The Stacks We Build Real Products With
Four technology tracks — our own AI infrastructure, full-stack web, ML frameworks, and MLOps — each with the tools we use and what we typically build with them.
In-house flagship compute, not shared cloud capacity
Day-to-day development, fine-tuning, and inference run on our own dedicated GPU infrastructure — including an NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip in-house, with partner and cloud infrastructure available for the largest multi-node training runs.
Our AI Lab
Superchip Interconnect
CUDA & Runtime
Scale-Up Compute (Partner/Cloud)
Inference Serving
What we build with this stack
- In-house NVIDIA GB300 Grace Blackwell Ultra Superchip — 748GB of coherent memory and up to 20 PFLOPS of AI performance, on our own hardware
- MIG support partitions our GPUs into up to seven isolated instances for secure, flexible multi-project usage
- Partner and cloud access to NVIDIA H100/H200-class GPUs for multi-node training runs beyond in-house capacity
- Full data sovereignty maintained even when scaling into partner infrastructure — orchestration and monitoring stay with us
End-to-end web application development
The same stack behind most of our custom software and SaaS builds — chosen for maintainability and hiring depth, not novelty.
Frontend
Backend
Data
Infrastructure
What we build with this stack
- Custom SaaS products, from auth and billing to admin tooling
- Internal operational tools and dashboards
- API design and third-party systems integration
- CI/CD pipeline setup for continuous, low-risk deployment
Model training and fine-tuning frameworks
The frameworks behind our fine-tuning methodology — full training, parameter-efficient tuning, and everything needed to get a model from base weights to production.
Core Frameworks
Fine-Tuning & PEFT
Alignment
Evaluation
What we build with this stack
- Full fine-tuning and parameter-efficient tuning (LoRA/QLoRA) on open and proprietary base models
- RLHF pipelines for alignment and behavior tuning
- Distributed training with DeepSpeed for large-parameter models
- Systematic evaluation and benchmarking before a model ships
Operating and monitoring models after they ship
Deployed on our own infrastructure by default; cloud ML platforms (SageMaker, Vertex AI, Azure ML) are available as an option only when a client's environment specifically requires it, not our default path.
Orchestration
Monitoring
Data Pipeline
Cloud (When Required)
What we build with this stack
- Containerized model serving with Kubernetes across our own hardware or a client's
- Drift detection and monitoring dashboards for every deployed model
- Data pipeline orchestration feeding training and evaluation sets
- Cloud deployment on SageMaker, Vertex AI, or Azure ML when a client's own environment requires it