Data & Analytics

Farmdar is an agritech company that leverages AI and satellite technology to provide comprehensive crop insights to agribusinesses globally. The platform offers solutions like CropScan for crop classification, YieldPro for yield analytics, eSurvey for land surveying, and DeveloPro for development insights. By covering over 500 million acres, Farmdar empowers businesses in sectors like sugar mills, seed and fertilizer companies, and lending institutions to make data-driven decisions, enhance productivity, and promote sustainable agricultural practices. Its technology helps identify crop location, acreage, variety, yield, harvesting time, and potential disease or pest attacks, alongside health, stress, nitrogen, soil organic matter, and moisture levels.

Deep Skin AI offers B2B AI software for skin analysis and product recommendations, specifically designed for skincare and cosmetics retailers and beauty brands. The platform seamlessly adapts to various devices, from smartphones to in-store kiosks, providing personalized skincare recommendations anytime, anywhere. It helps improve the customer journey in e-commerce by fighting decision fatigue and elevates the in-store experience by streamlining product selection. Additionally, it supports research and development by tracking and analyzing skin improvements to enhance product precision and effectiveness. The tool is GDPR-compliant, ensuring data privacy.

CodeFlying is an innovative AI-powered platform designed for "vibe coding," allowing users to build full-stack applications simply by describing their ideas to an AI. This no-code solution streamlines the app development process, enabling the creation of web apps, mobile apps, and even WeChat mini-programs in minutes. It aims to democratize app creation, making it accessible to individuals without extensive coding knowledge. The platform focuses on rapid prototyping and deployment, transforming conversational input into functional applications, marking a new era in app development.

Deix S.r.l. specializes in developing innovative algorithms and applications by leveraging expertise in mathematical modeling, artificial intelligence, and optimization. They provide solutions that enable companies to make informed decisions and identify new business opportunities. Deix offers both ready-to-use products and tailor-made solutions designed to meet specific business needs. Their approach integrates internal knowledge and data to deliver high-quality, efficient results, as evidenced by client testimonials highlighting speed, technical expertise, and proactivity in solving complex challenges.

Falcondale specializes in developing applied quantum machine learning and optimization solutions designed to deliver real-world impact. The company focuses on leveraging quantum intelligence to solve complex problems across various industries. Falcondale aims to provide a competitive edge through its advanced quantum technologies, offering solutions that go beyond traditional computational methods. Their expertise lies in translating cutting-edge quantum research into practical, deployable applications for businesses and organizations seeking innovative data analysis and optimization capabilities.

Edo specializes in energy and demand optimization, collaborating with utilities to convert commercial buildings into virtual power plants (VPPs). The platform integrates various distributed energy resources, such as HVAC, lighting, solar, batteries, and EV charging, to provide grid flexibility and reliability. Edo's AI-powered automation reduces manual work, optimizes energy use, and can cut peak demand by up to 15%. It offers solutions for office buildings, educational institutions, healthcare facilities, and municipalities, focusing on reducing energy consumption, improving occupant comfort, and meeting evolving performance standards. The technology works with existing building systems to enhance reliability, efficiency, and lower operational costs, supporting a decarbonized future.

StreamingVLM is an innovative AI tool designed for real-time understanding of effectively infinite video streams. Developed by mit-han-lab, it addresses common challenges in long-video analysis by maintaining a compact KV cache and aligning training directly with streaming inference. This approach efficiently avoids the quadratic cost associated with traditional methods and mitigates the pitfalls of sliding-window techniques. The system is capable of running at up to 8 frames per second (FPS) on a single H100 GPU, offering stable and efficient video processing. It has demonstrated superior performance, winning 66.18% against GPT-4o mini on a new long-video benchmark and also enhances general Video Question Answering (VQA) capabilities without requiring task-specific fine-tuning. The project provides scripts for environment setup, inference, supervised fine-tuning (SFT), and various evaluations including OVOBench and VQA tasks.

synthetic-data-kit is a powerful open-source tool developed by Meta Llama for generating high-quality synthetic datasets specifically designed to fine-tune Large Language Models. It streamlines the often complex process of data preparation, allowing users to create reasoning traces, QA pairs, and summaries from various input formats. The tool features a modular 4-command CLI flow: ingest, create, curate, and save-as, enabling users to process individual files or entire directories. It supports different LLM backends like vLLM or external API endpoints and can convert curated data into various fine-tuning formats such as Alpaca, OpenAI fine-tuning format, and ChatML. Additionally, it handles multimodal data, extracting both text and images, and offers intelligent chunking for large documents to maintain context and quality.

The synthetic-personality-dataset offers a high-fidelity collection of 10,000 synthetic records designed to simulate the behavioral and social patterns of introverted and extroverted individuals. Generated using Syncora.ai's synthetic data engine, this dataset ensures zero privacy risk while preserving real-world behavioral distributions. It is ideal for researchers, data scientists, and AI developers focused on personality prediction, behavioral modeling, machine learning experiments, and social science research. The dataset includes features like time spent alone, social event attendance, social media posting habits, and a personality target label, making it suitable for various analytical and ML use cases without compromising privacy or ethical concerns.

Table Transformer (TATR) is a deep learning model developed by Microsoft for extracting tables from unstructured documents, including PDFs and images. Based on object detection, TATR can be trained to work across various document domains, with pre-trained model weights available for the PubTables-1M dataset. The repository also provides the official code for the PubTables-1M dataset, a large-scale dataset for table detection, structure recognition, and functional analysis, and the GriTS evaluation metric for table structure recognition. Researchers and developers can use TATR to detect and recognize tables, convert them to HTML or CSV, and train custom models for specific needs.

deepmath is a deep-tech company specializing in advanced mathematical modeling, engineering simulations, and AI-enhanced engineering. It provides industry-grade services and next-gen engineering tools by combining advanced mathematical modeling, physics-based simulation, statistics, and AI. The company focuses on solving complex physical and operational challenges in industries such as renewable energy, offshore engineering, and marine engineering, particularly when standard tools or workflows are insufficient. deepmath offers solutions like Finite Element Methods (FEM), Computational Fluid Dynamics (CFD), Discrete Event Simulations (DES), and various forms of Artificial Intelligence (AI) to provide high-fidelity descriptions and predictions for optimizing design and operations. They also offer custom in-house tool development and support for startups and R&D teams.

torch-template-for-deep-learning is an open-source project providing PyTorch implementations of a wide array of classical backbone Convolutional Neural Networks (CNNs), alongside essential tools for deep learning development. It includes various data enhancement techniques like Cutout and Mixup, a collection of torch loss functions such as Focal Loss and Dice Loss, and numerous attention mechanisms including SE Attention and Self Attention. The template also features deployment modes for PyTorch models, conversion utilities from TensorFlow to PyTorch, and Class Activation Mapping (CAM) methods. This comprehensive resource aims to simplify and accelerate the development of deep learning applications by offering readily available and well-structured components.

Dragonfruit AI is an all-in-one enterprise AI platform specifically designed for retail, leveraging existing camera infrastructure to provide actionable intelligence. It employs computer vision and specialized AI agents to address critical retail functions such as shoplifting detection, queue management, checkout loss prevention, and customer journey insights. The platform offers a unified dashboard for centralized control across various applications and agents, making it easy for LP, Operations, and CX teams to manage. Dragonfruit AI is built for scalability and cost-effectiveness, integrating with existing VMS and camera systems even in low-bandwidth environments. Its patented split AI architecture focuses on edge-first processing to reduce bandwidth and cloud compute costs, making it an efficient solution for multi-location enterprises.

VideoLLaMA2 is an open-source project designed to significantly advance spatial-temporal modeling and audio understanding within video-Large Language Models (LLMs). It offers a comprehensive framework for researchers and developers to explore and build upon state-of-the-art video analysis capabilities. The tool provides various pre-trained models, including vision-only and audio-visual checkpoints, supporting tasks such as multi-choice video QA, video captioning, open-ended video QA, and audio-visual QA. It includes detailed instructions for installation, running online and offline demos, and quick-start guides for training and evaluating custom VideoLLaMA2 models using datasets like VideoLLaVA. The project emphasizes its top performance on leaderboards like MLVU and VideoMME for ~7B-sized VideoLLMs.

VideoLLM-online is the official implementation of an Online Video Large Language Model for Streaming Video, presented at CVPR 2024. Unlike traditional models that process full videos offline, VideoLLM-online enables real-time interaction within a video stream, allowing it to proactively update responses based on activity changes or assist with next steps. It features a cheap and scalable method for synthesizing streaming data by transforming offline annotations into dialogue data using open-source LLMs. The inference method is parallelized, combining video encoding, LLM forwarding, and response generation asynchronously, achieving high speeds of 10-15 FPS on an A100 GPU for long-form videos up to 10 minutes. The tool is designed for researchers and developers working with streaming video analysis and real-time multimodal AI.

VideoMamba is an innovative open-source state space model designed for efficient video understanding, specifically addressing the dual challenges of local redundancy and global dependencies in video data. It adapts the Mamba architecture to the video domain, overcoming limitations found in existing 3D convolution neural networks and video transformers. Its linear-complexity operator enables efficient long-term modeling, which is crucial for processing high-resolution and extended video content. The tool demonstrates scalability in the visual domain without requiring extensive dataset pretraining, thanks to a novel self-distillation technique. It also exhibits sensitivity for recognizing fine-grained short-term actions, superiority in long-term video understanding, and compatibility with multi-modal contexts, setting a new benchmark for comprehensive video analysis.

Epinote offers a comprehensive suite of services designed to plug people into workflows, specializing in data annotation, data collection, and project support. It enables businesses to save resources by delegating labor-intensive projects and simple tasks to its network of freelancers. The platform provides efficient data annotation for AI & ML, data collection, and various project support functions. Epinote helps companies accelerate go-to-market strategies, streamline back-office operations, and prepare data for AI initiatives. It offers tailored projects with bespoke workflows and supports various departments including sales, marketing, customer support, data, operations, HR, finance, and project management, aiming to double efficiency by replacing manual processes with a mix of technology and on-demand workforce.

ChatRecap AI is an AI-powered tool designed to analyze chat histories and provide valuable insights into communication patterns and relationship dynamics. By processing conversational data, it helps users gain a deeper understanding of their interactions, identify key themes, and recognize areas for improvement in their communication style. The tool aims to foster better relationships and enhance personal communication skills through data-driven analysis. It is particularly useful for individuals looking to reflect on their conversations and improve their interpersonal effectiveness.

DigyCorp provides advanced digital twin technology and an AI platform to enable secure collaboration, rapid adaptation, and sustainable progress. Their Nexus Digital Twin is outcome-motivated, data-driven, and enhanced by physics, offering real-time insights and optimized performance across complex systems. The Nexus AI Platform leverages machine learning for predictive simulations and data-driven decision-making. DigyCorp's solutions contribute to enhanced operational efficiency, predictive maintenance for cost savings, and sustainability through resource optimization. They also offer bespoke consultancy services, tailoring solutions to specific industry challenges in sectors like ecology, energy, transportation, aerospace, and smart housing.

The NC State Data Science and AI Academy provides comprehensive resources for individuals and organizations looking to enhance their capabilities in data science and artificial intelligence. The academy offers a range of courses designed to build foundational knowledge and advanced skills, alongside consulting services to help apply these concepts in real-world scenarios. It also supports research enablement, fostering innovation and practical application of data science principles. The academy's mission is to empower its participants to think critically and work effectively with data, exploring various applications of data science and AI across different domains.

SmartProxy is a leading global residential proxy service provider, offering access to over 100 million residential IPs across 200 countries. Engineered for reliable web data collection and AI workflows, it provides blazing-fast, enterprise-grade access to a vast network of IPs. The platform supports various proxy types including Residential Proxy, Unlimited Proxy, Static Residential Proxy, Static Data Center Proxy, and Long Acting ISP Proxy, catering to diverse business needs. SmartProxy also offers Web Scraper APIs for real-time structured data extraction and customized solutions. With features like free geolocation, real residential IPs, and no hidden fees, it ensures high success rates for scraping and automation. The service is optimized for AI/LLM data pipelines, offering stable and reliable connections for AI data operations, ad verification, price monitoring, social media management, e-commerce, and market research.

Dova Health Intelligence, formerly Docbot, specializes in AI-powered solutions for gastrointestinal (GI) health. Their platform utilizes advanced AI to analyze live image data, providing actionable insights for clinicians and researchers. Key products include DovaVision UC for granular disease severity analysis in Ulcerative Colitis from colonoscopy videos, DovaVision BE for real-time neoplasia detection and biopsy targeting in Barrett's Esophagus during gastroscopy, and DovaSound for rapid Inflammatory Bowel Disease activity assessment from intestinal ultrasound imaging. These tools aim to improve diagnostic precision, efficiency, and patient outcomes in GI care, with several products currently available for research and clinical trials, and others in development as medical devices.

Electra Vehicles, Inc. offers an AI-powered battery intelligence platform designed to optimize battery performance and accelerate the transition to sustainable power. Their EVE-AI™ Brain for Batteries provides total visibility and control, helping users cut costs, boost ROI, and extend battery lifespan while minimizing risk. The platform is applicable across various industries, including BESS (Battery Energy Storage Systems), EVs, robotics, and aviation. Key offerings include real-time monitoring, predictive maintenance, adaptive controls, and performance intelligence for applications ranging from fleet management to automotive OEMs and energy infrastructure. Electra's AI-driven BMS (Battery Management System) ensures proactive safety, reliability, and extended battery life.

Canvass AI is an advanced AI platform designed to transform disparate information into actionable insights, offering significant improvements in accuracy, speed, and cost-efficiency. It features AI Knowledge Engines that are fine-tuned for specific sectors such as Oil & Gas, Government, Finance, Healthcare, and Manufacturing. The platform provides industry-specific AI Assistants like Engineering Assistant, Patient Case Assistant, and Regulatory Clause Assistant, which are tailored to understand technical symbols, annotations, and specifications. Canvass AI ensures high accuracy through Human-in-the-Loop validation, self-learning, and performance evaluation to prevent hallucinations. It offers flexible deployment options, including on-premise and cloud, and integrates seamlessly with existing enterprise ecosystems for rapid adoption and immediate value, delivering proven results like 30-50% faster turnaround times and significant cost savings.