Know how well your actually works.
We put AI systems to the test, uncover where they fall short, and give you clear evidence to make better decisions.
“Our assistant handles expense-policy questions accurately.”
- ✓Routine claims handled correctly5 of 7
- ✕Ignores the $75 cap above the limitT-02, T-07
- Not tested: multi-currency claimsStated
Illustrative example
AI that looks good in a demo doesn't always work in practice.
Whether you're building an AI system or buying one, you deserve a clearer picture.
A promising score
A benchmark score doesn't tell you everything about the tasks your AI will face.
Your real tasks
Internal testing can miss edge cases, inconsistent behavior, and costly mistakes.
Benchloom's role: examine what the system is supposed to do, test how it behaves, and make the results understandable.
See what a useful finding looks like.
One realistic scenario can expose a weakness that a generic benchmark wouldn't catch. Explore these synthetic examples.
The answer approved the full amount. The company’s $75 meal cap was never applied.
- Root cause
- The expense policy was never sent to the model. The retrieval step returned nothing, no error was raised, and the model answered confidently from general assumptions.
- How we caught it
- We logged the exact context the model received for every test and compared each answer against the policy it should have used.
Performance beyond a single score.
Every assessment is tailored to what the system is meant to accomplish. We measure the things that matter for its actual use, not a generic checklist.
Correctness
Are answers accurate, relevant, and supported?
Task success
Can the system complete the work users need it to do?
Consistency
When and why does the system fail or behave differently?
Operational fit
Do speed, cost, and other constraints meet real needs?
Figures inside these tiles are illustrative.
A straightforward process. Findings you can act on.
We take the evaluation work off your plate and give your team a clear, documented assessment, not just another dashboard.
Explore our methodologyDefine what success means.
We learn the use case, intended tasks, and the decisions the results need to support.
Put the system to the test.
We build representative scenarios, assess the outputs, and investigate failure patterns.
Make the evidence useful.
You receive documented results, reproducible examples, limitations, and prioritized recommendations.
For teams building, deploying, or buying AI.
Different organizations. The same fundamental need: confidence grounded in evidence.
AI product companies
Understand product reliability and identify what needs to improve before customer deployment or expansion.
Learn moreEnterprise AI teams
Evaluate internal applications against the actual tasks they need to perform before wider use.
Learn moreAI buyers
Check how a proposed solution performs on your use cases, beyond a vendor's own demonstration.
Learn moreOur first commercial focus includes finance-related AI workflows; other well-defined applications are considered case by case.
No mystery scores. No guaranteed outcomes.
Good evaluation isn't about making a system look impressive. It's about finding out what the available evidence really supports.
We agree upfront what is being tested and what counts as success.
Results include methods and examples your team can review.
We distinguish findings from assumptions and identify what wasn't tested.
We don't promise that a client's system will pass an evaluation.
Better AI decisions start with better evidence.
Tell us what you’re building or evaluating. We’ll discuss the system, the questions you need answered, and whether a focused assessment makes sense.
Is our finance assistant ready for wider rollout?