Accelerating Research with AI: Insights On Helping Government Researchers Discover Data Faster

Ask any researcher what slows them down, and you’ll hear a common response, finding data. Scientific knowledge is growing at an incredible pace, but ironically, that growth is creating a new kind of bottleneck.

Critical insights are buried deep in dense publications, scattered across journals, hidden in tables, or locked behind formatting that makes them nearly impossible to extract. You spend hours downloading papers, opening tabs, scanning PDFs, only to Ctrl+F your way through a sea of documents, in hopes you will find the table you need and that it's usable.

In addition to wasting valuable time, manual processes limit the breadth of publications that can be reviewed. 

Every hour spent digging is an hour not spent analyzing, modelling, interpreting, or expanding knowledge. Researchers are being forced to trade curiosity for clerical work. 

This is the problem that we set out to change while working with NRC researchers. Our mission was to expand the breadth of search and automate the data extraction process from online sources and internal sources. Our solution, Datahunter, serves as a lighthouse example of using AI to accelerate workflows in the public sector:

Researchers using Datahunter reported up to 92% less time spent on data search and extraction, freeing up critical hours for analysis, discovery and innovation.

Introducing the Pilot: Rethinking How we Research

What was the NRC team working on?

They were modelling sustainability scenarios. The environmental research team at the National Research Council (NRC) was diving deep into lifecycle analysis (LCA) work. This involves dissecting studies that help Canada measure the true environmental impact of everything from building materials to fuel sources.

And getting to that data was tedious and time-consuming. 

What is Lifecycle Analysis?

To put it simply, LCA (Life Cycle Analysis) is how researchers evaluate the full environmental footprint of a product or process across its entire lifespan. It looks at every stage: from raw material extraction, to use, to disposal.

But none of that modelling can happen without an LCI (Life Cycle Inventory). These are structured tables packed with data on energy use, emissions, water consumption, and more. These tables are the backbone of the research. Without them, the analysis isn't possible.

That made LCI data collection one of the most important, and most time-consuming parts of the job.

The Old Way: A Tangle of Tabs, PDFs, and Manual Work

Here’s how it used to work.

Researchers would begin by casting a wide net, searching academic repositories, downloading 25–50 publications at a time. From there, it was a manual sprint: scanning full-text PDFs, flipping between pages, looking for key terms, and hoping the tables weren’t embedded as unsearchable images.

Even once they located the right data, they’d still have to manually extract the tables often by retyping them or copy-pasting, then the reformatting begins so that they could use the information. Often times, researchers would then need to organize the content by region, study type, material ect.

This process would then be repeated over and over to build a full and accurate dataset.

Once the dataset was complete the work of analyzing and modelling began.

And the deeper the research went, the slower the process got. This didn't only present as an inconvenience, it presented a major limitation. It held teams back from scaling their work, expanding scope, or exploring more complex comparisons. The data was there but it was trapped behind a wall of repetition.

Key Take-Aways:

• Previous practices were time consuming and tedious
• They only allow you to search one repository at a time
• Researchers need to filter through hundreds of publications
• Data and data tables found need to be manually transcribed

Solving the Research Bottleneck: The Story Behind Datahunter’s Evolution

Datahunter proves a target use-case of AI in the public sector: discovering valuable scientific data and automating workflows.

It wasn’t spun up in a product sprint or brainstormed in a boardroom. Datahunter was developed right alongside teams doing the work. It was designed to solve real data bottlenecks in real research settings.

Powered by Apption & Apption Labs

Apption is an Ottawa-based software firm with 20 years of experience, who have done hundreds of projects delivering high-impact data and AI solutions to the Canadian public sector and private industry. We are a team of data-engineers, data scientists, and developers that design scalable, secure analytics and AI infrastructure and build operational tools that turn complex data into actionable insights. Our secret-cleared team is equipped to handle Protected B data and is actively engaged with several federal government departments.

At Apption, we’ve always believed the best solutions are built collaboratively. That’s why we created an incubator we call Apption Labs. 

"Apption Labs is something that's core to our DNA as a small company, that alongside client projects we're consistently investing in research and development and working to commercialize software products. We're firm believers in trying to build and innovate in Canada,  and we have a track-record of success with this approach. " - Erik Putrycz, CTO at Apption

Datahunter was originally spun out of machine learning IP built during a customer engagement project and has been used with the Ontario Ministry of Transportation (MTO), and several other leading customers. Our team piloted Datahunter with Canada Forest Service at Natural Resources Canada (NRCan), where the goal was to improve scientific data asset discovery by automating metadata tagging and governance. That early work gave us our first glimpse into how messy, disconnected, and difficult it was to find and leverage structured research data.

We wanted to offer an integrated solution that was able to do more than pre-existing AI tools. Datahunter was built specifically to unlock valuable data from both internal and external documents. 

Unlike other AI powered scientific research tools, Datahunter allows full text downloads, searches of multiple large online repositories at once, and surfaces relevant tables and context from the full-text of documents so none of the important information you need gets missed.

The NRC Pilot: From Experiment to Impact

When we started working with the National Research Council (NRC), with Datahunter in hand we were incredibly excited to showcase our document AI intelligence expertise. We were determined to show how Datahunter could improve their current workflows and unlock government efficiency.

Datahunter was used in practice for the scanning of large volumes of literature, extracting clean data tables, organizing results for analysis, and doing it all inside secure, government-compliant environments. No more endless searching. Just structured, ready-to-use information.

“In my opinion this tool represents a leading advancement in the application of AI and automation to literature review. With Datahunter, our team observed significant time savings —reducing the effort per publication from nearly one hour to just five minutes to validate data.” -Cyrille D.C., Senior Research Officer, National Research Council

New Research Workflow with Datahunter:

The results spoke for themselves, the difference was immediate. Researchers were saving up to 92% of their time just on data search and extraction. There was no more need for digging through PDFs or reformatting messy tables. The new workflow was simple, repeatable, and actually fit the way government researchers worked. Most importantly, it gave them back time to do what they signed up for in the first place which was to analyze, explore, and push their research forward.

Key Features: 

• Centralized access to 60 million open-access publications
• API integration with paid repositories such as Elsevier
• Bulk download and analysis of scientific publications
• Automatic extraction of relevant data

From Side Project to Scalable Platform

What started as a side initiative in Apption Labs, evolved step by step into a production-ready platform now being piloted by labs, policy teams, and data science units across the country.

Every version of Datahunter carries something we’ve learned from public sector teams: how to balance security with usability, how to integrate into real workflows, how to make sure no good research is ever slowed down by inaccessible data.

That’s what makes Datahunter different.

What’s Next for Datahunter: Expanding Discovery Across Research Fields

The NRC pilot proved an incredible success that acts as a pivotal step in purpose driven innovation.

Datahunter is already being explored across departments and disciplines, each with their own complex research needs and workflows. But the one thing they share? A growing need for tools that help teams focus less on searching and more on solving.

We’re actively applying Datahunter to other research sectors in various capacities. For example, healthcare and biomedical research, where structured data can help accelerate literature reviews and meta-analyses. For use in energy and infrastructure, where reliable extraction feeds into scenario planning and sustainability models. Potential innovations in due diligence and defence, essential for informed decision-making. And of course, continuing to support environment and climate science, where our work with the NRC is just the tip of the iceberg.

For government teams Datahunter is easy to integrate to showcase the value of AI. The solution is procurable through our PSPC standing offer. That means your team can pilot, validate, and scale faster with low risk and real results.

The direction we’re headed has always been shaped by the people we build with. So if your work depends on data and your time is too valuable to waste, consider Datahunter to help reduce your manual workload and improve the effectiveness of your data.

Key Take-Aways of Datahunter's Versatility Across Research Settings:

1. Increased Efficiency - Automates tedious data prep so researchers can focus on analysis and innovation.
2. Improved Data Accessibility - Centralizes and structures critical datasets, making the easier to find, query and trust.
3. Security and Scalability - Built for Protected B Research Environments, ideal for high-impact public sector spaces.
4. Procurement Ready - easy to implement with a standing offer for procurement.

Curious to See How your Team Fits in?

If your team is buried in documents, juggling deadlines, or spending more time finding data than using it, we’d love to help.

Have questions or want to explore a pilot?

Reach out to us at [email protected]

Learn more about Datahunter and Apption Labs at

www.apption.com

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