Interview: How instro is Bringing AI to Technical Documentation

In Issue 7 of Lab Horizons digital edition we caught up with Jonathan Rogers the co-founder of Instro which is a service bringing AI to the complex world of technical documentation.

Can you share the story behind Instro and what motivated your team to develop AI tools specifically for scientific and engineering environments? 

Instro came about because of a challenge I faced personally. When I was working with complex systems in Higher Education, I often had to deal with long, technical manuals that were tough to navigate. I built my first AI system to solve that problem—it could read through the documentation for me and provide clear, straightforward answers in plain language. It saved me so much time and frustration. 

After seeing how useful that was, it got me thinking: there must be other industries dealing with a large amount of complicated, hard-to-search documentation. Scientific and engineering environments stood out as areas where this kind of tool could really make a difference. That’s what led Phil and me to develop Instro, as a way to make life easier for people working in these highly technical fields. 

What are the key challenges that scientists and engineers face in managing data and documentation, and how does Instro’s AI address these pain points? 

One of the biggest challenges we’ve seen is knowledge management. Many companies are fortunate to have incredibly skilled and experienced staff who end up becoming the go-to source of information and training for newer or less experienced colleagues. While the same information might exist in documentation—such as PDFs, site visit reports, or manuals—these can be difficult to search or navigate efficiently. Let’s face it, it’s often quicker and easier to ask someone directly. 

That’s where Instro comes in. By providing clear, plain-language responses, Instro allows teams to search across a company’s documentation—manuals, reports, and more—using a conversational, question-and-answer format. It’s like having an expert on call who can instantly pull up exactly what you need. 

Another challenge we’ve noticed is language barriers. Sometimes, documentation is written in a language that not all staff are fluent in, which can make it even harder to access the information they need. With Instro, staff can ask questions in their preferred language, and the AI translates the answers from the documentation into that language. It bridges the gap and ensures everyone can access the same information, no matter their language skills. 

Could you describe some of the most interesting or impactful use cases you’ve seen for Instro in laboratory or engineering settings? 

One of the most impactful use cases we’ve seen is how Instro helps companies with non-English-speaking workforces navigate complex English-language documentation. By combining instant subject matter expertise with on-the-fly language translation, Instro bridges the language barrier, ensuring all team members—regardless of their language proficiency—can access critical information and operate confidently. 

Another example is the way Instro empowers junior staff to take on more responsibility. With access to a comprehensive knowledge base, they can solve problems and answer questions on their own, without needing to constantly rely on senior colleagues. This not only helps newer staff grow but also frees up senior team members to focus on higher-priority tasks, improving efficiency across the board. 

How does Instro integrate with existing laboratory and engineering document systems to enhance workflows and ensure clear data management?

You can read Jonathan’s answer to this question and the rest of his interview in Lab Horizons Issue 7. Read it for free now.