Sovereign models, built machines, and two things you can use today.
Most of our work is one of three things: fine-tuning a model on a corpus that is not allowed to leave a building, building the machine it runs on, or both. Underneath that are two products you can go and use right now without talking to anybody. All of it was trained and benchmarked on the machines in Troy.
Live, and free to try.
ToxScreen — drug safety AI
Predicts hERG cardiac channel blockade and cytochrome P450 metabolic toxicity from a SMILES string in seconds. Built on the ADMET-AI multi-task architecture, with calibrated uncertainty quantification added — every prediction carries a confidence score rather than a bare label, which matters when you are deciding what to make next.
Built for people screening compound libraries for cardiotoxicity early, before the expensive part of the pipeline. CYP3A4 and CYP2D6 isoform-specific models are in progress.
Lithic — AI-assisted CNC design
Describe a mechanical part in plain English. Lithic generates CAD geometry as STEP, CAM toolpaths, G-code, and a manufacturing report with estimated cycle time and material utilisation.
Built for small and mid-size shops that need quick turnaround without a full-time CAD/CAM specialist on staff. 3-axis milling, with a tool library you can customise.
Work that starts with a conversation.
Fine-tuning on your own data
We handle curation, prompt formatting, training (LoRA, QLoRA or full-weight), and building an eval harness that measures the thing you actually care about rather than the thing that is easy to measure. Models come back as vLLM-compatible checkpoints, GGUF files or ONNX exports.
Models that stay in your building
For groups who cannot send data to a cloud API at all. We fine-tune and deploy entirely on-premise, on your hardware or ours. Nothing leaves the machine: no telemetry, no third-party API in the loop.
The usual reason is not preference. It is a contract, a regulation or a statute: ITAR and EAR on export-controlled technical data, HIPAA on patient records, CUI under a DFARS clause, attorney-client privilege that a third-party disclosure would waive, or an NDA with a customer whose process data you are not allowed to transmit anywhere. In those cases “we do not train on your data” from a vendor is not the same thing as the data never having left.
Model families: Qwen3, Llama 3/4, Mistral, DeepSeek, Phi-4, Gemma 3. Quantisation to INT8/INT4/FP8 where it helps. RLHF and DPO alignment if the task actually calls for it.
The weights
The checkpoint itself, in a format you can serve without us — safetensors, GGUF or an ONNX export. Yours outright, not licensed back to you.
The pipeline
Curation scripts, prompt templates, training config and the exact package versions. When your corpus grows next year you re-run it yourself.
The eval harness
The one that measures the thing you actually care about, plus the baseline numbers, so a future change can be shown to be an improvement rather than asserted to be one.
The machine
Optional, and often the right answer. Specced, built, burned in and documented — see the reference builds.
Not sure which of these you need?
That is a normal place to start. Describe the problem in your own words and we will tell you which of these fits, or that none of them do.