Raven AI

Ready-to-use solutions for Multimedia Edge AI

Raven AI Engine is a flexible AI framework with turnkey solutions, specially designed for the real-time execution of AI algorithms in multimedia applications. It is the result of combining decades of experience in video and multimedia with our expertise in artificial intelligence.

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VALUE PROPOSITION

Power under control

With our in-house technology, we ensure exclusive solutions and customization

FLEXIBILITY AND CUSTOMIZATION

We tailor the engine to client needs, optimizing performance, accuracy, and speed based on project and hardware requirements.

continuous innovation

Our in-house engine allows us to stay ahead of AI and video processing trends, delivering cutting-edge solutions.

SUPPORT AND MAINTENANCE

As the engine’s developers, we offer specialized support and rapid, customized maintenance when needed.

RAVEN AI

Deploy multimedia processing with customizable AI solutions

Developed in-house, Raven AI combines advanced machine learning algorithms with a versatile, multi-platform framework, offering exceptional performance and adaptability. With full control over the technology stack, we can customize solutions for specialized hardware, operating systems, or unique environments.

Our engine is much more than an AI runtime toolkit; it’s a complete SDK offering advanced graphical pre- and post-processing capabilities, GStreamer integration, and multimodal AI inference. It utilizes hardware acceleration to maximize performance and ensure real-time efficiency across demanding tasks.

Features

Versatile integration

Raven AI integrates with multiple APIs and programming languages, easily incorporating it into existing workflows and systems. This flexibility allows businesses to adopt this technology without overhauling their current infrastructure.

With bindings available for C++, Python, and C# and integration with GStreamer, clients can easily embed Raven AI Engine into their existing codebases, eliminating the need for significant application changes. This ensures a smooth and efficient integration process, minimizing disruption while maximizing the technology’s value.

Features

Integration with gstreamer

If your application is based on GStreamer, Raven AI offers an optional module for direct integration with your GStreamer pipeline. Additionally, if desired, we can develop self-contained GStreamer plugins to perform your AI tasks fully isolatedly.

Features

Truly cross-platform

With Raven, the client code is written once and compiled without changes across multiple hardware environments and operating systems. It automatically selects the best available hardware on the machine, whether it’s AMD, Intel, or NVIDIA GPUs, ensuring an optimal workflow pipeline. Additionally, our engine supports video from multiple graphic APIs, such as DirectX12 and Vulkan, allowing for more efficient operation across a wide range of applications and systems.

// Inputs setting
fsai::Image image(file);
auto engine = fsai::EngineFactory::createByType("dml");
auto texture = engine->createTexture(image);

// Specific API setting
fsai::sports::strongsort::ModelDetectionFastGeneric model{“models”};
fsai::sports::strongsort::DetectorFastGeneric detector{modelGeneric, engine};

// Inference
auto detections = detector.push(texture);

// Get bounding boxes
for (auto& detection : detection){
   const auto& coords = detection.getBoundingBox() ; }

// Inputs setting
var image = Fsai.Image(file);
var engine = new Fsai.EngineFactory.CreateByType("dml");
var texture = engine.CreateTexture(image);

// Specific API setting
var model = Fsai.Sports.Strongsort.ModelDetectionFastGeneric(“models”);
var detector  =  Fsai.Sports.Strongsort.DetectorFastGeneric(model, engine);

// Inference
auto detections = detector.Push(texture);

// Get bounding boxes
foreach (Fsai.Sports.Strongsort.Object detection in detections){
   var coords = detection.GetBoundingBox();}

# Inputs setting

image = fsai.Image(file);
engine = fsai.Engine_factory.create_by_type("dml")
texture = engine.Create_texture(image)

# Specific API setting
model = fsai.sports.strongsort.model_detection_fast_generic(“models”)
detector  =  fsai.sports.strongsort.detector_fast_generic(model, engine)

# Inference
detections = detector.push(texture)

# Get bounding boxes
for detection in detections: 
coords = detection.get_bounding_box()