This week our demo videos were generated on an NVIDIA H100 rented from RunPod. In the rental log, the pod's region is called CA-MTL-1. Montréal. The machine that made our pictures was running in my own city.
That was not the plan. I wanted a 48 GB GPU at DigitalOcean, in Toronto, where our databases already live. There was never one in stock in that region, the only one our private network reaches. So we took the H100 in Montréal. Looking at that region name, it struck me as a good place to start explaining what we do.
First, who is writing
My name is Oussama. I'm a Java and Spring engineer, and I founded Krizaka in Montréal. I build it mostly on my own. A large part of the code is written with AI agents that I direct, review and correct. That matters for what follows: when you work with machines that write, you quickly get picky about who did what, and where.
Sovereign, in one sentence
The word gets used for everything. Here it means one precise thing: a customer can know where their document was processed, which model read it, and who can see the result.
Think of a notary. You hand over a deed. You know which office keeps it, who signed it, and there is a register. If someone disputes it, you open the register. Sovereign AI is the same thing for a document that a machine has read.
In practice, every analysis is a run of Orazaka, the engine I'm building. The run keeps its id, the version of the processing that ran, and the rules it applied. The model it used is a row in a database, not a name buried in the code. A product built on Orazaka never calls a model on its own: it goes through the engine, which keeps the record.
None of this makes data private by magic, or the model any smarter. It lets you check, and that's all. A promise you cannot check is marketing.
How this was made
- Model
- Wan 2.2 T2V A14B · Apache-2.0
- Engine
- hosted: DigitalOcean serverless inference (steps and guidance not exposed)
- Machine
- hosted by the provider (not our machine)
- Through
- orazaka video --model wan2-2-t2v-a14b, routed by Orazaka
- Prompt
Winter night in Montréal, slow tracking shot along a snowy street of the Plateau with outside spiral staircases, warm windows glowing, snowflakes in streetlight, steam from a vent, cinematic 35mm, shallow depth of field. No text, no lettering, no logos.
- Seed
20261011- Size
- 832×480 · 16 fps, 5.06 s
- Time
- 132 s, as recorded by the run
- Cost
- US$0.60 (the provider's price)
- Orazaka run
0515ce61-0e91-4fe9-a0bf-2ca2838f74e3- Sound
- music bed made by Orazaka (Studio video-sound, ACE-Step 1.5, MIT), -14 LUFS, AAC
Why here
The law I read first is Quebec's: the Civil Code, and Law 25 on personal information. A Quebec lease, an employment contract, an invoice from a local firm: those are the documents our first tools have to understand. And the people who will have to trust the result live here too. I'd rather build a tool that reads a Quebec lease knowing I'll have to live with its answer myself.
Then there's language. Every Krizaka site ships in French and English on the same day. So does this blog: I write in French first, then make the English version.
What it forces me to do
Choosing to be checkable has a price, and I'd rather say so.
A result without a source doesn't show up. When a tool flags a problem in a document, it cites the rule and the exact passage. If it can't cite, it stays quiet. That is frustrating at first, because a model always has an opinion. But an opinion without a source is worth nothing in front of a client.
I keep the infrastructure small. No service I can't explain, no big machine left on for nothing. I rent a GPU when there is work and switch it off when the queue is empty. I learned that rule by paying for it, and the story is in the post about costs.
I publish the code of the foundations. The interface kit (Krizaka UI) and the Spring Boot building blocks (Krizaka Java) are open source. Anyone can read what our products stand on.
And I say what isn't finished. Several products are marked "in development": sign-up is closed, and their sites say so.
What isn't solved
Sovereignty of the model is still the hard part. Running a good model on a machine you control costs real GPU hours and real dollars. The Montréal H100 is US$3.99 an hour. I measure all of it and publish the numbers.
A rented machine isn't an owned machine. I know where our data is, but I don't build data centres. The region of a pod is something I can tell a customer. A contract with that provider is something else.
Last, our rules for reading Quebec leases are still waiting for a lawyer's review. The reports say so in plain words.
What you can take away
If you're evaluating an AI tool for your documents, ask it three questions. Where is my document processed? Which model read it? Who can see the result? If the answer is vague, or nobody can show you a trace, you have your answer.
This blog follows the same rule. Short posts, with the numbers I can show and the runs that produced them. If a claim doesn't lead back to a measurement, a run or a line of code, I don't write it.
