Best AI tools for< Data Ingest >

SingleStore is a real-time data platform designed for apps, analytics, and gen AI. It offers faster hybrid vector + full-text search, fast-scaling integrations, and a free tier. SingleStore can read, write, and reason on petabyte-scale data in milliseconds. It supports streaming ingestion, high concurrency, first-class vector support, record lookups, and more.

SID is a data ingestion, storage, and retrieval pipeline that provides real-time context for AI applications. It connects to various data sources, handles authentication and permission flows, and keeps information up-to-date. SID's API allows developers to retrieve the right piece of data for a given task, enabling them to build AI apps that are fast, accurate, and scalable. With SID, developers can focus on building their products and leave the data management to SID.

Databricks is a data and AI company that provides a unified platform for data, analytics, and AI. The platform includes a variety of tools and services for data management, data warehousing, real-time analytics, data engineering, data science, and AI development. Databricks also offers a variety of integrations with other tools and services, such as ETL tools, data ingestion tools, business intelligence tools, AI tools, and governance tools.

Ragie is a fully managed RAG-as-a-Service platform designed for developers. It offers easy-to-use APIs and SDKs to help developers get started quickly, with advanced features like LLM re-ranking, summary index, entity extraction, flexible filtering, and hybrid semantic and keyword search. Ragie allows users to connect directly to popular data sources like Google Drive, Notion, Confluence, and more, ensuring accurate and reliable information delivery. The platform is led by Craft Ventures and offers seamless data connectivity through connectors. Ragie simplifies the process of data ingestion, chunking, indexing, and retrieval, making it a valuable tool for AI applications.

Cognee is an AI application that helps users build deterministic AI memory by perfecting exceptional AI apps with intelligent data management. It acts as a semantic memory layer, uncovering hidden connections within data and infusing it with company-specific language and principles. Cognee offers data ingestion and enrichment services, resulting in relevant data retrievals and lower infrastructure costs. The application is suitable for various industries, including customer engagement, EduTech, company onboarding, recruitment, marketing, and tourism.

Jacquard is an AI-powered platform that offers hyper-personalized brand messaging at scale. It provides a core platform for generating brand-safe messaging, along with add-ons for audience optimization and personalized campaigns. The technology is designed to resonate with people by tailoring messaging to individual customer contexts. Jacquard's expert language calibration and trusted content generation ensure sustained brand affinity and high engagement levels. The platform integrates seamlessly with existing tech stacks and offers real-time API and data ingestion for continuous optimization.

Qualifyed is an AI predictive audiences and lead scoring platform designed to help businesses optimize their advertising efforts by targeting people with the highest probability to become customers. The platform utilizes industry-leading machine learning systems to continuously inspect and score leads, ultimately decreasing customer acquisition costs, increasing online conversions, and enhancing offline sales team efficiency. Qualifyed offers a streamlined process of data ingestion, machine learning modeling, advertising optimization, and media execution to reach ideal customers effectively. With a focus on driving qualified leads, Qualifyed aims to revolutionize the way businesses approach media buying for sales.

Clari is a revenue operations platform that helps businesses track, forecast, and analyze their revenue performance. It provides a unified view of the revenue process, from lead generation to deal closing, and helps businesses identify and address revenue leaks. Clari is powered by AI and machine learning, which helps it to automate tasks, provide insights, and make recommendations. It is used by businesses of all sizes, from startups to large enterprises.

Asktro is an AI tool that brings natural language search and an AI assistant to static documentation websites. It offers a modern search experience powered by embedded text similarity search and large language models. Asktro provides a ready-to-go search UI, plugin for data ingestion and indexing, documentation search, and an AI assistant for answering specific questions.

BalancedWork is an AI-powered platform that helps teams make data-driven decisions about when to work in-person. By combining AI and Social Science, the application addresses challenges such as frustrating virtual meetings, decreasing employee satisfaction due to mandates, weakening team bonds, and underutilized office spaces. BalancedWork offers solutions like ingesting data from enterprise APIs, evaluating work patterns, generating team schedules, and providing ongoing recommendations to adapt to changing needs. The platform aims to boost productivity, engagement, and collaboration in organizations by optimizing work interactions and relationships.

Doctrine is an AI-powered application that allows users to add AI-powered Q&A features to their apps in minutes. It leverages knowledge from data or knowledge bases to answer user questions or embed AI features. With the ability to ingest content from various sources like websites, documents, and images, Doctrine simplifies the process of knowledge extraction and enables seamless integration of AI capabilities into applications.

PandasAI is an open-source AI tool designed for conversational data analysis. It allows users to ask questions in natural language to their enterprise data and receive real-time data insights. The tool is integrated with various data sources and offers enhanced analytics, actionable insights, detailed reports, and visual data representation. PandasAI aims to democratize data analysis for better decision-making, offering enterprise solutions for stable and scalable internal data analysis. Users can also fine-tune models, ingest universal data, structure data automatically, augment datasets, extract data from websites, and forecast trends using AI.

The website offers a precision workflow solution for enterprises in the finance industry, combining AI technology with human oversight to empower financial decisions. It provides features such as accurate search citations, multilingual models, and complex human-in-loop automation. The application integrates seamlessly with existing platforms, uses advanced AI models, and offers meaningful time savings. Users can benefit from the application's ability to ingest unstructured data, improve over time, and avoid hallucinations.

IngestAI is a Silicon Valley-based startup that provides a sophisticated toolbox for data preparation and model selection, powered by proprietary AI algorithms. The company's mission is to make AI accessible and affordable for businesses of all sizes. IngestAI's platform offers a turn-key service tailored for AI builders seeking to optimize AI application development. The company identifies the model best-suited for a customer's needs, ensuring it is designed for high performance and reliability. IngestAI utilizes Deepmark AI, its proprietary software solution, to minimize the time required to identify and deploy the most effective AI solutions. IngestAI also provides data preparation services, transforming raw structured and unstructured data into high-quality, AI-ready formats. This service is meticulously designed to ensure that AI models receive the best possible input, leading to unparalleled performance and accuracy. IngestAI goes beyond mere implementation; the company excels in fine-tuning AI models to ensure that they match the unique nuances of a customer's data and specific demands of their industry. IngestAI rigorously evaluates each AI project, not only ensuring its successful launch but its optimal alignment with a customer's business goals.

Fleak AI Workflows is a low-code serverless API Builder designed for data teams to effortlessly integrate, consolidate, and scale their data workflows. It simplifies the process of creating, connecting, and deploying workflows in minutes, offering intuitive tools to handle data transformations and integrate AI models seamlessly. Fleak enables users to publish, manage, and monitor APIs effortlessly, without the need for infrastructure requirements. It supports various data types like JSON, SQL, CSV, and Plain Text, and allows integration with large language models, databases, and modern storage technologies.

LlamaIndex is a framework for building context-augmented Large Language Model (LLM) applications. It provides tools to ingest and process data, implement complex query workflows, and build applications like question-answering chatbots, document understanding systems, and autonomous agents. LlamaIndex enables context augmentation by combining LLMs with private or domain-specific data, offering tools for data connectors, data indexes, engines for natural language access, chat engines, agents, and observability/evaluation integrations. It caters to users of all levels, from beginners to advanced developers, and is available in Python and Typescript.

Mendable is an AI-powered search tool that helps businesses answer customer and employee questions by training a secure AI on their technical resources. It offers a variety of features such as answer correction, custom prompt edits, and model creativity control, allowing businesses to customize the AI to fit their specific needs. Mendable also provides enterprise-grade security features such as RBAC, SSO, and BYOK, ensuring the security and privacy of sensitive data.

Videograph is an AI-powered video platform that offers a wide range of video APIs for live and on-demand video streaming. It provides advanced features such as video encoding, live streaming, monetization, content distribution analytics, and portrait conversion. With seamless organization through Digital Asset Management, Videograph enables users to transcode videos in 4K, archive with low-res preview, tag content, and utilize Dolby Vision and Dolby Audio technologies. The AI cropping tool automatically converts landscape videos to portrait ratio for social media. Elevate broadcasts with low-latency live streams, real-time analytics, and Server-Side Ad Insertion for monetization. The platform also offers insights on partner-wise analytics, EPG programs, and ad performance trends. Videograph's plug-and-play APIs support video ingestion, processing, and delivery, enhancing the streaming experience with subtitles, thumbnails, and more.

Loata is an AI-powered platform that serves as a learning orchestrator for adaptive text analyses. It allows users to store their notes and documents in the cloud, which are then ingested and transformed into knowledge bases. The platform features smart AI agents powered by LLMs to provide intelligent answers based on the content. With end-to-end encryption and controlled ingestion, Loata ensures the security and privacy of user data. Users can choose from different subscription plans to access varying levels of storage and query capacity, making it suitable for individuals and professionals alike.

Canoe is a cloud-based platform that leverages machine learning technology to automate document collection, data extraction, and data science initiatives for alternative investments. It transforms complex documents into actionable intelligence within seconds, empowering allocators with tools to unlock new efficiencies for their business. Canoe is trusted by thousands of alternative investors, allocators, wealth management, and asset servicers to improve efficiency, accuracy, and completeness of investment data.

The `unstructured` library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more. The use cases of `unstructured` revolve around streamlining and optimizing the data processing workflow for LLMs. `unstructured` modular functions and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and efficient in transforming unstructured data into structured outputs.

Wandbot is a question-answering bot designed for Weights & Biases documentation. It employs Retrieval Augmented Generation with a ChromaDB backend for efficient responses. The bot features periodic data ingestion, integration with Discord and Slack, and performance monitoring through logging. It has a fallback mechanism for model selection and is evaluated based on retrieval accuracy and model-generated responses. The implementation includes creating document embeddings, constructing the Q&A RAGPipeline, model selection, deployment on FastAPI, Discord, and Slack, logging and analysis with Weights & Biases Tables, and performance evaluation.

LangDrive is an open-source AI library that simplifies training, deploying, and querying open-source large language models (LLMs) using private data. It supports data ingestion, fine-tuning, and deployment via a command-line interface, YAML file, or API, with a quick, easy setup. Users can build AI applications such as question/answering systems, chatbots, AI agents, and content generators. The library provides features like data connectors for ingestion, fine-tuning of LLMs, deployment to Hugging Face hub, inference querying, data utilities for CRUD operations, and APIs for model access. LangDrive is designed to streamline the process of working with LLMs and making AI development more accessible.

Neo4j Runway is a Python library that simplifies the process of migrating relational data into a graph. It provides tools to abstract communication with OpenAI for data discovery, generate data models, ingestion code, and load data into a Neo4j instance. The library leverages OpenAI LLMs for insights, Instructor Python library for modeling, and PyIngest for data loading. Users can visualize data models using graphviz and benefit from a seamless integration with Neo4j for efficient data migration.

SambaNova AI Starter Kits is a collection of open-source examples and guides designed to facilitate the deployment of AI-driven use cases for developers and enterprises. The kits cover various categories such as Data Ingestion & Preparation, Model Development & Optimization, Intelligent Information Retrieval, and Advanced AI Capabilities. Users can obtain a free API key using SambaNova Cloud or deploy models using SambaStudio. Most examples are written in Python but can be applied to any programming language. The kits provide resources for tasks like text extraction, fine-tuning embeddings, prompt engineering, question-answering, image search, post-call analysis, and more.

Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.

Ask Astro is an open-source reference implementation of Andreessen Horowitz's LLM Application Architecture built by Astronomer. It provides an end-to-end example of a Q&A LLM application used to answer questions about Apache Airflow® and Astronomer. Ask Astro includes Airflow DAGs for data ingestion, an API for business logic, a Slack bot, a public UI, and DAGs for processing user feedback. The tool is divided into data retrieval & embedding, prompt orchestration, and feedback loops.

Databend is an open-source cloud data warehouse built in Rust, offering fast query execution and data ingestion for complex analysis of large datasets. It integrates with major cloud platforms, provides high performance with AI-powered analytics, supports multiple data formats, ensures data integrity with ACID transactions, offers flexible indexing options, and features community-driven development. Users can try Databend through a serverless cloud or Docker installation, and perform tasks such as data import/export, querying semi-structured data, managing users/databases/tables, and utilizing AI functions.

VectorFlow is an open source, high throughput, fault tolerant vector embedding pipeline. It provides a simple API endpoint for ingesting large volumes of raw data, processing, and storing or returning the vectors quickly and reliably. The tool supports text-based files like TXT, PDF, HTML, and DOCX, and can be run locally with Kubernetes in production. VectorFlow offers functionalities like embedding documents, running chunking schemas, custom chunking, and integrating with vector databases like Pinecone, Qdrant, and Weaviate. It enforces a standardized schema for uploading data to a vector store and supports features like raw embeddings webhook, chunk validation webhook, S3 endpoint, and telemetry. The tool can be used with the Python client and provides detailed instructions for running and testing the functionalities.

LLM Finetuning toolkit is a config-based CLI tool for launching a series of LLM fine-tuning experiments on your data and gathering their results. It allows users to control all elements of a typical experimentation pipeline - prompts, open-source LLMs, optimization strategy, and LLM testing - through a single YAML configuration file. The toolkit supports basic, intermediate, and advanced usage scenarios, enabling users to run custom experiments, conduct ablation studies, and automate fine-tuning workflows. It provides features for data ingestion, model definition, training, inference, quality assurance, and artifact outputs, making it a comprehensive tool for fine-tuning large language models.

Docq is a private and secure GenAI tool designed to extract knowledge from business documents, enabling users to find answers independently. It allows data to stay within organizational boundaries, supports self-hosting with various cloud vendors, and offers multi-model and multi-modal capabilities. Docq is extensible, open-source (AGPLv3), and provides commercial licensing options. The tool aims to be a turnkey solution for organizations to adopt AI innovation safely, with plans for future features like more data ingestion options and model fine-tuning.

Chat with your data using OpenAI and AI Search. This solution accelerator uses an Azure OpenAI GPT model and an Azure AI Search index generated from your data, which is integrated into a web application to provide a natural language interface, including speech-to-text functionality, for search queries. Users can drag and drop files, point to storage, and take care of technical setup to transform documents. There is a web app that users can create in their own subscription with security and authentication.

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

Data Prep Kit is a community project aimed at democratizing and speeding up unstructured data preparation for LLM app developers. It provides high-level APIs and modules for transforming data (code, language, speech, visual) to optimize LLM performance across different use cases. The toolkit supports Python, Ray, Spark, and Kubeflow Pipelines runtimes, offering scalability from laptop to datacenter-scale processing. Developers can contribute new custom modules and leverage the data processing library for building data pipelines. Automation features include workflow automation with Kubeflow Pipelines for transform execution.

Airbroke is an open-source error catcher tool designed for modern web applications. It provides a PostgreSQL-based backend with an Airbrake-compatible HTTP collector endpoint and a React-based frontend for error management. The tool focuses on simplicity, maintaining a small database footprint even under heavy data ingestion. Users can ask AI about issues, replay HTTP exceptions, and save/manage bookmarks for important occurrences. Airbroke supports multiple OAuth providers for secure user authentication and offers occurrence charts for better insights into error occurrences. The tool can be deployed in various ways, including building from source, using Docker images, deploying on Vercel, Render.com, Kubernetes with Helm, or Docker Compose. It requires Node.js, PostgreSQL, and specific system resources for deployment.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

This project enables you to fetch liked tweets from Twitter (using Selenium), save it to JSON and Excel files, and perform initial data analysis and image captions. This is part of the initial steps for a larger personal project involving Large Language Models (LLMs).

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

FarmVibes.AI is a repository focused on developing multi-modal geospatial machine learning models for agriculture and sustainability. It enables users to fuse various geospatial and spatiotemporal datasets, such as satellite imagery, drone imagery, and weather data, to generate robust insights for agriculture-related problems. The repository provides fusion workflows, data preparation tools, model training notebooks, and an inference engine to facilitate the creation of geospatial models tailored for agriculture and farming. Users can interact with the tools via a local cluster, REST API, or a Python client, and the repository includes documentation and notebook examples to guide users in utilizing FarmVibes.AI for tasks like harvest date detection, climate impact estimation, micro climate prediction, and crop identification.

Private Investigators and Security Firms, given the nature of their work, handle highly sensitive information and must maintain strict confidentiality and data privacy standards.

Analyzes cryptocurrency charts and data for investment advice.

I assist in analyzing stocks with a detail-oriented, patient, data-driven approach, drawing from a wide range of expert authors.

Provides crypto investment insights from CoinMarketCap and Skynet.

Empowers investments with data analysis and strategic insights.

I provide investment insights and data, clarifying complex financial concepts.

This GPT assists in creating impactful investor updates for companies that have already received funding. It asks insightful questions and recommends KPIs and data that should be included, even assisting with formatting and structuring with updates. It prompts you to opt out of sharing chat data.

Expert in CFA, quant, software engineering, data science, and economics for investment strategies.

Offers Bitcoin investment strategy insights based on recent or your own chart data.

Investment Co-Pilot: Portfolio backtesting and access to in-depth financial data and historical closing prices of US-listed companies. (Pulse formerly)

Specializing in AI-driven trading indicators, offering innovative, data-driven solutions for traders and investors seeking enhanced market analysis and decision-making tools.

I am packed with every Bitcoin price from 2014-2023. I quickly analyze Bitcoin data to help you make smart investment decisions, plot trends, and find interesting correlations! *Warning: This is not financial advice.

The essence of Quantitative Investing

Advanced, uncensored AI for detailed UFO research and analysis, with diverse capabilities.

Unearths digital footprints

Expert in UFO/UAP analysis, employing scientific methods for realistic interpretations.

专注于“投资策略”类型的研报分析总结，提炼对行业配置的核心观点 Focusing on the analysis and summary of research reports on the type of "investment strategy", refining core perspectives on industry allocation

Professional yet approachable AI for finance and tech assistance.

The Sign of Money: A SherLockian Quest for Decoding the Financial Market’s Mysteries

Data-driven predictions on company funding and potential