Define the boundary
Map training data, model artifacts, retrieval, and logs to approved storage and services. Specify identity, residency, network egress, and operator access before provisioning the model stack.
Data topology and access modelPrivate model deployment
Your model, running on your infrastructure.
Private deployment gives your organization control over where models run and where their data goes. We configure open-weight training and inference in your cloud account or on-premises, then tune the serving stack for the context lengths, concurrency, and latency your application requires.
Your cloud account or on-premises environment
Training
Private datasetsCurated examples + permissions
Training workload
Checkpoint evaluation
Model registry
CandidateApproved Rollback
ServingPrivate inference endpointAuthenticated · scoped · monitored
Your applicationRetrieval + permitted tools
Reviewed traces return to curation
Training and serving share artifacts, not unrestricted access.
Approved checkpoints move through a registry to a private endpoint. Data access, network egress, telemetry and rollback are designed around the complete lifecycle.
What has to stay inside the deployment boundary?
An inference endpoint is one step in a larger data path. Embeddings, retrieval, tool calls, experiment tracking, and request logs can all move proprietary information. We design the complete path within the agreed boundary, including how models enter it, how checkpoints are stored, and how operators gain access.
The research work.
Each intervention has a purpose, a record of what changed, and a way to assess its effect.
Serve the model
Package the checkpoint with its tokenizer and inference settings. Configure GPU placement, batching, KV-cache capacity, and parallelism; measure the effect of quantization on both task quality and memory.
Model endpoint and serving configurationValidate under load
Replay representative prompt lengths and arrival patterns. Find where latency degrades as concurrency rises, set capacity limits, and exercise checkpoint rollback and worker recovery.
Capacity model and deployment runbookWhat determines
whether it works.
We set the evaluation around the application’s requirements, including the failures an aggregate score can hide.
- Data boundary
- Verify network egress, tool destinations, telemetry, and log retention across training and inference, including external dependencies.
- Serving performance
- Measure time to first token, inter-token latency, throughput, and GPU memory across the expected request and concurrency distribution.
- Operational control
- Exercise access revocation, load limits, worker failure, and restoration of a known checkpoint with the correct serving configuration.
What leaves
the lab.
Deployment and access design
The training and serving topology, approved data paths, access policies, and terms of use for the selected base model.
Serving infrastructure
Deployment configuration, authenticated endpoints, model storage, observability, and workload-specific capacity settings.
Operations handover
Quality and load measurements, GPU cost assumptions, rollout and recovery procedures, and operating responsibilities.
Before we begin.
Will our data reach another AI lab?
The deployment can keep training and inference within your environment without calling external model APIs. We apply that requirement to the complete data path, including retrieval, embeddings, tools, logs, and support access, and verify it as part of deployment.
Do we own the underlying model?
The base model remains subject to its license. We document your rights to use and modify it, together with ownership and access terms for custom datasets, adapters, checkpoints, and deployment code in the engagement agreement.
Work with the lab
Bring us a research problem.
Tell us where the model falls short and what better performance would mean for your team.