Best AI tools for< Manage Engineering Projects >

Spoke.ai is an AI tool designed to enhance communication and streamline workflow processes. The platform aims to help users communicate more effectively and build projects faster by leveraging artificial intelligence technology. By joining Salesforce to support Slack AI, Spoke.ai is at the forefront of revolutionizing the future of work. The tool offers various features to improve collaboration and productivity, making it a valuable asset for teams and individuals alike.

Slack is an AI-powered collaboration tool that revolutionizes communication within teams and organizations. It offers a platform for real-time messaging, file sharing, and project management, enhancing productivity and efficiency. With advanced AI capabilities, Slack automates tasks, provides insights, and streamlines workflows, making it a valuable tool for modern workplaces.

Siml.ai is a software platform designed for fast AI-driven physics simulations. It combines state-of-the-art machine learning with physics simulation to provide interactive visualization and high-performance AI-based numerical simulators. The platform offers tools like Model Engineer and Simulation Studio for training and optimizing simulators, managing datasets, and leveraging AI models for engineering and scientific problems. Siml.ai aims to democratize scientific-grade simulation tools by simplifying the development and deployment of physics-based simulations for engineers and researchers.

GitBook is a knowledge management platform that helps engineering teams centralize, access, and add to their technical knowledge in the tools they use every day. With GitBook, teams can capture knowledge from conversations, code, and meetings, and turn it into useful, readable documentation. GitBook also offers a variety of features to help teams collaborate on documentation, including a branch-based workflow, real-time editing, and user permissions.

Ocular is an AI-powered search platform that allows users to search, visualize, and take action on their work and engineering tools and data on one unified platform. It is designed to help engineers work more efficiently and effectively by providing them with a single, central location to access all of their relevant information.

Allex is a project management and portfolio management software that offers a digital multi-project solution. It helps project professionals accelerate time-to-market results by harmonizing projects, resources, and tasks. The platform enables users to have a sweeping overview of projects, predict capacity bottlenecks, improve project coordination through collaboration, and communicate effectively with internal and external stakeholders. Allex is designed to handle complex projects and offers expert support to ensure informed decision-making from planning to launch.

Head Energy is an independent engineering company that delivers projects, consultancy, products, and services primarily in the energy and construction sectors. They aim to promote sustainable solutions and make a positive impact by providing innovative engineering solutions for more efficient energy production, lower emissions, and improved infrastructure. With a focus on being a leading employer offering professional development, flexibility, and high expectations, Head Energy operates as a unique engineering group positioned in both the energy and construction industries.

EvolveLab is a digital solutions provider specializing in BIM management and app development for the AEC (Architecture, Engineering, and Construction) industry. They offer a range of powerful apps and services designed to empower architects, engineers, and contractors to streamline their workflows and bring their ideas to life more efficiently. With a focus on data-driven design and AI technology, EvolveLab's innovative tools help users enhance productivity and turn concepts into reality.

Slite is an AI-powered knowledge base application designed to streamline documentation, knowledge sharing, and information retrieval within teams and organizations. It offers a user-friendly interface, hassle-free adoption, and AI-driven search capabilities to enhance productivity and collaboration. With features like document creation, AI-powered search, templates, integrations with existing tools, and a library of resources, Slite aims to revolutionize the way teams manage and access information. The application caters to various departments such as Product & Engineering, Customer Support, Human Resources, and IT & Operations, providing a centralized platform for knowledge management and team communication.

DVC Studio is a collaboration tool for machine learning teams. It provides seamless data and model management, experiment tracking, visualization, and automation. DVC Studio is built for ML researchers, practitioners, and managers. It enables model organization and discovery across all ML projects and manages model lifecycle with Git, unifying ML projects with the best DevOps practices. DVC Studio also provides ML experiment tracking, visualization, collaboration, and automation using Git. It applies software engineering and DevOps best-practices to automate ML bookkeeping and model training, enabling easy collaboration and faster iterations.

Coherent Solutions is a custom software development and engineering company that offers a range of services including digital product engineering, artificial intelligence, data and analytics, mobile development, DevOps and cloud, quality assurance, Salesforce, and product design and UX. They work collaboratively and transparently with clients to deliver successful projects. The company values set the standard for all decisions, and they have a strong focus on understanding and meeting the business needs of their clients. Coherent Solutions has a strong track record of success in various industries and is known for empowering business success through global expertise and industry-specific product engineering consulting services.

Langtail is a platform that helps developers build, test, and deploy AI-powered applications. It provides a suite of tools to help developers debug prompts, run tests, and monitor the performance of their AI models. Langtail also offers a community forum where developers can share tips and tricks, and get help from other users.

Devin AI, developed by Cognition Labs, is the world's first fully autonomous AI software engineer. It streamlines software development by handling complex tasks, allowing engineers to focus on more challenging aspects. Devin AI possesses advanced programming skills, can manage complex tasks, understands and learns contextually, integrates with developer tools, and offers collaborative features. It can build and deploy applications, detect and fix bugs, contribute to open-source projects, train AI models, and handle GitHub repositories. Devin AI has demonstrated strong performance in issue resolution, surpassing previous AI models. It is currently in early access, with plans for future enhancements and integration with various development tools and platforms.

The Library Innovation Lab at Harvard University is an AI tool that focuses on bringing library principles to technological frontiers. It is a forward-looking group working at the intersection of libraries, technology, and law. The lab aims to democratize open knowledge and explore the use of generative AIs in information access and law. They offer various projects like Caselaw Access Project, H2O, The Nuremberg Project, Perma.cc, Alterspace, and Time Capsule Encryption to achieve their goals.

Febin AI Projects is a platform led by Febin, focusing on exploring emerging technology and developing products that enhance user experience. With a background in engineering, design, and writing, Febin now leads Partnerships at Tars, helping businesses modernize customer support through chatbots. The platform serves as a blog where Febin shares insights and experiments related to the future of AI applications.

Dart is an AI project management software designed to empower teams to work smarter, streamline tasks, and achieve more with less effort. It offers features like task execution, subtask generation, project planning, duplicate detection, roadmaps, calendar views, document management, integrations with workplace tools, and more. Dart is used by teams across various roles in organizations to drive focus, innovation, and impact. The tool's AI capabilities automate routine tasks, enhance project reporting, and provide actionable insights for better decision-making.

RealEngineers is an innovative AI-powered job platform that revolutionizes the engineering hiring process. Unlike traditional job sites, it focuses on detailed project-based profiles instead of resumes, leveraging AI to match skills and experiences with job requirements. Employers can evaluate candidates quickly and fairly, while candidates can showcase their authenticity and detailed projects to stand out. The platform aims to provide a level playing field for all candidates and help bridge the gap between talent and opportunity in the engineering industry.

Harness is an AI-driven software delivery platform that empowers software engineering teams with AI-infused technology for seamless software delivery. It offers a single platform for all software delivery needs, including DevOps modernization, continuous delivery, GitOps, feature flags, infrastructure as code management, chaos engineering, service reliability management, secure software delivery, cloud cost optimization, and more. Harness aims to simplify the developer experience by providing actionable insights on SDLC, secure software supply chain assurance, and AI development assistance throughout the software delivery lifecycle.

KUNGFU.AI is a management consulting and engineering firm focused exclusively on artificial intelligence. They empower CEOs and senior executives to leverage the full potential of AI to remain competitive in a rapidly evolving world. With 30+ years of AI expertise and 100+ projects delivered, they craft impactful, ethical, and cutting-edge solutions to solve tough challenges and drive measurable business results. KUNGFU.AI stands out for implementing AI strategies into production quickly, safely, and responsibly.

Engine is an AI software engineer tool designed for teams to streamline software development processes. It connects to popular project management tools like Jira, Trello, Linear, and more, automating tasks such as turning tickets into pull requests. Engine can complete up to 50% of tickets in minutes without supervision, helping teams ship faster and keep backlogs under control. It works seamlessly with existing workflows and tools, providing AI-powered engineering support to improve productivity and efficiency.

AI on OpenShift is a site providing installation recipes, patterns, and demos for AI/ML tools and applications used in Data Science and Data Engineering projects running on OpenShift. It serves as a comprehensive resource for developers looking to deploy AI solutions on the OpenShift platform.

Awesome AI & Data GitHub-Repos is a curated list of essential GitHub repositories covering the AI & ML landscape. It includes resources for Natural Language Processing, Large Language Models, Computer Vision, Data Science, Machine Learning, MLOps, Data Engineering, SQL & Database, and Statistics. The repository aims to provide a comprehensive collection of projects and resources for individuals studying or working in the field of AI and data science.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

AIDE @ unipi is a repository containing students' material for the course in Artificial Intelligence and Data Engineering at University of Pisa. It includes slides, students' notes, information about exams methods, oral questions, past exams, and links to past students' projects. The material is unofficial and created by students for students, checked only by students. Contributions are welcome through pull requests, issues, or contacting maintainers. The repository aims to provide non-profit resources for the course, with the opportunity for contributors to be acknowledged and credited. It also offers links to Telegram and WhatsApp groups for further interaction and a Google Drive folder with additional resources for AIDE published by past students.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

Fueling-Ambitions-Via-Book-Discoveries is an Advanced Machine Learning & AI Course designed for students, professionals, and AI researchers. The course integrates rigorous theoretical foundations with practical coding exercises, ensuring learners develop a deep understanding of AI algorithms and their applications in finance, healthcare, robotics, NLP, cybersecurity, and more. Inspired by MIT, Stanford, and Harvard’s AI programs, it combines academic research rigor with industry-standard practices used by AI engineers at companies like Google, OpenAI, Facebook AI, DeepMind, and Tesla. Learners can learn 50+ AI techniques from top Machine Learning & Deep Learning books, code from scratch with real-world datasets, projects, and case studies, and focus on ML Engineering & AI Deployment using Django & Streamlit. The course also offers industry-relevant projects to build a strong AI portfolio.

Latitude is an open-source prompt engineering platform that helps developers and product teams build AI features with confidence. It simplifies prompt management, aids in testing AI responses, and provides detailed analytics on request performance. Latitude offers collaborative prompt management, support for advanced features, version control, API and SDKs for integration, observability, evaluations in batch or real-time, and is community-driven. It can be deployed on Latitude Cloud for a managed solution or self-hosted for control and customization.

Comp AI is an open-source compliance automation platform designed to assist companies in achieving compliance with standards like SOC 2, ISO 27001, and GDPR. It transforms compliance into an engineering problem solved through code, automating evidence collection, policy management, and control implementation while maintaining data and infrastructure control.

Synmetrix (prev. MLCraft) is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube (Cube.js) for flexible data models that consolidate metrics from various sources, enabling downstream distribution via a SQL API for integration into BI tools, reporting, dashboards, and data science. Use cases include data democratization, business intelligence, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

Synmetrix is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube.js to consolidate metrics from various sources and distribute them downstream via a SQL API. Use cases include data democratization, business intelligence and reporting, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

EdgeChains is an open-source chain-of-thought engineering framework tailored for Large Language Models (LLMs)- like OpenAI GPT, LLama2, Falcon, etc. - With a focus on enterprise-grade deployability and scalability. EdgeChains is specifically designed to **orchestrate** such applications. At EdgeChains, we take a unique approach to Generative AI - we think Generative AI is a deployment and configuration management challenge rather than a UI and library design pattern challenge. We build on top of a tech that has solved this problem in a different domain - Kubernetes Config Management - and bring that to Generative AI. Edgechains is built on top of jsonnet, originally built by Google based on their experience managing a vast amount of configuration code in the Borg infrastructure.

KitOps is a packaging and versioning system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using. KitOps simplifies the handoffs between data scientists, application developers, and SREs working with LLMs and other AI/ML models. KitOps' ModelKits are a standards-based package for models, their dependencies, configurations, and codebases. ModelKits are portable, reproducible, and work with the tools you already use.

A curated list of Generative AI projects, tools, artworks, and models

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

Metaflow is a user-friendly library designed to assist scientists and engineers in developing and managing real-world data science projects. Initially created at Netflix, Metaflow aimed to enhance the productivity of data scientists working on diverse projects ranging from traditional statistics to cutting-edge deep learning. For further information, refer to Metaflow's website and documentation.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

Awesome-AI-Agents is a curated list of projects, frameworks, benchmarks, platforms, and related resources focused on autonomous AI agents powered by Large Language Models (LLMs). The repository showcases a wide range of applications, multi-agent task solver projects, agent society simulations, and advanced components for building and customizing AI agents. It also includes frameworks for orchestrating role-playing, evaluating LLM-as-Agent performance, and connecting LLMs with real-world applications through platforms and APIs. Additionally, the repository features surveys, paper lists, and blogs related to LLM-based autonomous agents, making it a valuable resource for researchers, developers, and enthusiasts in the field of AI.

Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.

LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. Here is an attempt to keep track of the initiatives around LangChain. Subscribe to the newsletter to stay informed about the Awesome LangChain. We send a couple of emails per month about the articles, videos, projects, and tools that grabbed our attention Contributions welcome. Add links through pull requests or create an issue to start a discussion. Please read the contribution guidelines before contributing.

Formal and detailed guide on ABB.

Provides expert guidance in transportation engineering projects.

Cloud Engineer Expert, guiding in cloud tech, projects, career, and industry trends.

Advises on water resources management and infrastructure development.

Advises on sustainable engineering solutions to environmental challenges.

Enhances manufacturing operations through advanced mechanical engineering expertise.

Civil Engineering expert

Engineering Director | Delivery Management, and Client Engagement | Bridging the Gap Between Technology and Business Goals

Premier expert in engineering, architecture, design, construction, and project management, powered by OpenAI

AI Assistant for Geotechnical Engineering

A replacement to your Product Manager, Engineering Manager, and your Average Developer and Tester

Ondersteun die skep van prompt vir ChatGpt - Afrikaanse taal

A focused and precise CTO advisor

Your communication assistant, TechTalker, simplifies messaging for software engineers with clarity and ease.

Friendly process engineer for Coda.io

Empowering beginners with lean construction knowledge by "Amir Zavichi"

Expert in BIM Integration, leading with precision and innovation - Powered by OpenAI

A 3D Printed Construction wiz trained on expert interviews. Use creatively, don't depend on 3DCP Guru GPT for factually accurate info (although it's pretty darn good)

A helpful guide for biochemical engineers, offering insights and reassurance.

Friendly, creative mechanical design specialist