Frontier models are widely available. Your workflows, expertise, standards, and institutional knowledge are not.
We work with your team to figure out what to evaluate, what is limiting performance, and the solution that will move your business forward. Then we help build and run it.
Make better model decisions and set a clear launch standard.
Generic benchmarks only tell you so much. We build evaluations around your workflows, quality standards, and operating constraints.
Don’t know where to start? We work with your technical teams and subject-matter experts to define the right tasks, benchmarks, and failure modes.
Compare leading models on your business goals, with a clear view of quality, cost, latency, reliability, and failure modes.
Test models and AI systems against custom benchmarks and evaluation sets before they reach customers. Establish a clear quality bar and identify weaknesses, edge cases, and failure modes early.
Evaluate outputs over time to catch regressions, quality drift, and new failure modes as models, prompts, tools, and workflows change.
Improve
Improve performance before you consider a model migration.
The model is only one part of the system. We evaluate prompts, context, retrieval, tools, routing, grading logic, and agent behavior to identify what is actually limiting performance.
We work with your team to diagnose the problem, prioritize the highest-impact changes, and test them against your workflows.
We measure the impact on quality, reliability, cost, and latency. Often, meaningful gains come without changing the underlying model.
Train
Teach models the judgment and workflows that set you apart.
When prompting and system optimization are not enough, we help determine what the model needs to learn and design a training program around it.
We work with your technical teams and domain experts to define target capabilities, identify failure modes, design tasks and evaluation criteria, and determine the right mix of training data, environments, and feedback.
We build the expert demonstrations, training tasks, environments, rubrics, preference data, and feedback loops needed to improve performance in your domain. Programs include supervised fine-tuning, reinforcement learning, preference optimization, and custom RL environments.
It’s a home game
Evaluate AI on your home turf
We work with your models, tools, data, and production workflows, alongside the people in your organization who know the work best.
That means evaluating AI with the tools you actually use, testing models against your real operating constraints, and defining quality with the experts responsible for the outcome.
Whether we’re comparing models, improving an existing system, or training a new capability, the work stays grounded in your environment, not an abstract benchmark.
Edge of the Frontier
Why Surge AI
Years of working with leading AI labs have given us a detailed understanding of what improves model performance: rigorous evaluations, difficult training examples, high-quality feedback, and clear signals about where models fail.
We bring those methods to enterprise AI programs.
Expert judgment at scale
High-stakes work requires more than generic annotation.
We work with doctors, lawyers, engineers, researchers, finance professionals, and other specialists who evaluate AI against professional standards and real-world requirements.
Results in a week
Frontier labs move in days, not quarters, and we’re built to match. You’ll have pilot results within a week of our first conversation.
Program design and execution
The hardest part is often deciding what to evaluate, what good performance looks like, and what intervention will actually improve it.
We design the program with you. From benchmarks and failure modes to data, feedback loops, and training approaches. We provide the infrastructure and expert workforce to execute it.
Published Research, Proven Results
We test our own data. We study whether post-training methods actually produce meaningful gains, not just theoretical capability. Our research shows the methods and performance improvements, across coding, reasoning, agentic tasks, and many other domains.
Enterprise AI problems rarely arrive as clean evaluation or training specifications.
We work with your technical teams and subject-matter experts to define what good looks like, design the right evaluations, identify failure modes, and determine the most effective path to improvement.
That might mean switching models, improving the surrounding system, building better evaluation data, or designing a custom training program.
You know an agent is underperforming, but not why.
You're choosing between models with no benchmark you trust.
A model must improve — prompting, data, or post-training?
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