Product Demo · 55 seconds
Protecting AI training from corrupted feedback.
The problem
AI systems learn from feedback. Corrupt the feedback, and the model learns the wrong thing.
The failure is subtle, scalable, and easy to miss until it becomes behavior.
Trusted
Trusted
Corrupt
The gap
Feedback is easy to collect. Trust is not.
Shared agents gather logs and scores from many sources, but raw feedback should not flow directly into training.
Org A
Org B
Org C
Org N
Shared production agent
serves users and logs interactions
Feedback signals
humans, automated checks, audits
Interaction logs
prompts, responses, metadata
Missing layer
No trust boundary
mixed signals still sit too close to training
Training path
updates become noisy or brittle
Without a trust layer, raw feedback remains an unstable learning signal.
The solution
COBRA moves in front of training.
It sits between feedback and model updates, turning mixed rewards into a curated training signal for the next release.
Continuous improvement loop
Org A
Org B
Org C
Org N
Production app / agent layer
serves users and logs interactions
Feedback signals
humans, automated checks, outcomes
Interaction logs
x, y, metadata, tenant id
COBRA
trust scoring · cohorts · robust aggregation
Curated training data
filtered and weighted examples
Model training
offline fine-tune / RFT
Release → deploy
evaluation, approval, rollout
Opt 1: application owners operate COBRA
Opt 2: model providers offer COBRA
How it works
One system. Three ways to preserve trustworthy learning.
COBRA continuously evaluates feedback quality before any training step.
Separate
distinguish trusted and suspicious cohorts
Measure
score consistency and reliability
Aggregate
promote useful signal, suppress corruption
Root of Trust
Trusted anchors keep the decision stable even when noisy feedback gets louder.
Dynamic Reliability Weighting
When trusted feedback converges, COBRA increases its influence.
Adaptive Variance Guidance
The lower-variance cohort becomes the guide when the environment turns unstable.
Results
A cleaner signal leads to better learning.
+25%
Accuracy improvement with COBRA under corrupted feedback.
Unprotected
48%
COBRA
73%
Conversational tasks
+30%
Improvement versus unprotected baseline.
Largest model tested
1.5B
Validated on real LLMs, including GPT-2 XL scale.

COBRA makes AI systems more trustworthy.

Any model. Any feedback source. Any training pipeline.