Your firm's engineering knowledge is only useful when someone can find the relevant guidance and apply it correctly. Most of that knowledge lives in internal documents: design guides, compliance checklists, QA procedures, worked examples, the accumulated "how we do it here." It's all there, and it's all locked in material that can't do anything except be read.
Queryable documents behave differently. You bring your own material into the workspace, and your team can ask it questions in plain language: what does our procedure require for this case, which of our internal rules applies here, what does the checklist expect at this stage. The answer comes back grounded in your own document, with a reference to where it came from, so you can check it rather than trust it. And because you're already in the calculation environment, you can move straight from the answer into a live calc that applies it.
Grounded in your document, not invented
The important distinction is that the answer is drawn from the material you brought in, not generated from a model's general impression of what it probably says. That's the difference between something an engineer can actually rely on and a chatbot you have to second-guess. When the answer cites the source, a reviewer can confirm it against the original, which is the only basis on which this kind of help belongs anywhere near engineering work.
Your content stays yours
For anyone whose internal documentation is proprietary, the data handling matters as much as the capability. What you bring into the workspace stays workspace-scoped and is not used to train any model. You're making your own material queryable for your own team, not contributing it to someone else's system. We've written separately about the risks of putting engineering documents into general-purpose AI tools; this is the version that doesn't have those problems.
From answer to applied calculation
The reason this sits inside a calculation platform rather than a standalone search box is the last step. A queryable document that just returns text still leaves you to go and apply it. Here, the answer connects to living logic: you can take the relevant guidance and drop into a calc that already implements it, with the inputs and the result laid out and traceable. Your documentation stops being something you consult and becomes logic you can run.
What's shipped
Bringing in your own documents and getting grounded, cited answers from them is available today, as is AI that reviews a calculation against them. Turning a body of internal documentation into a maintained library of runnable checks is work we do with teams rather than a default. The pattern, though, is the one that matters: your own compliance knowledge, queryable and then applied, in one place, with the source always in view.
If you'd like to see your own compliance documentation made queryable this way, book a call with our team.
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