Artificial Intelligence (AI) software is computer technology designed to simulate human intelligence. It can be used to perform tasks that require cognitive abilities, such as problem-solving, data analysis, visual perception and language translation. AI applications range from voice recognition and virtual assistants to autonomous vehicles and medical diagnostics.
Data science software is a collection of tools and platforms designed to facilitate the analysis, interpretation, and visualization of large datasets, helping data scientists derive insights and build predictive models. These tools support various data science processes, including data cleaning, statistical analysis, machine learning, deep learning, and data visualization. Common features of data science software include data manipulation, algorithm libraries, model training environments, and integration with big data solutions. Data science software is widely used across industries like finance, healthcare, marketing, and technology to improve decision-making, optimize processes, and predict trends.
Computer vision software allows machines to interpret and analyze visual data from images or videos, enabling applications like object detection, image recognition, and video analysis. It utilizes advanced algorithms and deep learning techniques to understand and classify visual information, often mimicking human vision processes. These tools are essential in fields like autonomous vehicles, facial recognition, medical imaging, and augmented reality, where accurate interpretation of visual input is crucial. Computer vision software often includes features for image preprocessing, feature extraction, and model training to improve the accuracy of visual analysis. Overall, it enables machines to "see" and make informed decisions based on visual data, revolutionizing industries with automation and intelligence.
AI coding assistants are software tools that use artificial intelligence to help developers write, debug, and optimize code more efficiently. These assistants typically offer features like code auto-completion, error detection, suggestion of best practices, and code refactoring. AI coding assistants often integrate with integrated development environments (IDEs) and code editors to provide real-time feedback and recommendations based on the context of the code being written. By leveraging machine learning and natural language processing, these tools can help developers increase productivity, reduce errors, and learn new programming techniques.
Code search engines are specialized search tools that allow developers to search through codebases, repositories, or libraries to find specific functions, variables, classes, or code snippets. These tools are designed to help developers quickly locate relevant parts of code, analyze code quality, and identify reusable components. Code search engines often support various programming languages, providing search capabilities like syntax highlighting, filtering by file types or attributes, and even advanced search options using regular expressions. They are particularly useful for navigating large codebases, enhancing code reuse, and improving overall productivity in software development projects.
... recognize ID documents from 180+ countries and it can recognize all kinds of ID documents including ID card, Passport and Driver License.
SDKs we offer
- Face recognition SDK (Android, iOS, Server)
- Liveness detection SDK (Android, iOS, Server)
- ID document recognition SDK (Android, iOS, Server)
- Palmprint recognition SDK (Server)
Programming languages:
Python, Swift, Object C, Java, Kotlin, Flutter
We provide free integration support.
Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing...
Build better AI with multiple models. AI models are a new kind of building block. Sieve is the easiest way to use these building blocks to understand audio, generate video, and much more at scale. State-of-the-art models in just a few lines of code, and a curated set of production-ready apps for many use cases. Import your favorite models like Python packages. Visualize results with auto-generated interfaces built for your entire team. Deploy custom code with ease. Define your environment...
....) and model providers (OpenAI, Cohere, Huggingface, self-hosted models, etc.). Gain visibility into your LLM app's costs, latency, and chain of calls. You have the option to create simple LLM apps directly from the UI. However, if you would like to write customized applications, you need to write code with Python. Agenta is model agnostic and works with all model providers and frameworks. The only limitation at present is that our SDK is available only in Python.
Semantic Kernel is a lightweight, open-source development kit that lets you easily build AI agents and integrate the latest AI models into your C#, Python, or Java codebase. It serves as an efficient middleware that enables rapid delivery of enterprise-grade solutions. Microsoft and other Fortune 500 companies are already leveraging Semantic Kernel because it’s flexible, modular, and observable. Backed with security-enhancing capabilities like telemetry support, hooks, and filters you’ll feel...
... and horizontal scaling. Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.
... completion and an AI-powered chat and supports numerous use cases such as code generation, explanation of code, creating unit tests, generating documentation, debugging code, refactoring and maintaining code. Tabnine is integrated with all the major IDEs such as VS Code, JetBrains, Visual Studio, Eclipse. It supports over 80 programming languages and frameworks including JavaScript, Python, Java, PHP, Go, and more.
... program due to ready-made libraries with code examples (C#, Python, JS, GO, PHP). If there is no integration of your program with CapMonster Cloud the API of other services is also supported.
The browser extension automatically solves captchas in the background mode of Chrome/Firefox browser. Install it and forget about captchas.
Ship your idea in days, not weeks, with Databutton, the world's first fully AI app developer. Describe what you want, and use natural language, screenshots, or diagrams to get React UIs built by AI. Power your product with any service. Connect your app to any API or model to realize its full potential. Prompt Databutton to build Python APIs that scrape websites fetch data across systems, and more. Ship value to your customers continuously, we handle the security and infrastructure for you...
Lunary is an AI developer platform designed to help AI teams manage, improve, and protect Large Language Model (LLM) chatbots. It offers features such as conversation and feedback tracking, analytics on costs and performance, debugging tools, and a prompt directory for versioning and team collaboration. Lunary supports integration with various LLMs and frameworks, including OpenAI and LangChain, and provides SDKs for Python and JavaScript. Guardrails to deflect malicious prompts and sensitive...
... LLM providers and configurations. Literal AI integrates seamlessly with various LLM providers and AI frameworks, such as OpenAI, LangChain, and LlamaIndex, and provides SDKs in Python and TypeScript for easy instrumentation of code. The platform also supports the creation of experiments against datasets, facilitating continuous improvement and preventing regressions in LLM applications.
ExcelMaster.ai is an AI-powered Excel assistant that comprehends real-world Excel table structures, enabling it to generate accurate formulas and automate tasks through simple conversational inputs. It excels in handling complex formulas, VBA, and Python tasks, surpassing tools like Copilot and ChatGPT in data processing challenges. The platform offers a VBA Assistant for creating and refining customized VBA code, a Formula Assistant for precise formula generation, and Cell AI features...
... authentication with support for OAuth, API keys, JWT, and automatic token refresh; optimization for LLM function calling to ensure enterprise-grade reliability; support for over 20 agentic frameworks like Pippin, LangChain, and LlamaIndex; integration with more than 30 Web3 platforms, including Binance, Aave, OpenSea, and Chainlink; and SDKs and APIs for agentic app interactions, available in Python and TypeScript.
... faster performance on diffusion models, achieving sub-second generation times with FLUX and other popular models. Developers can integrate AI capabilities in minutes using intuitive SDKs and comprehensive API documentation, with support for Python, JavaScript, and REST API. Synexa offers enterprise-grade GPU infrastructure with A100s and H100s across three continents, ensuring sub-100ms latency with smart routing and a 99.9% uptime guarantee.
LiteLLM is a versatile platform designed to streamline interactions with over 100 Large Language Models (LLMs) through a unified interface. It offers both a Proxy Server (LLM Gateway) and a Python SDK, enabling developers to integrate various LLMs seamlessly into their applications. The Proxy Server facilitates centralized management, allowing for load balancing, cost tracking across projects, and consistent input/output formatting compatible with OpenAI standards. This setup supports multiple...
... than dealing with coding complexities. It offers flexible API management, enabling you to add or remove APIs for LLM access with simple clicks through its management console, customizing your setup based on your project's evolving needs without hassle. Additionally, Interlify provides a lightning-fast client setup, allowing integration into your project with just a few lines of code in Python or TypeScript, saving valuable time and effort.
... experience with features like hot-reloading, webhooks, and scheduled jobs, and supports scale-to-zero workloads by default. It provides volume storage options, GPU support, including running on Beam's cloud with GPUs like 4090s and H100s or bringing your own, and Python-native deployment without the need for YAML or config files.
... of building blocks so you spend less time learning docs and more time solving problems. Summit supports loops to cycle over lists, fetch paginated API data, and honor rate limits. Each model gets its own API and integrates with no‑code companions like Zapier, HubSpot, Make, Clay, or any tech stack (Python, PHP, Ruby, JavaScript). It promotes reusability and composability; models can call other models, so you can build once and reuse everywhere.
... by 40%. Its features include automated test generation, real-time feedback, and support for multiple programming languages such as Java, JavaScript, TypeScript, Kotlin, Python, and Go. BaseRock.ai offers flexible pricing plans, including a free tier, to accommodate various development needs. It is trusted by leading enterprises to enhance software quality and accelerate feature delivery.
..., including Java, Go, Python, Node.js, and C#, and can generate tests for various frameworks like JUnit, TestNG, pytest, and Go's standard testing package. It also facilitates the creation of ephemeral test environments on demand, streamlining acceptance testing and reducing the time and resources required for quality assurance. By automating repetitive testing tasks and enhancing test coverage, Roost.ai empowers development teams to focus on innovation and accelerate release cycles.
.... Built-in performance monitoring lets you compare models’ accuracy and cost-effectiveness, while secure key management centralizes API credentials under role-based controls. You can deploy LLM Gateway on your own infrastructure under the MIT license or use the hosted service as a progressive web app, and simple integration means you only need to change your API base URL, your existing code in any language or framework (cURL, Python, TypeScript, Go, etc.) continues to work without modification.