Deep learning that runs on your DGX.

Objection 01

“Have you actually shipped a transformer / GNN / physics-informed net in production, or only fine-tuned an off-the-shelf model? Show me the architectures and the validation evidence.”

Public commits, pre-prints, and conference talks for every Brocode applied-research engineer are linked from their individual page on the site. Every engagement closes with a Reproducible Research Pack: containerised training, fixed seeds, documented validation protocol, hold-out metrics, ablation table, and a model card co-signed by the Brocode lead and the client SME. The same engineer presents it to your science committee.

Proof: anonymised ADNOC-tier reference - 3D seismic interpretation U-Net retrained on a proprietary subsurface volume, 87% horizon-picking agreement with the senior interpreter, deployed on the internal DGX cluster.

Objection 02

“Our cluster is air-gapped. Your team will have to work on-site, badge in, and never take code or data out. Are you set up for that operating model?”

Yes. Our default delivery mode for energy, defence-adjacent, and life-sciences clients is on-site, on the client's badge, with no code or data leaving the perimeter. The pod commissions the DGX, H100, or H200 SuperPOD inside your network, configures the training stack with your security operations team, and writes every commit into your repos. Brocode engineers are cleared for the relevant security classifications on a per-engagement basis.

Proof: anonymised TII-adjacent reference - physics-informed neural net for transient turbine thermo-mechanics, 60x speed-up over the legacy FEM solver at 4.2% MAE, the full programme delivered on-site on an air-gapped cluster.

Objection 03

“We have worked with a university group before. The papers are good but nothing runs in production. How do you avoid that failure mode?”

Different operating model. The university partnership is calibrated to a publication; the Brocode pod is calibrated to a working system on your cluster, with a sign-off from your domain SME against the validation protocol. The two are complementary - we routinely co-publish with the same academic groups. The Reproducibility Checklist we share lets you score any vendor or partner against the same bar before you commit.

Proof: anonymised Mubadala-portfolio biotech reference - graph neural net for ADMET property prediction, integrated into the medicinal-chemistry team's daily triage workflow within 14 weeks, with a joint pre-print authored alongside the client's computational chemistry lead.