Models Don't Act.
Agents Do.
AlpEye replaces passive model pipelines with coordinated AI agents that perceive, reason, act, and continuously self-improve autonomously.
Automatically discovers unlabeled frames with high information value based on model uncertainty.
- Identifies edge cases and rare scenarios
- Reduces manual dataset review by 70%
- Integrates with active learning loops
- Prioritizes frames by predicted labeling difficulty
Tracks training runs in real-time, alerting you to anomalies and recommending early stopping or continuation.
- Real-time loss curve analysis
- Anomaly detection in training metrics
- Resource utilization tracking
- Automated alerts for training failures
Answer questions about your datasets, models, and training runs with natural language queries.
- Query model performance metrics
- Dataset statistics and insights
- Training configuration recommendations
- Troubleshooting guidance
Suggests hyperparameters, schedules training runs, and tunes architectures based on dataset characteristics.
- Adaptive learning rate scheduling
- Architecture search for models
- Automated checkpointing and recovery
- Resource allocation optimization
How Agents Work
Each agent has live read access to your entire pipeline state every model version, every dataset, every training run, every test result. Their recommendations are traceable: every suggestion cites the exact data point that triggered it.
Dataset Discovery on Demand
When a team needs training data for a new weapon detection model, an engineer types a plain-English description into Data Scout something like "I need a dataset for guns and knives on CCTV footage". Data Scout searches Roboflow, Kaggle and HuggingFace simultaneously, ranks the results by class overlap and image volume, and returns a shortlist within seconds.
The engineer selects the most relevant dataset, clicks Import Selected, and Data Scout downloads it, converts it to the correct format, runs a health check and registers it as a new versioned dataset all without leaving the platform.
Catching Problems Before They Compound
Training Monitor activates automatically on every training job and reads metrics at the end of every epoch. When it detects that validation loss is rising while training loss continues to fall, it recognises the overfitting pattern.
In Autonomous Mode, it acts immediately and logs every decision with a full explanation. In Supervised Mode, it presents the recommendation and waits for engineer confirmation before taking any action keeping the team in control at every step.
A Deployment Readiness Check
Before signing off on a model for a client deployment, an engineer asks Expert Chat directly: "Is this model ready to deploy?" Expert Chat pulls the most recent Model Testing results, checks every class against defined performance thresholds and responds with a specific answer.
If knife class recall is below the required threshold, Expert Chat flags it, explains why it matters, estimates how many additional training images are needed and offers to trigger a retrain job with one confirmation click.
From Plain English to a Configured Training Job
An engineer needs to train a new model but is unsure which architecture fits their hardware budget. They open Auto-Train and describe the dataset, GPU limit and desired model size in plain English.
Auto-Train analyses the dataset, maps hardware constraints to a compatible architecture, derives hyperparameters and presents a clear configuration summary before anything runs. Training Monitor activates automatically once training begins.