Best AI tools for< Evaluate Risks >

ZestyAI is an artificial intelligence tool that helps users make brilliant climate and property risk decisions. The tool uses AI to provide insights on property values and risk exposure to natural disasters. It offers products such as Property Insights, Digital Roof, Roof Age, Location Insights, and Climate Risk Models to evaluate and understand property risks. ZestyAI is trusted by top insurers in North America and aims to bring a ten times return on investment to its customers.

AnalyzeMe is an application that allows users to easily conduct PEST analysis. By entering industry and keywords, it provides detailed market environmental analysis. Users can use AnalyzeMe to reassess their business strategies.

Ergodic is an AI tool called Kepler that empowers businesses to make data-driven decisions. Kepler acts as an AI action engine, bridging the knowledge gap between business context and data insights. It goes beyond number crunching to help businesses build scenarios, evaluate outcomes, and take action based on objectives. With a focus on action-first approach, Kepler streamlines decision-making processes by providing actionable insights for optimizing processes, identifying opportunities, and mitigating risks.

Lumenova AI is an AI platform that focuses on making AI ethical, transparent, and compliant. It provides solutions for AI governance, assessment, risk management, and compliance. The platform offers comprehensive evaluation and assessment of AI models, proactive risk management solutions, and simplified compliance management. Lumenova AI aims to help enterprises navigate the future confidently by ensuring responsible AI practices and compliance with regulations.

The Frontier Model Forum (FMF) is a collaborative effort among leading AI companies to advance AI safety and responsibility. The FMF brings together technical and operational expertise to identify best practices, conduct research, and support the development of AI applications that meet society's most pressing needs. The FMF's core objectives include advancing AI safety research, identifying best practices, collaborating across sectors, and helping AI meet society's greatest challenges.

The Center for a New American Security (CNAS) is a bipartisan, non-profit think tank that focuses on national security and defense policy. CNAS conducts research, analysis, and policy development on a wide range of topics, including defense strategy, nuclear weapons, cybersecurity, and energy security. CNAS also provides expert commentary and analysis on current events and policy debates.

The AI PESTEL Analysis Generator is an AI-powered tool designed to help organizations understand and evaluate external macro-environmental factors that can impact their business operations. It allows users to input a company description and instantly generate a PESTEL Analysis, covering Political, Economic, Social, Technological, Environmental, and Legal factors. The tool provides insights to develop strategies and plans for adapting and succeeding in the marketplace.

Applicant AI is an applicant tracking and recruiting software powered by AI (ATS). It helps companies review job applicants 10x faster by using AI to screen thousands of applicants and identify the right candidates in seconds. The tool transforms the traditional applicant selection process, saving users 80% of the time spent on screening. With features like AI-generated summaries, ratings, and custom instructions evaluation, Applicant AI streamlines the hiring process and ensures only high-quality applicants are considered. The platform is compliant with EU AI regulation, prioritizes human decision-making, and aims to minimize risks of unfair or biased outcomes in employment.

Convr is an AI-driven underwriting analysis platform that helps commercial P&C insurance organizations transform their underwriting operations. It provides a modularized AI underwriting and intelligent document automation workbench that enriches and expedites the commercial insurance new business and renewal submission flow with underwriting insights, business classification, and risk scoring. Convr's mission is to solve the last big problem of commercial insurance while improving profitability and increasing efficiency.

Convr is a modularized AI underwriting and intelligent document automation workbench that enriches and expedites the commercial insurance new business and renewal submission flow with underwriting insights, business classification and risk scoring. As a trusted technology partner and advisor with deep industry expertise, we help insurance organizations transform their underwriting operations through our AI-driven digital underwriting analysis platform.

PolygrAI is a digital polygraph powered by AI technology that provides real-time risk assessment and sentiment analysis. The platform meticulously analyzes facial micro-expressions, body language, vocal attributes, and linguistic cues to detect behavioral fluctuations and signs of deception. By combining well-established psychology practices with advanced AI and computer vision detection, PolygrAI offers users actionable insights for decision-making processes across various applications.

iCAD is an AI-powered application designed for cancer detection, specifically focusing on breast cancer. The platform offers a suite of solutions including Detection, Density Assessment, and Risk Evaluation, all backed by science, clinical evidence, and proven patient outcomes. iCAD's AI-powered solutions aim to expose the hiding place of cancer, providing certainty and peace of mind, ultimately improving patient outcomes and saving more lives.

Career Copilot is an AI-powered hiring tool that helps recruiters and hiring managers find the best candidates for their open positions. The tool uses machine learning to analyze candidate profiles and identify those who are most qualified for the job. Career Copilot also provides a number of features to help recruiters streamline the hiring process, such as candidate screening, interview scheduling, and offer management.

Robust Intelligence is an end-to-end solution for securing AI applications. It automates the evaluation of AI models, data, and files for security and safety vulnerabilities and provides guardrails for AI applications in production against integrity, privacy, abuse, and availability violations. Robust Intelligence helps enterprises remove AI security blockers, save time and resources, meet AI safety and security standards, align AI security across stakeholders, and protect against evolving threats.

Web3 Summary is an AI-powered platform that simplifies on-chain research across multiple chains and protocols, helping users find trading alpha in the DeFi and NFT space. It offers a range of products including a trading terminal, wallet study tool, Discord bot, mobile app, and Chrome extension. The platform aims to streamline the process of understanding complex crypto projects and tokenomics using AI and ChatGPT technology.

Checkmyidea-IA is an AI-powered tool that helps entrepreneurs and businesses evaluate their business ideas before launching them. It uses a variety of factors, such as customer interest, uniqueness, initial product development, and launch strategy, to provide users with a comprehensive review of their idea's potential for success. Checkmyidea-IA can help users save time, increase their chances of success, reduce risk, and improve their decision-making.

Arthur is an industry-leading MLOps platform that simplifies deployment, monitoring, and management of traditional and generative AI models. It ensures scalability, security, compliance, and efficient enterprise use. Arthur's turnkey solutions enable companies to integrate the latest generative AI technologies into their operations, making informed, data-driven decisions. The platform offers open-source evaluation products, model-agnostic monitoring, deployment with leading data science tools, and model risk management capabilities. It emphasizes collaboration, security, and compliance with industry standards.

Robust Intelligence is an end-to-end security solution for AI applications. It automates the evaluation of AI models, data, and files for security and safety vulnerabilities and provides guardrails for AI applications in production against integrity, privacy, abuse, and availability violations. Robust Intelligence helps enterprises remove AI security blockers, save time and resources, meet AI safety and security standards, align AI security across stakeholders, and protect against evolving threats.

Traceable is an intelligent API security platform designed for enterprise-scale security. It offers unmatched API discovery, attack detection, threat hunting, and infinite scalability. The platform provides comprehensive protection against API attacks, fraud, and bot security, along with API testing capabilities. Powered by Traceable's OmniTrace Engine, it ensures unparalleled security outcomes, remediation, and pre-production testing. Security teams trust Traceable for its speed and effectiveness in protecting API infrastructures.

MarketGPT is an artificial intelligence model trained to predict stock movements based on news items. It evaluates the news and decides how the company stock is going to be affected by it. Users can access the model through the MarketGPT website or mobile app to get stock predictions and picks. The model's performance can be viewed for different time frames such as 1 week, 1 month, and 1 year. However, users are advised that investing in stocks and derivatives carries a risk of financial loss, and past performance is not a guarantee of future performance. MarketGPT is designed to assist users in making informed decisions in the stock market.

R-Judge is a benchmarking tool designed to evaluate the proficiency of Large Language Models (LLMs) in judging and identifying safety risks within diverse environments. It comprises 569 records of multi-turn agent interactions, covering 27 key risk scenarios across 5 application categories and 10 risk types. The tool provides high-quality curation with annotated safety labels and risk descriptions. Evaluation of 11 LLMs on R-Judge reveals the need for enhancing risk awareness in LLMs, especially in open agent scenarios. Fine-tuning on safety judgment is found to significantly improve model performance.

Purple Llama is an umbrella project that aims to provide tools and evaluations to support responsible development and usage of generative AI models. It encompasses components for cybersecurity and input/output safeguards, with plans to expand in the future. The project emphasizes a collaborative approach, borrowing the concept of purple teaming from cybersecurity, to address potential risks and challenges posed by generative AI. Components within Purple Llama are licensed permissively to foster community collaboration and standardize the development of trust and safety tools for generative AI.

Do-Not-Answer is an open-source dataset curated to evaluate Large Language Models' safety mechanisms at a low cost. It consists of prompts to which responsible language models do not answer. The dataset includes human annotations and model-based evaluation using a fine-tuned BERT-like evaluator. The dataset covers 61 specific harms and collects 939 instructions across five risk areas and 12 harm types. Response assessment is done for six models, categorizing responses into harmfulness and action categories. Both human and automatic evaluations show the safety of models across different risk areas. The dataset also includes a Chinese version with 1,014 questions for evaluating Chinese LLMs' risk perception and sensitivity to specific words and phrases.

This repository compiles a list of academic papers that evaluate, align, simulate, and provide surveys or perspectives on the use of Large Language Models (LLMs) in the field of Social Science. The papers cover various aspects of LLM research, including assessing their alignment with human values, evaluating their capabilities in tasks such as opinion formation and moral reasoning, and exploring their potential for simulating social interactions and addressing issues in diverse fields of Social Science. The repository aims to provide a comprehensive resource for researchers and practitioners interested in the intersection of LLMs and Social Science.

The Lakera PINT Benchmark provides a neutral evaluation method for prompt injection detection systems, offering a dataset of English inputs with prompt injections, jailbreaks, benign inputs, user-agent chats, and public document excerpts. The dataset is designed to be challenging and representative, with plans for future enhancements. The benchmark aims to be unbiased and accurate, welcoming contributions to improve prompt injection detection. Users can evaluate prompt injection detection systems using the provided Jupyter Notebook. The dataset structure is specified in YAML format, allowing users to prepare their datasets for benchmarking. Evaluation examples and resources are provided to assist users in evaluating prompt injection detection models and tools.

Beyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systems. It simplifies the process with automated integration, customizable evaluation metrics, and support for various Large Language Models (LLMs) tailored to specific needs. The aim is to reduce LLM hallucination risks and enhance reliability.

OpenRedTeaming is a repository focused on red teaming for generative models, specifically large language models (LLMs). The repository provides a comprehensive survey on potential attacks on GenAI and robust safeguards. It covers attack strategies, evaluation metrics, benchmarks, and defensive approaches. The repository also implements over 30 auto red teaming methods. It includes surveys, taxonomies, attack strategies, and risks related to LLMs. The goal is to understand vulnerabilities and develop defenses against adversarial attacks on large language models.

EvalPlus is a rigorous evaluation framework for LLM4Code, providing HumanEval+ and MBPP+ tests to evaluate large language models on code generation tasks. It offers precise evaluation and ranking, coding rigorousness analysis, and pre-generated code samples. Users can use EvalPlus to generate code solutions, post-process code, and evaluate code quality. The tool includes tools for code generation and test input generation using various backends.

hackingBuddyGPT is a framework for testing LLM-based agents for security testing. It aims to create common ground truth by creating common security testbeds and benchmarks, evaluating multiple LLMs and techniques against those, and publishing prototypes and findings as open-source/open-access reports. The initial focus is on evaluating the efficiency of LLMs for Linux privilege escalation attacks, but the framework is being expanded to evaluate the use of LLMs for web penetration-testing and web API testing. hackingBuddyGPT is released as open-source to level the playing field for blue teams against APTs that have access to more sophisticated resources.

This repository tracks the latest research on machine unlearning in large language models (LLMs). It offers a comprehensive list of papers, datasets, and resources relevant to the topic.

Awesome-LLM-in-Social-Science is a repository that compiles papers evaluating Large Language Models (LLMs) from a social science perspective. It includes papers on evaluating, aligning, and simulating LLMs, as well as enhancing tools in social science research. The repository categorizes papers based on their focus on attitudes, opinions, values, personality, morality, and more. It aims to contribute to discussions on the potential and challenges of using LLMs in social science research.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

Giskard is an open-source Python library that automatically detects performance, bias & security issues in AI applications. The library covers LLM-based applications such as RAG agents, all the way to traditional ML models for tabular data.

Awesome LLM Security is a curated collection of tools, documents, and projects related to Large Language Model (LLM) security. It covers various aspects of LLM security including white-box, black-box, and backdoor attacks, defense mechanisms, platform security, and surveys. The repository provides resources for researchers and practitioners interested in understanding and safeguarding LLMs against adversarial attacks. It also includes a list of tools specifically designed for testing and enhancing LLM security.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

This repository provides a curated list of open-source projects that implement instruction-tuned large language models (LLMs) with reinforcement learning from human feedback (RLHF). The projects are evaluated in terms of their openness across a predefined set of criteria in the areas of Availability, Documentation, and Access. The goal of this repository is to promote transparency and accountability in the development and deployment of LLMs.

torchchat is a codebase showcasing the ability to run large language models (LLMs) seamlessly. It allows running LLMs using Python in various environments such as desktop, server, iOS, and Android. The tool supports running models via PyTorch, chatting, generating text, running chat in the browser, and running models on desktop/server without Python. It also provides features like AOT Inductor for faster execution, running in C++ using the runner, and deploying and running on iOS and Android. The tool supports popular hardware and OS including Linux, Mac OS, Android, and iOS, with various data types and execution modes available.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

tinyllm is a lightweight framework designed for developing, debugging, and monitoring LLM and Agent powered applications at scale. It aims to simplify code while enabling users to create complex agents or LLM workflows in production. The core classes, Function and FunctionStream, standardize and control LLM, ToolStore, and relevant calls for scalable production use. It offers structured handling of function execution, including input/output validation, error handling, evaluation, and more, all while maintaining code readability. Users can create chains with prompts, LLM models, and evaluators in a single file without the need for extensive class definitions or spaghetti code. Additionally, tinyllm integrates with various libraries like Langfuse and provides tools for prompt engineering, observability, logging, and finite state machine design.

Advisor on Security Token Offerings, providing insights without financial advice. Powered by Magic Circle

Provides detailed STEEPLE analysis with a formal tone.

FinWiz-GPT is designed for finance professionals. It assists in market analysis, financial modeling, and understanding complex financial instruments. It's a great tool for financial analysts, investment bankers, and accountants.

Expert in war game strategy using maps.

Apply gold-standard startup valuation and assessment methods to identify risks and gaps in your business model and product ideas.

Helps you evaluate whether or not tech platforms match UNESCO's Internet for Trust Guidelines for the Governance of Digital Platforms

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ERC20 analyst focusing on mintability, holders, LP tokens, and risks, with clear, conversational explanations.

A professional assistant for technical item evaluations in banking

Risk assessment advisor for business challenges

Advisor for safe LLM integration using OWASP guidelines

AI advisor for tech resilience, balancing strategy and technicality

Assesses and recommends potential suppliers for organizational needs.

투자실패사례들을 바탕으로 벤처투자/벤처경영 고민을 상담합니다.

SWOT GPT specializes in conducting detailed SWOT analyses across various industries. Utilizing strategic insights, it identifies strengths, weaknesses, opportunities, and threats for businesses and projects. Ideal for entrepreneurs and marketers seeking competitive advantage.

構造体の写真を入力すると、弱点部位を分析してくれます

Advises on maximizing project benefits post-project closure.

Simulates and analyzes potential environmental disaster scenarios for preparedness.

Analyzing global health data for pandemic prediction and strategy.

I critically assess business ideas with witty feedback.