LIVE
Live data collection
Operators record egocentric episodes on site.
LIVEDeliver data your customers accept the first time
Automated checks, live alerts, and models fine-tuned on your own QA decisions
turn egocentric data QA from a bottleneck into an advantage.
Scale data collection,
not your review backlog
Manual review cannot keep up with collection. SignalFlag checks footage in parallel, so batches are ready to deliver sooner and problems are caught before they reach your customer. All of this runs on infrastructure we manage, so your team has nothing to build or maintain.
LIVECloud ingest to SignalFlag
Episodes upload automatically: no manual hand-off.
- episode_0141.mp4Ingested
- episode_0142.mp4Uploading
- episode_0143.mp4Uploading
Cloud eval
Evals run in parallel and score every episode in minutes.
Eval notification
Failures reach the team while operators are still on site.
# data-collection-alerts
Eval failed · episode_0142
- Temperature setting: 450°F
(maximum 400ºF)
Kitchen-03 · collected 4 min ago
Case Study
Catch the mistakes
that put delivery at risk
Manual annotation is slow, and mistakes are inevitable. Our world reasoning models annotate each episode and flag likely operator errors against your customer’s criteria, so your reviewers spend less time searching and more time on the decisions that need human judgment.
Fine-tuning that improves with every batch
Off-the-shelf models don’t know your customers’ acceptance criteria, and they don’t learn from your team’s feedback. At SignalFlag, we fine-tune a customized version of our world reasoning model on your team’s approvals and corrections. As stronger base models are released, we fine-tune those too, shipping a new version only when it outperforms the last. To validate this approach, we evaluated small, fine-tuned models against leading proprietary models on unseen tasks. Here’s what we found:
Each new base model
raises the barWhen we applied our fine-tuning to a newer, more capable base model, the result outperformed both leading proprietary models.
Frontier-level detection
at a fraction of the costOur fine-tuned models caught as many mistakes as OpenAI’s GPT-6 Astra and Twelve Labs’ Pegasus, all at a fraction of their cost per video-hour.
Strong results
from a small amount of dataFine-tuning on a few hundred labeled examples was enough to turn a general model into a purpose-built QA model.
Secure from collection to review
Your customers trust you with their footage, and SignalFlag helps you keep that trust. Footage moves securely from collection to review, with no manual hand-offs along the way. Your footage and your customized version of our world reasoning model stay isolated, so every correction your team makes sharpens your version and no one else’s.
Footage moves securely
Data stays isolated
Trained models stay yours
See how SignalFlag fits your QA workflow and your customer’s criteria
Book a callFAQ: Egocentric data QA
How does SignalFlag improve QA for egocentric training data?
SignalFlag automates the first pass of QA for egocentric video. Our world reasoning models annotate each episode and flag likely operator errors against your customer's acceptance criteria, on infrastructure that checks footage in parallel. It's a human-in-the-loop workflow: your reviewers make the decisions, and those decisions train the models to get better with each batch.
Why not use a general video model?
A general video model can describe what's happening in egocentric footage, but it doesn't know what your customer will accept. We fine-tune a customized version of our world reasoning model on your customer's criteria, so every check reflects their standards. When footage doesn't look like anything the models have seen, it goes to a general model with full human review.
What happens when a customer changes their acceptance criteria?
Your specialist models are updated for the new criteria without rebuilding your QA pipeline. A new version ships only if it tests better than the last, so you can adapt to new requirements without risking the quality you've already built.
Can we use SignalFlag inside our existing tools?
Yes. Use the SignalFlag app, or bring the checks into your own app with our app harness.
Can we test SignalFlag on our own footage?
Yes. Send us a sample and your customer's acceptance criteria, and we'll walk you through the results.