5 Use Cases of CoLab’s Operator for Mechanical Engineers

Operator is CoLab’s AI interface for searching engineering data, performing targeted design analysis, and generating engineering artifacts. It can also draw on specialized engineering agents as part of those workflows. For mechanical engineers, that can mean using the same product context to choose a component, find relevant feedback from previous programs, analyze a drawing, prepare a DFMEA brainstorm, build an inspection plan, and much more.

Rather than look at those as five separate features, let’s imagine we’re building a product together and walk through these use cases step by step.

Say we’re developing a new electric pressure washer. I have the pump assembly drawing open in CoLab, along with our company standards, design guidelines, review history, and other information our team has accumulated over previous programs. The design is taking shape, but there are still a number of decisions to make before we can bring in senior decision-makers.

Here’s where I might use Operator to help streamline my work.

1. How can Operator help with component selection?

Let’s start with a seal in the pump assembly.

I know the conditions the seal needs to handle and I have the geometry in front of me, but choosing a component still means checking several things. What does our company standard recommend? Does the existing geometry accommodate that option? Do we already have an approved component that meets the requirements?

I can ask Operator to bring those sources together. It can analyze the drawing, search the relevant standards and guidelines, and reference internal component information so I can evaluate the various options and tradeoffs against this specific design.

That is more valuable than simply asking an AI model which seal materials are commonly used in pressure washers. General engineering knowledge may get me started, but my decision depends on the current design and the information my organization uses to make that decision.

It is also a simple example of why engineering data readiness for AI has to be considered one use case at a time. Operator can only bring a standard or approved component into the analysis if that information exists in a form the system can interpret.

Below is a video showing a related example, where an engineer is using Operator to choose the right gasket for a design.

‍

2. Can Operator find relevant feedback from previous designs?

Before the pump assembly goes much further, I also want to know whether we have dealt with similar issues before. This is especially helpful if I’m new to a team or program and need to get up to speed quickly.

Perhaps earlier reviews surfaced problems involving leakage, fastener access, tolerance choices, assembly clearance, drawing notes, or supplier requirements. I could search old files and reviews individually, but that takes a lot of time and I may not find everything I need. Alternatively, I can also ask Operator to search that history and bring forward feedback related to the design that’s right in front of me.

If I broaden the search, Operator can analyze larger volumes of review feedback for recurring themes. Maybe the same drawing issue has been raised several times across different programs. At that point, I am no longer looking at an isolated comment. I have information that could justify a new review criterion, checklist item, or standard.

This example connects directly to a principle we cover in our engineering design review best practices. When review feedback is captured and made searchable, teams can use it to spot recurring issues and avoid repeating the same mistakes on future programs.

3. Can Operator run an AI drawing review?

First drafts of anything are usually far from perfect, and CAD or engineering drawings are no different. There could be some basic design errors that I missed that I should clean up before sending the design to the wider review team.

If I have a specific question about the drawing, Operator can perform targeted analysis using the design and the engineering information available to it. That is different from AutoReview, CoLab’s specialized AI agent for drawing and CAD review.

AutoReview is proactive and repeatable. It runs defined checks based on review criteria, checklists, and guidelines. Depending on the checks being run, AutoReview can examine areas such as drawing completeness, dimensions and tolerances, GD&T, BOM information, company standards, and other repeatable review criteria.

Operator, by comparison, is targeted and flexible. If AutoReview flags something that needs more investigation, I can use Operator to find the applicable standard, search previous review feedback for a similar issue, or analyze other information associated with the design before deciding whether anything needs to change.

For a closer look at what that first pass involves, our AutoReview drawing-review demo walks through the process on an engineering drawing.

4. How can Operator support DFMEA preparation?

Now let’s move into the design failure mode and effects analysis, or DFMEA. This is where we can identify how the pressure washer could fail, consider the causes and consequences of those failures, and decide which risks need to be addressed before the design is released.

If we begin with a blank brainstorming meeting, the quality of the discussion depends heavily on who is in the meeting and the implicit engineering knowledge they bring to the table. With Operator, I can bring more of the relevant engineering knowledge that has already been captured across standards, previous reviews, lessons learned, and program history into the session before we start evaluating failure modes.

For this pressure washer, I might ask for previous feedback, lessons, standards, or failure modes related to sealing, component fit, assembly, vibration, environmental exposure, or similar products. Operator can use that information to generate an initial DFMEA or structured brainstorm, giving the team something concrete to evaluate instead of starting from a blank template.

That doesn’t mean Operator is producing a finished DFMEA for us to accept without review. The engineering team still needs to determine which failure modes apply, how they should be evaluated, what the tradeoffs are, and whether the design needs to change. SMEs can then refine and improve the artifact in CoLab.

That division of responsibility is central to how we think about AI agents for engineering design. The agent can search, analyze, check, or prepare information, but true engineering judgment remains with the people responsible for the product.

The video below shows how you can use Operator to help with your next DFMEA analysis.
‍

‍

5. Can Operator create an inspection plan from a drawing?

By this point, the pump geometry has largely been worked out in CAD. But before the part can move toward manufacturing, that geometry has to be translated into an engineering drawing that communicates how the part should be made and inspected. That means adding the dimensions, tolerances, material requirements, surface finishes, and other information that the manufacturing and quality teams will rely on.

Once the drawing is in place, the next step is figuring out how those requirements will be verified. That is where the inspection plan comes in.

Normally, that involves going through the drawing feature by feature, identifying what needs to be measured, matching those characteristics with available inspection methods or equipment, and putting everything into a sensible sequence.

Operator can perform much of that initial analysis from the drawing and the inspection information available to it.  I can then organize the proposed plan in a Canvas and refine it with the people responsible for quality and manufacturing.

As we’ve noted elsewhere, the inspection strategy still belongs to the engineer or quality specialist. Operator can give us a structured first pass to review instead of having to reconstruct the entire plan manually. The engineering decisions and validation still belong to us, while Operator helps assemble the design context and produce a useful first version faster.

How is Operator different from ChatGPT or a PLM copilot?

Across these five use cases, there are several points where a large language model or a PLM copilot could help.

LLMs like ChatGPT can analyze files, explain engineering concepts, and, depending on how it is deployed, retrieve information from connected company sources. Meanwhile, PLM copilots are increasingly capable of searching product structures, BOMs, documents, revisions, and change information inside systems such as Teamcenter or Windchill.

The pressure-washer example crosses several boundaries, though. We have used information from a drawing, internal standards, approved components, previous review feedback, specialized design review agents, previous failure modes, and inspection resources. Some of that information may live in the PLM, but much of it may not. Similarly, a general-purpose LLM will not necessarily have access to the engineering knowledge your team has captured through past designs, review conversations, supplier feedback, tradeoffs, and decisions.

Operator is designed to bring those sources and capabilities together around the engineering workflow in front of you. It complements the product record in PLM by helping engineers search engineering data, perform targeted analysis, and generate engineering artifacts, while drawing on specialized engineering agents where the workflow calls for them.

Our guide to AI-driven PLM workflows goes deeper on where PLM-native AI fits and where design review and engineering decision-making extend beyond the system of record.

What makes a good Operator use case?

The strongest Operator use cases tend to have something in common. Firstly, it typically involves an engineer trying to make or prepare for a decision, but the information needed to do that is spread across several sources or requires more than one type of analysis.

Component selection is one illustrative example, and DFMEA preparation is another. So are drawing review, revision analysis, inspection planning, standards lookup, and searching previous feedback.

In each case, the goal isn’t to offload engineering judgment to AI. The output still has to be useful enough to support a real engineering workflow, which means grounding it in reliable inputs such as company standards, approved parts, design data, and lessons captured from previous programs. Engineers and SMEs remain responsible for validating the result and deciding what action, if any, the design requires.

That is also where Operator connects to the broader value of running design reviews in CoLab. As teams capture feedback, decisions, standards, and program context over time, they build a growing body of engineering knowledge that can be reused on future designs. Operator gives engineers an intuitive interface for accessing that knowledge when they need it.

That points to a more practical role for AI in engineering. AI shouldn’t do an engineer’s job for them, but instead should reduce the searching, cross-referencing, first-pass analysis, and preparation work that surrounds many engineering decisions.

“Enablement, not replacement” is also a practical way to evaluate the broader category. Our guide to the best AI tools for mechanical engineers looks at the different roles AI can play across engineering knowledge, CAD, simulation, design review, and PLM. 

If you want to see how all of this translates to your own products and processes, you can book a demo with one of our engineers. Bring a real use case or engineering problem you’re trying to solve, and we can walk through how CoLab and Operator would approach it using representative CAD, drawings, and engineering data.

‍