We buildwhat’s next
Engineering intelligent systems that power a better tomorrow, with a clear line of sight from the first dataset to a model you can hand off.
Every run leaves
evidence behind.
Dataset intelligence
Register a dataset, check that it's healthy and compatible, and keep its provenance visible before you spend a run.
Launch control
Preflight the provider with live pricing, set a budget ceiling, keep a watchdog on the job, and refuse a duplicate launch before it starts.
Training workflows
Run speech training, PEFT LoRA and QLoRA language-model work, Signal Forge streams, and custom evaluations from the same desk.
Evidence to handoff
Stay with logs, telemetry, and charts through checkpoints and recovery, then leave with a reproducibility pack and a model you can export or hand to Hugging Face.
Train. Observe.
Hand off.
1dataset registered2provenance recorded3health checked4target compatible56preflight waiting for launch
A control plane
for training.
A desktop control plane for speech and language-model training, from dataset health and launch preflight through telemetry, recovery, and model handoff.
Windows desktop
A control plane that runs on your machine.
Solo and small teams
Built for people who run their own training.
Speech and language
Whisper, PEFT LoRA and QLoRA, custom evaluation.
Dataset to handoff
One desk from the first check to the export.
speech Whisper fine-tuninglanguage PEFT LoRA, QLoRAstreams Signal Forgeevaluation custom slices
Your compute.
Your accounts.
User-owned MLflow, Neo4j, Sentry, Hugging Face, buckets, and agent integrations remain optional connections, not hidden requirements.
Works with the stack
you already run.
Bring your own compute and accounts. Connect what you use, leave out what you don't.
Power with a clear
line of sight.
Built around local control, observable runs, user-owned infrastructure, and recoverable failure states.
Local-first control
The desktop control plane, credentials, and workflow state stay close to the user instead of making a hosted control service mandatory.
Provider-aware orchestration
Provider selection, live pricing where available, budget ceilings, watchdogs, and failure handling stay visible before and during a run.
Evidence over guesswork
Telemetry, logs, metrics, checkpoints, and reproducibility packs make the state of a run inspectable rather than implied.
Open boundaries
User-owned MLflow, Neo4j, Sentry, Hugging Face, buckets, and agent integrations remain optional connections, not hidden requirements.
Work on the hard,
useful parts.
If this work sounds like your kind of problem, send a short note with the work you do and the problems you like solving.
A short note is enough