Applied AI research
Models that learn how
your business works.
We turn proprietary data, expert judgment and task feedback into specialized models—and deploy them within infrastructure you control.
Find the relevant exception
Cite the source evidence
Escalate missing information
Teach the model the decisions, evidence and response structure your experts expect.
The model is
a starting point.
Your organization’s expertise lives in decisions, corrections and work that is difficult to describe in a prompt. We study how to make that expertise learnable.
That means constructing the right tasks, adapting the model, and testing whether the behavior transfers. The result must work within your data, compute and operating constraints.
Research grounded
in a task.
Train a specialist to review technical evidence. Teach an agent to verify an analytical result. Find the data intervention that addresses a recurring failure.
Source evidence paired with an expert’s decision.
Keep the base weights frozen. Learn a small adapter.
Identify the issue, cite evidence and flag missing information.
Low-rank adaptation learns a weight update, ΔW = BA, while the base weights remain frozen. Domain examples define the target behavior; independent documents test whether it transfers.
From the experiment
to the operating model.
We work across post-training, evaluation and infrastructure. The method follows the bottleneck: data quality, model behavior, task feedback or the cost of serving.
Model post-training
Specialize an open-weight model for your task, using expert demonstrations, preference data, and experiments that separate useful learning from memorization.
Explore the work ↗Reinforcement learning
Train models to solve tasks through repeated attempts, with environments that reproduce the work and rewards grounded in the outcome.
Explore the work ↗Model evaluation & research
Build evaluations that expose the task’s difficult cases and show which data, model, or training change is responsible for an improvement.
Explore the work ↗Private model deployment
Run training and inference in your infrastructure, with private data paths and serving configurations matched to the model and workload.
Explore the work ↗Continual model improvement
Use production failures and expert feedback to choose the next training experiment, then promote the candidates that improve the deployed model.
Explore the work ↗Applied AI & agent infrastructure
Build the retrieval, tool interfaces, and execution environment that let a model complete enterprise work and generate useful training feedback.
Explore the work ↗A private model needs
a private data path.
Training, checkpoints, inference and feedback can run in your cloud account or on-premises. We design the boundary around the full lifecycle, including tools, telemetry and support access.
Private model infrastructureTraining & release
Curated examples with scoped access.
Adapt the model. Test each checkpoint.
Versioned artifacts. Approved releases.
Private serving
Send a task with permitted context.
Authenticate requests. Enforce scope.
Serve the approved model at scale.
Improve through reviewed feedback. Curate selected failures into the next training dataset.
Approved checkpoints move through a registry to a private endpoint. Data access, network egress, telemetry and rollback are designed around the complete lifecycle.
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.