Best Free Unstructured Data Analysis Tools of 2025

Bright Data is a leader in data collection, enabling businesses to gather crucial structured and unstructured information from millions of websites using our proprietary technology. Our proxy networks allow you to access sophisticated target sites by precise geo-targeting. Our tools can be used to block difficult target sites, perform SERP-specific data collection tasks and manage and optimize proxy performance.

Analyze data, share insights, and build your research library. Find opportunities and be a hero within your team. You can find patterns using a variety qualitative research methods, unstructured data, or video files. Dovetail is an analysis program you will love to use. Dovetail can be used to identify patterns in interviews, usability testing, surveys responses, and many other areas. Drag & drop intuitive controls make it easy to organize tags into a hierarchy. You can also add global tags to your project. You can transform qualitative data into quantitative data by adding highlights and visualize your work using a variety of charts. To add tags, simply highlight text and select the text. Transcribe video recordings to find patterns in interviews, usability tests, survey answers, and many other things. Turn qualitative data into quantitative data. You can filter, segment, and chart themes across transcripts, interview notes, survey responses, among other things.

The most innovative cloud database service available, with unmatched data mobility across AWS, Azure and Google Cloud, as well as built-in automation for resource optimization and workload optimization. MongoDB Atlas is a global cloud database service that supports modern applications. Fully managed MongoDB can be deployed across AWS, Google Cloud and Azure. This is possible with best-in class automation and proven practices that ensure availability, scalability and compliance with the highest data security and privacy standards. This is the best way to deploy, scale, and run MongoDB in cloud. MongoDB Atlas provides security controls for all data. Allow enterprise-grade features to be integrated with your existing security protocols or compliance standards. MongoDB Atlas protects your data with preconfigured security features that allow for authentication, authorization and encryption.

KlearStack automates invoice processing without the need for templates and eliminates the tedious task of manually entering unstructured documents. Our mission is to automate tedious manual processes and tedious data entry so that humans can be freed up for more creative and intelligent tasks. Organizations can use unstructured data to gain competitive advantage. This is done by unlocking the useful information in semi-structured and unstructured documents. KlearStack's AI provides the best solutions to automate these processes that involve unstructured data. Invoice Automation Automate your Purchase Order Receipt Capture Consumer Durable Loans Multi-Vendor Trade Finance Process Automation Two-wheeler Loan Automation Autonomous Loan Process for Used Cars Our proprietary template-less AI/ML technology means that you no longer need to spend hundreds of hours designing and maintaining templates. Increase productivity by up to 200

Instead of creating custom scrapers to extract unstructured information, our generative AI can get you the data you need in seconds. Define data, sources, schedule. Kadoa automatically generates scrapers for the sources, and adapts to site changes. Kadoa extracts data and ensures data accuracy. Our API allows you to receive the data in any format. Our AI-generated scrapers make it easy to extract data from any website. No programming is required. It's quick and easy to get your data set up. You can focus on other tasks and not worry about changing data structures. Avoid CAPTCHAs and other stumbling blocks. Recurring data extraction allows you to set it and forget about it. Access and use the data easily in your own tools and projects. To make better pricing decisions, track market prices automatically. You can aggregate and analyze job postings from thousands of job boards. Instead of copying and pasting information, let your sales team concentrate on discovery and closing.

Metal is a fully-managed, production-ready ML retrieval platform. Metal embeddings can help you find meaning in unstructured data. Metal is a managed services that allows you build AI products without having to worry about managing infrastructure. Integrations with OpenAI and CLIP. Easy processing & chunking of your documents. Profit from our system in production. MetalRetriever is easily pluggable. Simple /search endpoint to run ANN queries. Get started for free. Metal API Keys are required to use our API and SDKs. Authenticate by populating headers with your API Key. Learn how to integrate Metal into your application using our Typescript SDK. You can use this library in JavaScript as well, even though we love TypeScript. Fine-tune spp programmatically. Indexed vector data of your embeddings. Resources that are specific to your ML use case.

DataChain connects your unstructured cloud files with AI models, APIs and foundational models to enable instant data insights. Its Pythonic stack accelerates the development by tenfold when switching to Python-based data wrangling, without SQL data islands. DataChain provides dataset versioning to ensure full reproducibility and traceability for each dataset. This helps streamline team collaboration while ensuring data integrity. It allows you analyze your data wherever it is stored, storing raw data (S3, GCP or Azure) and metadata in inefficient datawarehouses. DataChain provides tools and integrations which are cloud-agnostic in terms of both storage and computing. DataChain allows you to query your multi-modal unstructured data. You can also apply intelligent AI filters for training data and snapshot your unstructured dataset, the code used for data selection and any stored or computed meta data.

Playmaker is an automation platform for document management that transforms unstructured information from various sources such as PDFs and images, spreadsheets and web data into structured, actionable formats. It offers more than 100 document workflow templates, including financial statements and invoices. Users can streamline processes such as data extraction, validation and integration with other apps. Users can import documents using email, APIs, or manual uploads. The platform then converts the unstructured data to clear, tabular formats that are suitable for powering workflows in more than 300 different applications. Playmaker focuses on security and compliance. Data is stored and processed in the European Union and United States. It adheres to regulations such as GDPR and CCPA.

UnDatas.IO focuses on parsing unstructured data. It uses advanced technology to recognize document layouts, categorize images, tables, formulas and text, and greatly simplify the data processing process. The platform saves users a lot time by organizing data, but also helps them extract valuable insights and make better strategic decisions. UnDatas.IO is a powerful data support tool for academic research, technology development, and business analysis. Recognize layouts of documents by identifying tables, images and text. Then convert them to markdown or json format. APIs allow different platforms and apps to work together seamlessly, facilitating the sharing of data and the integration business processes. Our platform allows you to easily launch data-driven projects. Boost your productivity and get better results. Advanced analytics can help you make better decisions.

Qubole is an open, secure, and simple Data Lake Platform that enables machine learning, streaming, or ad-hoc analysis. Our platform offers end-to-end services to reduce the time and effort needed to run Data pipelines and Streaming Analytics workloads on any cloud. Qubole is the only platform that offers more flexibility and openness for data workloads, while also lowering cloud data lake costs up to 50%. Qubole provides faster access to trusted, secure and reliable datasets of structured and unstructured data. This is useful for Machine Learning and Analytics. Users can efficiently perform ETL, analytics, or AI/ML workloads in an end-to-end fashion using best-of-breed engines, multiple formats and libraries, as well as languages that are adapted to data volume and variety, SLAs, and organizational policies.