Atlanta · AI systems & research engineering
We help mid-sized organizations put private AI to work and collaborate with research teams on demanding machine-learning systems.
Private by designKeep models and sensitive data under deliberate control.
Evidence-ledMeasure progress against reproducible technical criteria.
Built to transferOwn the resulting system, documentation, and decisions.
Local systems
to institutional scale
Research ideas
to reproducible evidence
Two practices
The common thread is engineering discipline: clear constraints, testable claims, and systems that remain useful after the engagement ends.
Private AI infrastructure designed around your data, operational constraints, and path from pilot to production.
Reproducible ML systems and focused engineering collaboration for teams working beyond standard model recipes.
Technical experience
We select technologies for the operating environment and research question—not for novelty alone.
Tune and operate local models with vLLM, llama.cpp, SGLang, and fit-for-purpose serving layers.
Connect models to useful applications through Model Context Protocol, AI gateways, and deliberate cloud on-ramps.
Build reproducible MLflow pipelines, evaluation harnesses, custom CUDA kernels, and PyTorch model components.
Move workloads from a local workstation to H200, GH200, and Blackwell systems with practical SLURM support.
Representative research domain
Protein modeling is one example of our research engineering practice: technically specialized work conducted in service of a collaborator’s scientific question.
From wet-lab context to model. Data preparation, PEFT, custom loss functions, and training implementations.
Interpretation. t-SNE, UMAP, attention heat maps, and gradient-weighted attention rollout.
Model families. Experience spanning AntiBERTy, AntiBinder, ESM-2, MINT, and protein–protein interaction tasks.
How we work
Agree on the decision, benchmark, or operational outcome the work must support.
Test assumptions in a bounded pilot with visible tradeoffs, risks, and measurements.
Deliver documented systems your team can operate, reproduce, and extend.
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