Best AI tools for< Feasibility Analyst >

ACHIV is an AI tool for ideas validation and market research. It helps businesses make informed decisions based on real market needs by providing data-driven insights. The tool streamlines the market validation process, allowing quick adaptation and refinement of product development strategies. ACHIV offers a revolutionary approach to data collection and preprocessing, along with proprietary AI models for smart analysis and predictive forecasting. It is designed to assist entrepreneurs in understanding market gaps, exploring competitors, and enhancing investment decisions with real-time data.

HelloData is an AI-powered platform designed for multifamily professionals in the real estate industry. It offers automated rent surveys, effective rent calculation, historical rent trends, expense benchmarks, and development feasibility analysis. The platform provides unlimited market surveys with competitor leasing trends, concessions, fees, and amenities, helping users optimize rents and grow net operating income. HelloData saves time by automating market surveys, reducing report times, and providing nationwide access to real-time data. It is a comprehensive toolbox that eliminates manual surveys and offers accurate data for real estate analysis.

The Intelligent Automation Leadership Community is an AI-powered platform that serves as a portal for RPA Master automation. It helps users plan, launch, and scale their process automation programs. The platform offers the latest AI and automation technology insights, inspiring success stories, and feasibility assessment for automation projects. Users can also get advice from online AI & Automation Advisor, participate in automation projects, and share their expertise within the community.

SaaSlidator is an AI-powered application designed to help users validate their project ideas efficiently and effectively. By providing a project name and description, SaaSlidator offers valuable insights to support decision-making on whether to proceed with building and launching a minimum viable product (MVP). The platform leverages AI algorithms to analyze data, offer market demand insights, competition analysis, and assess the feasibility of project ideas. With features like rapid validation, monetization suggestions, and benchmarking data, SaaSlidator aims to streamline the idea validation process and empower users to make informed decisions for successful project development.

Architechtures is a generative AI-powered building design platform that helps architects and real estate developers design optimal residential developments in minutes. The platform uses AI technology to provide real-time architectural solutions based on user input design criteria. It facilitates collaboration between users and AI, allowing for quick iterations and accurate insights. Architechtures aims to streamline the building design process by optimizing feasibility analysis, providing instant insights, and generating standardized BIM models for further development.

Archistar is a leading property research platform in Australia that empowers users to make confident and compliant property decisions with the help of data and AI. It offers a range of features, including the ability to find and assess properties, generate 3D design concepts, and minimize risk and maximize return on investment. Archistar is trusted by over 100,000 individuals and 1,000 leading property firms.

Archistar is a leading property research platform that utilizes data and AI to help investors, developers, architects, and government officials make confident and compliant decisions. The platform offers features such as finding the best use of a site, researching real estate rules and risks, generating 3D design concepts with AI, and fast-tracking building permit assessments. With over 100,000 users, Archistar provides access to advanced algorithms, filters, and market insights to discover real estate opportunities efficiently.

TestFit is a real estate feasibility platform that uses AI to help developers, architects, contractors, and brokers evaluate deals and make better decisions. It provides real-time insights into design, cost, and constructability, and integrates with a variety of other software tools. TestFit can help users save time and money, and make more informed decisions about their real estate projects.

TestFit is a real estate feasibility platform that helps users maximize site potential and get the right deals done faster. It uses real-time AI for rapid iterations, allowing users to evaluate deals in hours instead of weeks. TestFit also integrates with other software, such as Revit and Enscape, to streamline the design and documentation process.

AskGiraffe is an AI-powered mentorship platform that provides instant feedback and strategic guidance to aspiring entrepreneurs. Users can input their business ideas and receive a concise feasibility assessment to help turn their ideas into reality. The platform caters to various industries such as technology, e-commerce, healthcare, and arts, offering tailored support for each sector. AskGiraffe aims to empower individuals by assisting them in transforming their innovative concepts into successful ventures.

Altamira is an AI-driven software development company that offers a wide range of services including software discovery, ideation, audit, consulting, and development. They specialize in AI feasibility studies, AI development, dataOps pipelines, and pre-built AI/ML models. Altamira focuses on providing holistic care for digital solutions, with expertise in various industries such as fintech, retail, healthcare, and more. They aim to optimize software development processes for established businesses, startups, and spinoffs by offering tailored solutions that make a tangible impact on growth and productivity.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

ChatGPT Queue Prompts is a collection of prompt chains designed to enhance interactions with large language models like ChatGPT. These prompt chains help build context for the AI before performing specific tasks, improving performance. Users can copy and paste prompt chains into the ChatGPT Queue extension to process prompts in sequence. The repository includes example prompt chains for tasks like conducting AI company research, building SEO optimized blog posts, creating courses, revising resumes, enriching leads for CRM, personal finance document creation, workout and nutrition plans, marketing plans, and more.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

Awesome LLM for APR is a repository dedicated to exploring the capabilities of Large Language Models (LLMs) in Automated Program Repair (APR). It provides a comprehensive collection of research papers, tools, and resources related to using LLMs for various scenarios such as repairing semantic bugs, security vulnerabilities, syntax errors, programming problems, static warnings, self-debugging, type errors, web UI tests, smart contracts, hardware bugs, performance bugs, API misuses, crash bugs, test case repairs, formal proofs, GitHub issues, code reviews, motion planners, human studies, and patch correctness assessments. The repository serves as a valuable reference for researchers and practitioners interested in leveraging LLMs for automated program repair.

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

This repository provides an LLM-driven audio codec model, LLM-Codec, for building multi-modal LLMs (text and audio modalities). The model enables frozen LLMs to achieve multiple audio tasks in a few-shot style without parameter updates. It compresses the audio modality into a well-trained LLMs token space, treating audio representation as a 'foreign language' that LLMs can learn with minimal examples. The proposed approach supports tasks like speech emotion classification, audio classification, text-to-speech generation, speech enhancement, etc., demonstrating feasibility and effectiveness in simple scenarios. The LLM-Codec model is open-sourced to facilitate research on few-shot audio task learning and multi-modal LLMs.

QA-Pilot is an interactive chat project that leverages online/local LLM for rapid understanding and navigation of GitHub code repository. It allows users to chat with GitHub public repositories using a git clone approach, store chat history, configure settings easily, manage multiple chat sessions, and quickly locate sessions with a search function. The tool integrates with `codegraph` to view Python files and supports various LLM models such as ollama, openai, mistralai, and localai. The project is continuously updated with new features and improvements, such as converting from `flask` to `fastapi`, adding `localai` API support, and upgrading dependencies like `langchain` and `Streamlit` to enhance performance.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

The 'fms-fsdp' repository is a companion to the Foundation Model Stack, providing a (pre)training example to efficiently train FMS models, specifically Llama2, using native PyTorch features like FSDP for training and SDPA implementation of Flash attention v2. It focuses on leveraging FSDP for training efficiently, not as an end-to-end framework. The repo benchmarks training throughput on different GPUs, shares strategies, and provides installation and training instructions. It trained a model on IBM curated data achieving high efficiency and performance metrics.

NanoFlow is a throughput-oriented high-performance serving framework for Large Language Models (LLMs) that consistently delivers superior throughput compared to other frameworks by utilizing key techniques such as intra-device parallelism, asynchronous CPU scheduling, and SSD offloading. The framework proposes nano-batching to schedule compute-, memory-, and network-bound operations for simultaneous execution, leading to increased resource utilization. NanoFlow also adopts an asynchronous control flow to optimize CPU overhead and eagerly offloads KV-Cache to SSDs for multi-round conversations. The open-source codebase integrates state-of-the-art kernel libraries and provides necessary scripts for environment setup and experiment reproduction.

Enhanced GPT for in-depth feasibility studies, powered by OpenAI

Creates detailed feasibility reports with online research and images.

An economic advisor for loan feasibility, market analysis, and investment advice.

A certified advisor for personalized fundraising and feasibility studies.

Fund Development Consultant for non-profit charities, specializing in Fundraising and Philanthropy.