For Enterprises
Services spanning the full AI development lifecycle,from evaluation and training to deployment.
Build, improve, and evaluate AI for complex real-world work.
We partner with enterprises across the model lifecycle: identifying where models fall short, building expert training data and reward signals, improving models through post-training, and deploying AI systems grounded in the context of how your organization actually works.
The lifecycle
Four capabilities, one continuous loop.
Start with a specific model challenge, or build with us across the full lifecycle.
Find where models fall short
Rigorous evaluations and benchmarks measured against real domain tasks and failure modes.
Create the data and signals that teach them
Expert demonstrations, trajectories, preference data, and verifiable reward signals.
Turn training signals into better model performance
Post-training programs across SFT, preference optimization, RL, and checkpoint evals.
Put capable AI to work inside your organization
Applied AI systems and agents grounded in organizational tools, data, and workflows.
Custom Evaluations & Benchmarks
We design rigorous evaluations that measure how models perform on the complex work that matters to your organization.
- Built around realistic tasks, workflows, and failure modes rather than generic benchmarks.
- Designed and reviewed by experts who understand what high-quality work actually looks like.
- Scored with expert-defined rubrics, model graders, and automated verifiers where outcomes can be measured objectively.
- Reusable across models, checkpoints, and vendors so improvements can be measured consistently over time.
The result is more than a score. It is a clear picture of what your models can do, where they fail, and what they need to learn next.
Custom Data & Reward Signals
We turn professional judgment into the data and feedback advanced models need to learn difficult work.
Our programs combine expert-generated data, model-assisted generation, rigorous verification, and structured quality systems to produce training signals designed around specific capabilities.
- Expert demonstrations that capture how difficult work is actually performed.
- Reasoning and tool-use trajectories for complex, multi-step tasks.
- Preference data, critiques, corrections, and comparative judgments.
- Verifiable tasks and reward signals for reinforcement learning.
- Synthetic data generated at scale and validated against expert-defined standards.
Every program is built around the capability being trained, not simply the volume of data being produced.
Post-Training & Model Improvement
We help enterprises turn high-quality training signals into measurable improvements in model capability.
Starting from frontier or open-weight models, we design post-training programs around the behaviors, workflows, and performance requirements that matter to your organization.
- Supervised fine-tuning on expert demonstrations and trajectories.
- Preference optimization using expert judgments and comparative data.
- Reinforcement learning against domain-specific rewards and verifiers.
- Targeted data generation based on model failures discovered during training.
- Continuous evaluation across checkpoints to measure real capability gains.
Training, data, and evaluation operate as one system: evaluate what fails, create the signals needed to address it, train, measure the result, and repeat.
Continuous loop
Across the post-training lifecycle
Training, data, and evaluation operate as one system: evaluate what fails, create the signals needed to address it, train, measure the result, and repeat.
Data generation & curation
Build the training corpus around the capabilities that matter.
Supervised fine-tuning
Teach models the behaviors and reasoning patterns they need.
Preference optimization
Align outputs with expert judgment and desired outcomes.
Reinforcement learning
Improve performance through rewards, verifiers, and interaction.
Evaluation & failure analysis
Measure every iteration and identify what remains difficult.
Training optimization
Refine the data, rewards, and training recipe as models improve.
Forward Deployed Engineering
We embed engineers with your team to turn operational context, including data, documents, tools, and workflows, into production AI systems.
- Engineers work directly with domain experts and operators to understand decisions, edge cases, and quality standards.
- We map the data, templates, permissions, and processes your organization relies on before choosing the agent architecture.
- Each system can use frontier or open-weight models, including models adapted through an Alpheva post-training program.
Build with us
Build, improve, and evaluate AI for complex real-world work.
Start with a specific model challenge, or build with us across the full lifecycle.
Build with us