Technology Stack

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We leverage the most advanced vision-language models, foundation models, and deep learning frameworks to build reliable, scalable, and cutting-edge AI solutions.

Vision Language Models

Multimodal AI That Sees and Understands

Vision-Language Models (VLMs) combine visual perception with language understanding, enabling capabilities from image captioning to visual question answering to complex reasoning about images. We deploy both proprietary and open-source VLMs depending on your requirements.

Proprietary VLM

GPT-4o / GPT-4V

OpenAI's flagship multimodal model for complex visual reasoning and understanding

Proprietary VLM

Claude 3.5 Vision

Anthropic's vision model with strong document understanding and analysis

Proprietary VLM

Gemini Vision

Google's multimodal model with native image understanding

Open Source VLM

LLaVA

Large Language and Vision Assistant - open source visual instruction tuning

Open Source VLM

Qwen-VL / Qwen2-VL

Alibaba's powerful open-source vision-language model series

Open Source VLM

InternVL

Shanghai AI Lab's scalable vision foundation model

Open Source VLM

CogVLM

Tsinghua's visual expert language model with strong grounding

Open Source VLM

LLaMA 3.2 Vision

Meta's open multimodal model for visual understanding

Foundation Models

Pre-trained on Billions of Images

Foundation models are pre-trained on massive datasets to learn general visual representations. We leverage these models for transfer learning, zero-shot classification, and as backbones for downstream tasks—dramatically reducing the data and compute needed for your specific application.

Contrastive

CLIP / OpenCLIP

Contrastive Language-Image Pre-training for zero-shot classification and retrieval

Contrastive

SigLIP

Google's improved CLIP variant with sigmoid loss for better scaling

Self-Supervised

DINOv2

Meta's self-supervised vision transformer with emergent properties

Segmentation

SAM / SAM2

Segment Anything Model for promptable, universal segmentation

Detection

Grounding DINO

Open-vocabulary detection with language grounding

Vision-Language

BLIP-2

Bootstrapping Language-Image Pre-training for efficient multimodal learning

Task-Specific Models

Specialized Architectures for Every Task

For specific computer vision tasks like detection, segmentation, and tracking, we deploy specialized architectures optimized for accuracy, speed, and efficiency. These models represent years of research distilled into production-ready solutions.

Detection

YOLOv8/v9/v10/v11

Real-time object detection family

Detection

RT-DETR

Real-time Detection Transformer

Detection

Co-DETR

Collaborative hybrid detection transformer

Segmentation

Mask2Former

Universal image segmentation

Segmentation

U-Net / nnU-Net

Medical image segmentation

Pose

ViTPose / RTMPose

Human pose estimation

Tracking

ByteTrack / BoT-SORT

Multi-object tracking

Depth

Depth Anything

Monocular depth estimation

Generative & Enhancement

Create, Enhance, and Transform

Generative models create new visual content while enhancement models improve existing images. From text-to-image generation to super-resolution to 3D reconstruction, these models enable powerful creative and restorative capabilities.

Text-to-Image

Stable Diffusion XL

High-quality image generation from text

Text-to-Image

FLUX

Black Forest Labs' next-gen image model

Controlled Gen

ControlNet

Adding conditional control to diffusion models

Super Resolution

Real-ESRGAN

Real-world image super resolution

Restoration

Restormer

Image restoration transformer

3D

3D Gaussian Splatting

Real-time novel view synthesis

3D

NeRF

Neural radiance fields for view synthesis

Video

Stable Video Diffusion

Video generation from images

Frameworks

Deep Learning Frameworks

We work with the most powerful and flexible frameworks in the industry.

PyTorch

Primary

Primary framework for research and production deep learning

Hugging Face

Primary

Transformers, datasets, and model hub ecosystem

TensorFlow

Enterprise deployments and TFLite mobile

JAX/Flax

High-performance research and TPU training

ONNX Runtime

Cross-platform model interoperability

TensorRT

NVIDIA GPU optimization for inference

OpenVINO

Intel hardware optimization

Core ML

Apple device deployment

Deployment

Flexible Deployment Options

Deploy models wherever they need to run - cloud, edge, or on-premise.

Cloud Deployment

Scalable cloud solutions on major platforms

  • AWS SageMaker & Bedrock
  • Google Vertex AI
  • Azure ML
  • Docker / Kubernetes
  • Serverless (Lambda, Cloud Functions)

Edge Deployment

Optimized inference on edge devices

  • NVIDIA Jetson (Orin, Xavier)
  • Qualcomm SNPE
  • Raspberry Pi 5
  • Mobile (iOS/Android)
  • Custom FPGA/ASIC

On-Premise

Secure deployments within your infrastructure

  • Private Cloud
  • Air-gapped Systems
  • GPU Clusters (A100, H100)
  • Triton Inference Server
  • TorchServe / BentoML
MLOps

Production-Ready Infrastructure

We use modern MLOps tools to ensure your models are production-ready from day one with proper versioning, monitoring, and continuous improvement.

MLflow

Experiment tracking and model registry

Weights & Biases

Experiment visualization and collaboration

DVC

Data and model version control

Kubeflow

ML pipelines on Kubernetes

NVIDIA Triton

High-performance model serving

Label Studio / CVAT

Data annotation platforms

Great Expectations

Data validation and quality

Evidently AI

ML monitoring and observability

Ready to Transform Your Vision?

Let's discuss how computer vision can solve your unique business challenges. Our team is ready to help you from concept to production.

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