For Enterprises
Services across the full evaluation, training, and deployment lifecycle.
We partner with enterprises across the whole arc of model work: finding where models break down in real professional workflows, building the data that closes the gap, post-training custom models, and deploying agents that run on your firm’s own context.
The lifecycle
Four services, one arc. Start at any stage, or hand us the whole loop.
01 · Evaluate
Custom Evals and Benchmarks
We create evaluation task sets that pinpoint exactly where a model breaks down, across any domain or workflow.
Written by practitioners who do the work daily, not by annotators approximating it.
Graded by expert-crafted rubrics and automated verifiers, so every score points at a specific failure mode.
Delivered as a benchmark you can re-run against every new model, checkpoint, and vendor.
02 · Curate
Custom Datasets
We design proprietary datasets optimized for fine-tuning language models to your exact requirements and use cases. We curate expert-generated datasets, generate synthetic data, and help turn messy enterprise data into a form models can be trained on.
Scoped from the failure modes the evals surface, so you train on the gaps that matter rather than the data that is easy to collect.
Captured from domain experts step by step, decision by decision, and verified before delivery.
Shaped for the method: reasoning traces for SFT, comparison pairs for preference training, verifiable tasks for RL.
03 · Post-train
Post-Training
Our frontier research team helps enterprises train custom models end-to-end, starting from open-source bases like Nemotron: models that are more performant, cheaper, and faster.
The model and the data around it treated as one system: generate and curate what is missing before touching a weight.
Warm up on expert demonstrations, then push past imitation with reinforcement learning against verifiers designed for your domain.
Every checkpoint scored on the benchmark from stage 01, so progress is measured rather than asserted.
Every stage of the post-training lifecycle
Build the corpus the run needs
Teach the behavior before RL begins
Push the model past what imitation reaches
Turn expert judgment into a reward signal
Score every checkpoint, find what still breaks
Refine the recipe and run it again
04 · Deploy
Forward-Deployed Engineering
Agents integrated with internal firm context, including internal files and data sources, built with out-of-the-box or fine-tuned models.
Our forward deployed team works on site, side by side with the people who do the work.
We start with the data: the precedents, templates, and workflows a firm actually runs on. Agents come last.
Built on an off-the-shelf frontier model, or on the one we post-trained for you.
