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AI-native fraud and AML platform with real-time decisioning and cross-entity intelligence for banks, fintechs and payments.
End-to-end computer vision platform for teams annotating data, training YOLO models, and deploying them at scale.
Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
Undetectable desktop AI assistant that feeds real-time answers during coding and technical interviews.
Free tool that auto-generates conversational, browsable documentation for any public GitHub repo, from the makers of Devin.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Real-time AI fraud/AML decisioning
- ✦Adaptive ML detection engine
- ✦Cross-entity link analysis
- ✦AI agents incl. conversational 'Vera'
- ✦Investigation and reporting tools
- ✦Low-latency scoring at high QPS
- ✦Smart data annotation with SAM-powered one-click masks across six task types
- ✦Cloud training with 22+ GPU configurations from RTX 2000 Ada to B200
- ✦Support for YOLOv5 through YOLO26 model families
- ✦One-click deployment across 43 global regions with auto-scaling
- ✦Export to 18 formats including ONNX, TensorRT, and CoreML
- ✦Live training metrics and experiment comparison dashboard
- ✦Chat with your documents (RAG)
- ✦Runs locally and offline for privacy
- ✦Supports any LLM (local or cloud)
- ✦Built-in AI agents
- ✦Handles PDFs, Word, CSV, codebases
- ✦No-code setup
- ✦Real-time AI answers during technical interviews
- ✦Invisible to screen sharing and recording
- ✦Hidden from dock, tray and activity monitor
- ✦Click-through overlay
- ✦Live audio capture and transcription
- ✦Lifetime unlimited access license
- ✦AI-generated documentation for GitHub repos
- ✦Conversational Q&A about a codebase
- ✦Browsable index of popular repositories
- ✦Deep code indexing via Devin
- →Stop payment, card and check fraud
- →Prevent account takeover and application fraud
- →Meet AML/BSA compliance
- →Onboard customers with less friction
- →Building and training custom object detection or segmentation models
- →Labeling large image/video datasets for computer vision projects
- →Deploying vision models to edge or mobile devices
- →Running quality control or defect detection in manufacturing
- →Powering retail, logistics, or agriculture vision applications
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app
- →Getting live help on coding interview problems
- →Answering technical questions in real time
- →Avoiding detection during screen-shared interviews
- →Understanding an unfamiliar codebase quickly
- →Onboarding to open-source projects
- →Answering questions about repo internals