Fix What Your LLM Gets Wrong. Automatically.
Entropy Sentinel identifies where your model breaks in production and sources the new data to fix it. No annotations, no manual review.
Your LLM fails on the same questions, repeatedly. You don't know which ones until it's too late.
You can't see what's breaking
Models fail confidently. The output looks fine until a user sends a screenshot. By then you've served hundreds of wrong answers with no record of which questions triggered them.
Fine-tuning without a target is expensive noise
You sample from your data uniformly, run a $30K fine-tuning job, and the eval numbers barely move. The model was already good at most of that data. You needed to fix the 8% it kept getting wrong.
Every cycle resets the clock
The next run doesn't know what the last one fixed. You retrain, redeploy, and hit the same weak points three months later. There's no memory between cycles.
How It Works
Three steps. Runs on your logs. Outputs a fine-tuning dataset and an updated model.
Detect failure regions
We scan your production logs and identify the weak points where your model consistently underperforms. No correct answers needed, just the interactions you already have.
Source targeted training data
For each weak point, we source the new data your model needs to cover it. The dataset targets exactly what broke, nothing else.
Retrain on what actually broke
You fine-tune on the targeted dataset. The next cycle starts from the updated model, so gains accumulate instead of resetting.
The same problem, three different shapes.
RAG pipelines, chatbots, fine-tuned models. The failure pattern is the same. Entropy Sentinel finds it regardless of your stack.
Your retrieval model answers confidently, and wrongly.
Most RAG failures concentrate around a handful of topic areas the retriever consistently misses. We identify those weak points from your logs and source the data that covers them. No annotation team, no guessing which chunks to fix.
Fix the 8%
that drives 80% of complaints
Users notice the failures before you do.
Your support bot handles most tickets well. The problem is the 40 topics it consistently botches, and you only find out through escalations. We map exactly where it breaks and retrain only those areas.
Don't touch what works
retrain only what breaks
A $30K fine-tuning run that moves the eval by 0.3%.
That's what happens when you sample training data uniformly. The model was already solid on most of it. Entropy Sentinel identifies the weak points first, so the budget goes where it actually matters.
Targeted data
on verified weak spots
Built from research. Pointed at a real problem.
Entropy Sentinel started as a pipeline for detecting LLM failure regions without labeled data. The research worked. The question became whether it could be turned into something engineers could actually integrate.
It can. The same pipeline is now an API.
Ian Bedac
Founder & CEO
Designed the Entropy Sentinel pipeline end-to-end from the research architecture to the API. Previously built production systems at the intersection of ML and software engineering.
Questions we actually get asked.
Request beta access.
We're onboarding a small first cohort. Leave your email and we'll reach out with API credentials and a short intake form about your stack.