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Private LLM solutions

Private LLM solutions from training to deployment.

Fine-tune an open model, train an agent in a gym, qualify it, then serve the artifact inside your security boundary. Each stage leaves assets and evidence you keep.

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LLM Post-Training Platform
Private AI Training Environments
Reproducible LLM Evaluation
Private LLM Deployment for Sensitive Industries
One run, four stages. Each is a page.in: an open model + your examples01Fine-tuneSFTadapter02ReinforceGRPO in a gymadapter03Evaluatesealed test setscore04Deployyour endpoint↑ the only stage that can say nothe weights sit on your disk the whole way. nothing is uploaded to us or to anyone.run stage 1 alone, 1+2, or all four. each stage leaves an artifact you keep.
Click a stage to read what you bring, what changes, what you keep and what must pass before the next one. Stages can be run separately; the artifacts are what make them compose.

Capabilities

A connected lifecycle for models you own.

Follow a capability to see its workflow, evaluation evidence, and deployment considerations.

SFT + GRPO

LLM Post-Training Platform

Turn open models into owned, specialized skills with supervised fine-tuning and reinforcement learning.

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OPENENV

Private AI Training Environments

Train against typed workflows, hidden deterministic checks, and authoritative task outcomes.

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QUALIFICATION

Reproducible LLM Evaluation

Compare base and adapted policies on frozen held-out tasks before any promotion decision.

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PRIVATE SERVING

Private LLM Deployment for Sensitive Industries

Serve approved adapters and checkpoints inside your private cloud or on-premises environment for sensitive workloads.

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Where to start

Start with the evidence your workflow already has.

You have expert examples

Start with supervised fine-tuning (SFT) to teach the model how your best engineers work. Save a secret test exam it never sees, compare the before-and-after scorecard, and keep the resulting weights file on your disk.

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Your task has measurable outcomes

When an agent can take actions in an environment—run code, simulate a circuit, test a robot—use reinforcement learning (GRPO). The model explores by trial and error, and an automated judge scores whether it actually solved the problem.

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You need to deploy an adapted model

Before anyone hits deploy, make the model pass a frozen exam. Then run it inside your private cloud or on-prem servers: your data never leaves, your latency stays predictable, and you stop paying per-token API bills.

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Before you start

Private LLM post-training questions

What is private LLM post-training?

Private LLM post-training means taking a capable open model (like Qwen or Llama) and teaching it your specific company's work using your own examples and simulators—all running on your own servers. You keep the resulting weights file.

Do my data leave my organization?

No. Datasets, simulation runs, prompts, and weights stay strictly inside your own cloud VPC or on-premises machines. g factor never stores your data or proprietary fixtures.

SFT or GRPO — what should I choose?

Use SFT (imitation) when you have good examples of humans doing the work. Use GRPO (reinforcement learning) when you have an automated test or simulator that can grade the result. Most production agents use SFT first to learn the basics, then GRPO to master edge cases.

Can I run g factor on-premises with my own GPUs?

Yes. Qualified weights run anywhere standard open models run: your private AWS/GCP account, a dedicated Nebius cluster, or your own on-prem GPU rack.

Private beta

Have a high-value task an AI agent keeps fumbling?

Tell us what the model is supposed to do and how you check its work. We'll tell you if post-training can fix it.

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