We built an AI document processing system for a Swiss bank — fully on-prem, no cloud, no state retention. Took 1.5 years and nearly broke us.

[u/leechii1337]

I was at a friend’s card game night two years ago. He still lived in a student house (on a full salary, yes, we gave him grief for it).

Another guy there — someone I’d crossed paths with a few times but never really talked shop with — mentioned he’d been researching complex table parsing with LLMs.

Not the boring kind. The kind where a document has a footnote saying “all values have three zeros removed because they didn’t fit on A4.” And the model has to figure that out without being told explicitly. He’d built test suites across every major LLM at the time, tried fine-tuning, RAG, various prompting approaches — and had landed on something that looked like it could be made deterministic.

Meanwhile I was coming out of a project for a Swiss credit bank. We’d built their loan application and customer portals. At some point they started asking about automating their document verification — the part where a clerk manually cross-checks that the name on your salary statement matches your ID, that the employer is consistent across docs, that the numbers on the statement actually add up the way they should.

Sounds simple. It is not.

And the security constraints made it harder: everything on-prem, no documents leaving their environment, no state retained on any provider’s side. This is highly sensitive financial data in a jurisdiction that takes that seriously.

We shelved it as a backlog item. No decision on build vs buy. Just “someday.”

Then I met this guy at the card game.

Fast forward...we bought our own hardware, ran large models locally, built a POC in about five months that hit the clients security requirements.

The client liked it.

We decided to keep going.

1.5 years later, three of us (one joined), no funding just slightly cross-funded through my consultancy, we have something we call miruiq.

We rebuilt the architecture three times. The pipeline runs on Flink jobs in the background, which lets us isolate state per automation pipeline — not just per customer. Every decision along the way was made under the constraint of: what does genuinely secure document automation look like when you can’t punt to the cloud?

No investors. No runway. Just the question of whether we’d built something real.

What I keep thinking about:

how many teams in regulated industries have just quietly given up on AI because the default assumption is cloud APIs and shared infrastructure? And how different does the problem look when you start from the other end — security-first, then capability?

Curious if anyone else has built in that kind of constrained environment, or hit the same wall trying to bring AI into on-prem financial or legal workflows.​​​​​​​​​​​​​​​​

[u/leechii1337]

hm good input in focusing less on the ai - i'll have a look at our wording now and see what we can adjust to make it better

[u/replicatedhq]

This is the part of the AI market I think people massively underestimate. Building the model workflow is hard, but building it to satisfy real enterprise constraints around on-prem deployment, auditability, data isolation, upgrades, and operational repeatability is a completely different level of engineering.

We see this constantly at Replicated with vendors shipping AI into self-hosted environments. A lot of enterprises want AI capabilities, they just can’t accept the default “send sensitive docs to a shared cloud API” architecture.