Stop guessing whether your AI works.
Train your team to build AI evaluations on your own system.
Learn to define success, turn real failures into evaluations, and refine them as your system changes.
AE Studio collaborates with Anthropic on AI research. We advise companies including Samsung, Walmart and Berkshire Hathaway.
Your AI can look impressive and still fail at the job.
A convincing answer can violate a business rule. A successful demo can miss a critical exception. Your team needs a way to see beyond the examples that look good.
- Your team disagrees about what “good” means. Without shared criteria, reviews become debates and release decisions depend on who checked the output.
- Important failures remain hidden. The examples you’ve tried may never test the situations where your AI gives an incorrect answer or takes the wrong action.
- It’s hard to know what to improve next. Without evidence of where the system falls short, your team can spend weeks fixing the wrong problems.
Well-designed AI evaluations turn requirements and real failure cases into repeatable tests. They help your team define success, uncover weaknesses, and decide what to improve.
Learn the method by building evals for your own system.
Work alongside AE’s practitioners to design, build, and improve evaluations for your own system. Training typically runs over two weeks, with a one-week option for teams able to commit to a concentrated schedule.
Make the important behaviors explicit. Your team identifies what the system must do, what it must avoid, and where human judgment is needed. Together, we turn those expectations into criteria you can test.
Turn requirements and real failures into test cases and scoring criteria. Learn why each case belongs in the suite and how to judge the result.
Run the suite and inspect failures together. Check whether a result points to a problem in the AI system or in the evaluation. Use the evidence to decide what needs attention.
Learn how to write, run, and extend the suites as new cases appear. Your team keeps the evaluations and a method for continuing the work.
What your team leaves with.
A method your team can repeat
Learn how to define criteria, create cases, inspect results, and improve evaluations.
Written standards for your system
Make successful behavior, important constraints, and known limits explicit.
Evaluation suites you keep
Keep the suites built during the engagement, running in accounts you own.
Prioritized findings
See observed failures, why they matter, and estimated effort for the identified fixes.
Taught by senior practitioners who build AI and study how it fails.
AE Studio has built production AI since 2016. Every person on the team is senior, in-house, and client-facing, with 10+ years of experience.
Senior technical AI PM
Works with your product and domain leads to define success and choose the cases.
Senior AI engineer
Works with your engineers to build, run, and score the suites in your accounts.
Research behind the method
Our alignment researchers study failures that are hard to spot, such as sycophancy and goal drift. That work shapes the cases we teach your team to look for.
Read the study on reducing deceptive answers →Production AI we’ve built
Results from build work for clients, not from this engagement.
Azul Airlines
Eight production models pricing more than 50,000 flights, updated every 50 minutes, with anomaly checks and circuit breakers in front of every change.
Read the case →Common questions
Why are AI evaluations important for production AI?
AI evaluations turn your quality and reliability requirements into repeatable tests. They help your team define success, uncover failures, and use evidence to steer development. Used throughout development and in production, they help you check whether your AI meets those standards as models, data, and user behavior change.
How much time does our team need to commit?
Your product or domain lead and each participating engineer should plan on 4–6 hours per week over two weeks. The engagement includes 4–6 working sessions in total. The compressed one-week option requires 8–12 hours per participant that week.
Will our AI system be ready for production afterward?
That depends on what the evaluations uncover. Your team leaves with working evaluation suites and prioritized findings to guide the next steps. Some findings may require further development before release.
Do we have to continue working with AE afterward?
No. You keep every evaluation suite we build together, along with the written success criteria and findings. Your team learns how to run, extend, and improve those evaluations. Follow-on support is optional.
Discuss your team’s evals.
In 15 minutes, we’ll discuss your AI system, the skills your team wants to build, and whether the engagement fits your team.
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