Turn proprietary data into proprietary advantage.
Build specialist models on the knowledge, decisions and operating data that make your organization unique. SF Tensor makes private-data post-training repeatable, affordable and production-ready.
01. Time to a specialist model
From a production-ready dataset.
02. Faster iteration cadence
With repeatable data, evaluation and retraining pipelines.
03. Deployment boundary
Keep data, weights and policy inside the environment you control.
Your edge is what public models never saw.
A frontier model can reason, but it does not know your underwriting history, clinical protocols, factory telemetry or internal decisions. Private-data post-training integrates that knowledge into the model itself, then keeps it current as the business changes.
01
Connect private knowledge
Prepare proprietary and specialist data inside the boundary you control.
02
Private-data post-training
Start from the right base model, integrate domain data, align and evaluate.
03
Deploy under your controls
Ship the resulting weights in your cloud, our cloud or an edge environment.
04
Keep the model current
Repeat the pipeline as policies, data and business conditions evolve.
A better reasoning-cost curve.
Compare each model across its available reasoning-effort settings. A specialist model can reach the target capability with far less evaluation-time compute.
Reasoning efficiency frontier
Medium
0.754 / $455
High
0.808 / $713
Extra high
0.844 / $1,084
Max
0.866 / $1,549
High
0.769 / $638
Extra high
0.824 / $1,151
Max
0.849 / $1,831
Specialist model
0.841 / $231
- Specialist model: score 0.841, cost $231
- GPT-5.6 Sol, Medium: score 0.754, cost $455
- GPT-5.6 Sol, High: score 0.808, cost $713
- GPT-5.6 Sol, Extra high: score 0.844, cost $1,084
- GPT-5.6 Sol, Max: score 0.866, cost $1,549
- Opus-4.8, High: score 0.769, cost $638
- Opus-4.8, Extra high: score 0.824, cost $1,151
- Opus-4.8, Max: score 0.849, cost $1,831
A model-building function, without an infrastructure detour.
Your domain team owns the objective and data. We bring the training system and forward-deployed engineers needed to turn both into a model.
Private-data post-training
Integrate private knowledge during training instead of attaching it only at inference time.
Specialist model fleets
Train smaller models for specific tasks, customers, devices and modalities.
Bring your environment
Run inside existing cloud accounts, private networks and operational controls.
Outcome-based optimization
Choose hardware and topology for cost, time, memory or deployment constraints.
Forward-deployed engineering
Work with the engineers who build the compiler, runtime and training platform.
Private by design
Your data teaches your model. Nothing else.
Your cloud
Run inside your existing accounts, networks and controls.
Your weights
Keep complete ownership of checkpoints and resulting models.
Your policy
Bring retention, access, audit and regional requirements.
Our operators
Work directly with engineers who own the training stack.
Build the model your industry has been waiting for.
Financial services
Train on decades of underwriting, research, risk and operational decisions.
Healthcare and life sciences
Combine domain literature with private protocols, outcomes and modalities such as imaging, omics or sensor data.
Industrial and government
Turn telemetry, reports, procedures and institutional knowledge into capability.
The knowledge that makes you different is already in your data. Train it into a model you own.
Bring a production-ready dataset. We build the repeatable post-training system around it.
Train the models only you can build. One stack for enterprise post-training and frontier pre-training.
All Systems Operational© 2026 San Francisco Tensor Company
