No vendor access to client data
The planned deployment gives Elmden no access to your documents or their contents. Your client data isn’t sent to us for processing.
BUILT IN-HOUSE. PREPARING FOR PUBLIC RELEASE.
Your cloud.
Your client data.
Your control.
Our working in-house prototype extracts and validates sensitive client data with a trained LLM. We’re now preparing it for deployment in your cloud—with processing inside your environment and no access for Elmden.
Working prototype today. Public availability is still ahead.
YOUR CLOUD ACCOUNT NO VENDOR DATA ACCESS
Client intake.pdfILLUSTRATIVE DOCUMENT
CLIENT INFORMATION
CompanyMeridian Studio
Client referenceC-1042
Reporting periodOctober 2026
One detail worth a lookREVIEW
The client reference differs between documents.
C-1042C-1402
Documents → your model → resultsConcept preview
We provide the product. You control the data.
01 / No vendor access02 / Model runs with you03 / Customer control
LESS CROSS-CHECKING. MORE CLARITY.
Let the documents
check in with each other.
Extract the information. Apply your rules.
Compare related documents.
All within the environment you control.
CROSS-DOCUMENT CHECK
Same client. Different references.
Client intake.pdf · page 1Client referenceC-1042
Supporting document.pdf · page 2Client referenceC-1402
Needs your review
The references don’t match. Check the source documents before proceeding.
CONTROL IS THE FOUNDATION
Our product.
Your environment.
We’re turning our in-house prototype into software you can deploy and operate in your own cloud account. The documents, the model, and the results stay on your side.
The trained model runs with you
The LLM runs inside your environment. Documents, extracted data, prompts, and results stay there, without being sent to an external AI service.
You decide who can see what
You control the deployment, access permissions, and data retention. Your team reviews the findings and decides what happens next.
THE STORY BEHIND THE BUILD
Working in-house.
Getting ready to go further.
Getting ready to go further.
Built and working in-house.
Now preparing to share it.
Our starting point was a practical requirement: use AI to check sensitive client documents while keeping the data inside our own environment.
That work became a functioning in-house prototype. A trained model extracts information from our documents, checks it against our requirements and related files, and brings exceptions back to our team.
The prototype exists. Making it ready for other teams is the next step: packaging deployment, writing documentation, and testing beyond our own setup. We’re doing that work before a public release, so you can run the product in your cloud and keep control of your clients’ data.
The Elmden teamFrom an internal prototype to a public product.A LITTLE MORE CONTEXT
Good questions.
Can I use it today?
A working prototype already exists as our in-house development. It isn’t publicly available yet. We’re preparing it for other teams to deploy and use; following the build lets you hear about availability when we’re ready.
What still needs to happen before release?
We need to turn the internal setup into a product other teams can deploy: prepare installation and configuration, document requirements, and test across supported customer environments. Pricing, licensing, and the release date are still to be decided.
What do you mean by “validation”?
Checking extracted information against requirements you define and related documents: for example, a missing field, conflicting reference, or date outside an expected period. It doesn’t establish that a document is authentic or that its contents are true.
Can we deploy it in our own cloud?
Yes—that’s the deployment model we’re preparing for the public product. The software and trained LLM run in your cloud account, under your administration. Supported cloud environments, hardware requirements, and installation details will be shared before release.
Will Elmden or another AI provider see our data?
The architecture we’re developing keeps document processing entirely in your environment. Elmden has no access to client documents, and document contents, prompts, extracted data, and results aren’t sent to us or an external AI provider. This is a product requirement; the public release is still in development.
Does “your control” include access and retention?
Yes. The intended deployment puts your team in charge of access permissions, storage, and retention. You also control the cloud account and its security configuration, including the network rules that keep processing inside your environment.
How much will it cost?
Pricing and licensing are still being shaped. We’re learning which workflows matter most before deciding how to package the product. Following the build doesn’t commit you to a purchase.
WORKING PROTOTYPE · PUBLIC RELEASE AHEAD
Bring document AI
into your environment.
Follow our progress from working prototype to public release. Tell us what your team needs to keep in its control.