AI research for engineers: 200 papers in minutes

How engineers work with AI: From 200 papers to an answer in minutes

Jonas has four days left. By Friday, he needs to finalize the material selection for a new valve housing, complete with a justification that will hold up under client scrutiny. On Monday morning, his search for studies and test reports on the corrosion resistance of duplex stainless steels under fluctuating chloride exposure yields over 200 results: technical articles, patents, and internal test reports from three previous projects. By Monday evening, he has skimmed the abstracts of maybe thirty of them.

This is what industrial research looks like in practice. It is not an academic exercise, but a bottleneck before every design approval, every specification review, and every patent application. And the bottleneck is tightening: back in 2022, according to WordsRated , around 5.14 million scientific articles were published worldwide, and the trend is rising. On top of that, there are patent specifications, standards, internal test reports, and supplier documentation that do not appear in any public database.

Anyone who thinks this is only a problem for university research departments underestimates how much traditional literature review is involved in engineering, quality assurance, and product development today. The only difference is that engineers do not have six months for a systematic review. They usually only have until Friday.

Why research is becoming a bottleneck

The problem is not new; it has simply intensified. According to a CADENAS study, which surveyed over 100,000 engineers and designers, nearly half of them spend at least an hour every day just searching for the right components. Across all knowledge workers, according to the Coveo Workplace Relevance Report 2022 , an average of 3.6 hours per day is spent searching for information—an increase of one hour compared to the previous year. And according to Atlassian's "State of Teams" study, cited by Papershift, 56 percent of respondents regularly have to ask colleagues or schedule an extra meeting because they cannot find the information they need themselves.

In a classic systematic literature review, which would be the standard in research and development, the methodology alone takes between six and eighteen months, according to Tulane University . This includes screening abstracts, full-text review, cross-referencing, and documenting exclusion criteria. No mechanical engineering project plan allocates time for this. So, shortcuts are taken: you read what you can in the time available, ask the colleague who "knows about it," and hope that the most important source isn't hidden among the remaining, unreviewed results.

The risk here is not abstract. An overlooked publication on a failure mechanism, a patent that already covers the planned solution, or a test report from a previous project that warned against this exact material. In the worst case, all of this costs more than just time—it leads to rework loops, a failed component, or a patent infringement lawsuit.

From 200 papers to an answer in minutes: how it works in practice

The approach we built MAIA for reverses the order. Instead of loading documents only when a question is asked, MAIA analyzes the entire repository in advance, even before a question is raised. The platform identifies entities, technical terms, relationships between documents, and version statuses, building an index that understands that a test report should be read differently than a patent claim, even if both are provided as PDFs.

For Jonas, this would look like this in practice: Instead of opening 200 search results one by one, he uploads them all to MAIA at once—technical articles, patents, internal reports. MAIA analyzes the entire collection in the background. Jonas then asks his actual question in plain language: "Which duplex stainless steels show no pitting corrosion under varying chloride exposure over 5,000 operating hours, and are there any internal test reports that contradict this?" The answer doesn't come as a vague summary, but with references to the source, page, and document version. Verifiable, not just plausible.

In just a few minutes, over 200 search results are turned into a reliable, source-backed answer. This isn't because the underlying language model is smarter than ChatGPT, but because MAIA has already done the groundwork before the first question is even asked.

Where AI-powered research has the greatest impact in engineering

In practice, this need arises at several points in the product development process, not just during standard literature research:

Material and technology selection. Before a design decision is made, all relevant publications, patents, and internal test series regarding a material or process should be considered—not just the ones the responsible engineer happens to know.

Specification and standards compliance. Manually checking a requirements specification with hundreds of items against internal documentation and current standards takes days. With an AI that searches both document worlds simultaneously, this becomes an auditable overview in minutes rather than hours.

Patent and competitor research. Before development resources are invested in a solution, it is worth checking whether it has already been patented or if a competitor has long since included it in their data sheets. Searching through hundreds of patent specifications and product data sheets is a task that is almost impossible for humans to perform manually.

Root cause analysis of past projects. Why was a certain solution rejected three years ago? Which workaround worked for the client in Brazil? This knowledge is rarely found in a neatly maintained wiki; it is scattered across project folders, emails, and test reports that an AI with document intelligence can cross-reference, but a human hardly can.

What matters when choosing a research tool

Traceability down to the page and version. An answer without a source citation is worthless in the industry because it cannot be audited. Demand that every statement can be traced back to the original document.

Understanding of document types. A tool that treats a bill of materials like continuous text will hit its limits with technical documentation. Good systems distinguish between standards, test reports, patents, and engineering drawings.

Scaling beyond the context window. Ask specifically how many documents the tool can process simultaneously, not just how quickly it can answer questions about a single file.

Data location and confidentiality. Material data, design details, and unpublished research findings are among an industrial company's most sensitive information. A tool that uses this data to train further models should be out of the question for this purpose.

How MAIA solves this challenge

We built MAIA specifically for this problem. Before you ask your first question, MAIA performs a deep analysis of all uploaded papers, patents, standards, and internal documents, identifying connections across hundreds of files that would be missed during manual reading. The High Precision Mode breaks every answer down into individual statements and verifies each one against the original source before displaying it—traceable down to the page and document version. And because MAIA is operated in Germany and Switzerland, your research findings and internal reports stay exactly where they belong: with you. Your data is never used for training.

For engineers like Jonas, this ultimately means: 200 search results no longer equal a week of lost time, but rather a reliable answer developed with MAIA by Monday afternoon—with plenty of time to double-check it before Friday.

If you would like to see how this works with your own technical documentation: Schedule a free demo.