, SciSpace jumps straight to the source inside the paper and highlights the exact section where the information was found. So, rather than wondering whether the AI generated the answer correctly, we can immediately inspect the original text ourselves. If something looks off, we can review the context, check the methodology, and verify the result directly against the paper.
This turns the workflow from trust the AI into verify the AI, which is a much more practical way to use these systems in technical environments. You’re not blindly accepting outputs; you’re using the AI to surface candidates and then doing a quick human sanity check. That’s the kind of loop that actually works when the stakes are high.
Exporting research into a usable dataset
Once you’re happy with the data, the next step is getting it into a format you can actually use. Instead of manually copying everything into Excel or building a spreadsheet from scratch, we can simply export the entire table. With a couple of clicks, SciSpace generates a CSV containing all of the extracted information.
At this point, the research is no longer trapped inside a collection of PDFs. We now have a structured data set that can be analyzed in Excel, imported into Python, fed into a dashboard, or combined with other sources of data. For founders, this can become a competitor intelligence data set. For engineers, it can become a technology comparison matrix. For researchers, it can become the starting point for a much larger analysis pipeline. And once you have the structured data, you can move a lot faster because you’re spending your time evaluating information instead of hunting for it.
Decoding equations and jargon without leaving the paper
There’s one more problem that shows up when you’re reading technical papers. Even after you’ve found the right document, you’ll eventually run into a page full of equations, symbols, or domain-specific terminology that makes absolutely no sense at first glance. So, instead of leaving the paper and opening 10 more tabs, we can use SciSpace’s Co-pilot and Math Snippet tools directly inside the document.
For example, here’s a fairly complex equation. I’m going to activate Math Snippet and simply draw a box around it. Once I do that, SciSpace analyzes the equation, identifies the variables, and explains what’s actually happening in plain English. Rather than just describing the math itself, it also explains how the equation is being used within the context of the paper. So, instead of seeing a wall of symbols, we get an explanation of what each variable represents, how the different terms interact, and why the equation matters to the experiment or system being described.
The same idea applies to technical jargon as well. If there’s a paragraph, methodology, or concept you don’t understand, you can highlight it and ask questions directly inside the document. This is particularly useful when you’re researching outside your normal domain. Maybe you’re a software engineer trying to understand a material science paper, or a founder evaluating research in an industry you’ve never worked in before. Instead of constantly jumping between Google searches, documentation, and Wikipedia pages, you can stay inside the paper and ask questions as you go.
What I like about this workflow is that it removes a lot of the friction that normally comes with technical research. You’re not just finding papers faster, you’re also reducing the amount of time it takes to understand them once you’ve found them. And when you combine that with the search, extraction, and verification workflow we looked at earlier, you end up with a process that feels much closer to working with structured data than working with a giant pile of PDFs.
Key Takeaways
- The real bottleneck in technical research isn’t discovery, it’s turning unstructured PDFs into comparable, usable data.
- SciSpace lets you explore an entire topic, not just hunt for individual papers, pulling relevant research into one place.
- Custom extraction columns let you pull specific metrics (architecture, benchmark, success rate) from the full text of papers automatically, saving hours of manual reading.
- Every extracted value is directly linked to its source in the paper, so you can click and verify the original context instead of trusting the AI blindly.
- Exporting to CSV turns a pile of papers into a structured dataset that can be analyzed in Excel, Python, or any other tool you already use.
- Built-in tools like Math Snippet and Co-pilot let you decode complex equations and jargon without leaving the document, keeping you in the flow.
- The overall shift is from a manual, multi-tool process to a single research operating system where you spend time analyzing information, not hunting for it.
SciSpace feels less like a paper reading tool and more like a research operating system. Instead of spending hours digging through PDFs and spreadsheets, you’re spending more time analyzing information and making decisions, which is where the real value comes from. That’s probably the biggest takeaway for me: the workflow turns research into a structured process. We started with a broad question, found relevant papers, extracted specific data points, verified where every answer came from, and exported everything into a data set we can actually work with. Normally, that would involve multiple tools and a lot of manual effort. If you’re curious to try this workflow yourself, SciSpace has a lot more features than what I covered here, so it’s worth exploring the platform and seeing how it fits into your own research process.