Canada's open-weight model lab. We train, quantize, and deploy sovereign AI on Canadian Blackwell silicon — for the regulated industries that can't run on someone else's API.
Public capital is flowing through every layer of the Canadian AI stack — data centres, compute subsidies, defence platforms, sovereign cloud. The one layer that's empty is the one where the IP actually lives.
Canada is investing $2 billion in sovereign AI. No open, sovereign model lab exists.
Every dollar today flows through closed APIs, telco-hosted metal, or thin wrapper layers. Cohere open-weighted Command A+ in May — a Canadian foundation worth building on. The layer above it — vertical-specialized, audited, customer-owned — is still unoccupied.
We're not a wrapper. Not a hosting platform. Not a fine-tuning API. We take frontier open base models and produce shippable, quantized, sovereign deployments — with the IP and audit evidence customers actually own.
Post-training on open base models. SFT, DPO, GRPO, RLAIF. Customer data stays sovereign throughout the run.
Production W4A16, NVFP4, MXFP4 recipes — with MTP draft heads preserved so speculative decoding survives end-to-end. Patches land upstream in vLLM and llm-compressor.
Air-gapped, on-prem, sovereign cloud. With audit trails, eval evidence, and model risk documentation packaged with the build.
We're starting wide. The verticals will narrow themselves as contracts land — not as we pretend to know. Each one has a clear buyer set, a clear corpus, and a clear wedge against frontier general models.
Hallucination floors below frontier models on Canadian case law.
On-prem deployable, PHIPA/PIPEDA-clean, evidence-cited outputs.
Air-gapped, classified-ready, doctrine-aware, Five Eyes interoperable.
OSFI-aligned, MRM-documented, audit-ready, on Canadian soil.
Sovereign AI in Canada has shifted from policy paper to active deployment in under twelve months. The first lab to ship audited, vertical, open-weight models with defence contracts behind them owns the category.
Engineering notes and release writeups from the lab — recipes, evaluation methodology, and the walls we hit along the way. The same rigor that goes into the model cards, in long form.
Our Canadian legal-reasoning benchmark now has a live leaderboard at canlegalbench.com: 14,547 primary-source records, 17 task types, 14 jurisdictions, 31% in French, five frontier models scored closed-book. Claude Fable 5 leads at 80.1, with GPT-5.6 Sol and Gemini 3.1 Pro close behind. No model clears 60% on citation resolution, citation holdings, bijural equivalence or issue spotting.
DFlash2-G, our third self-trained drafter, is the first to beat the widely-used reference drafter on acceptance, outside the noise band. On 8× H100 our W4A16 quant beats NVIDIA's own NVFP4 checkpoint on the same rig (+5.1% at c256; +47.8% single-stream against NVFP4 run without speculative decoding) and posts the model's first formal vision scores: MMMU 0.747, OCRBench 888. On 2× DGX Spark, NVIDIA's NVFP4 stack is ahead, and we say so.
We trained a DFlash2 block-diffusion speculative-decoding drafter for GLM-5.3-Flash from scratch — 350,260 self-generated samples, no third-party drafter weights or traces, Apache-2.0. It beats our previous best in every same-rig A/B cell on 2× DGX Spark and is now our serving drafter-of-record — with the 2× Spark serving stack open-sourced alongside.
If your organization needs a model it can audit, deploy on-prem, and keep under Canadian jurisdiction — we should talk. We respond to every serious inquiry within 48 hours.
Or email [email protected] · [email protected] directly.