REAL PEOPLE. REAL CONDITIONS. REAL EVIDENCE.
Find the gap before your users do.
Evaluate your models and machines against real human experience. Understand what works, where it breaks, and under which conditions.
LLMs & VLMs · Fashion tech · Robotics
A convincing output is only the beginning.
Good on a benchmark. Better understood in the world.
✳WHEN THE OUTPUT MISSES THE POINT
The answer sounds right. The fit looks perfect. The robot makes its move. Real-world evaluation asks: did it actually do what you needed?
01 / LLMs & VLMs“The mug is in good condition.”
WHAT THE IMAGE SHOWSA crack and a chipped rim.
A confident description can still miss the evidence that matters to the user.
Test grounding, not just fluency ↗
02 / FASHION TECHA relaxed shirt with room to move.
WHAT THE WEARER FINDSPulling fabric and gaps between buttons.
A realistic-looking image can create expectations the actual garment doesn’t meet.
Compare the preview with the wearer ↗
03 / ROBOTICSPick up the blue cube.
THE OBSERVED SCENARIOThe gripper closes. The cube stays put.
Executing a movement is different from completing the task. Can the system detect and recover from a miss?
Measure the outcome, not the motion ↗Where does your system fall short—and what evidence tells you why?
Find your model’s gaps ↗01 / WHAT WE EVALUATE
Different technologies need different tests.
Every study starts with the people, tasks and conditions your product is meant to serve.
Test how your model responds to the questions, images and situations your users actually bring.
Put virtual try-on beside the physical try-on. See where the promise matches the experience.
Observe your robot carrying out defined tasks in a controlled setting with real users.
02 / WHAT YOU TAKE AWAY
A practical account of your model’s strengths and limitations. Clear enough to share with your product team. Specific enough to guide what you improve next.
Capabilities that held up in the tested scenarios.
Failure cases, mismatches and participant observations.
The model version, participant group, conditions and limits of the study.
ILLUSTRATIVE FORMAT · NOT STUDY RESULTS
Select an application to explore the report.
Each finding includes supporting evidence and test conditions. Untested capabilities stay explicitly untested.
03 / HOW WE WORK
Agree on the scope first.
Make the findings useful from the start.
Tell us what you’re building, who it’s for and what you need to know. Together, we define scenarios and evaluation criteria.
Run the agreed study with suitable participants, tasks and controlled conditions. Capture outcomes and structured feedback.
Review your specification sheet, evidence and improvement priorities. Use the findings to scope your next iteration.
ABOUT US
GroundMyAI is a Pixophilia initiative. Our engineering team is led by artificial intelligence and machine learning experts who previously worked at Meta, Adobe, and Carnegie Mellon University.
We collaborate with academic institutions around the world to build and validate our AI models—bringing research and engineering together.
Our collaborators include experts from Carnegie Mellon University, the University of Illinois Urbana-Champaign, Seoul National University, and the Indian Institute of Science, Bengaluru.
Meet Pixophilia ↗LET’S START WITH YOUR QUESTION
Tell us about your product and the gap you want to investigate. We’ll discuss a suitable pilot, scope and deliverables.
office@pixophilia.io ↗