Data & Analytics

Julius AI

Your AI data analyst

Julius AI analyzes data, creates visualizations, and builds predictive models from spreadsheets and databases using natural language. No coding experience required.

Trends Monitor

Monitor Google Trends and discover rising search queries.

Trends Monitor helps you continuously monitor Google Trends to discover new keywords, rising queries, and search demand. Explore trend data, save root keywords, enable daily monitoring, and review historical snapshots for SEO, content creation, and product research.

TryAPI

Build & Scale Your APIs

TryAPI offers 81+ production-ready APIs with pay-as-you-go pricing, 99.9% uptime, and sub-200ms response times. Includes currency exchange, IP geolocation, weather, UUID generation, and X/Twitter data APIs. Pay only for what you use, scale without limits, enterprise-grade security with API key encryption and TLS 1.3.

VerdictKit

Explore typed AI decisions in an independent Jev playground

VerdictKit is an independent playground for developers exploring typed AI decisions with Jev. It introduces Choice for named alternatives, Score for ordered ratings and Noul for yes/no questions. Developers can inspect structured inputs and outputs before connecting a decision to an application workflow. Clearly marked demo previews illustrate the interface without representing live predictions. Live requests require the appropriate provider credentials. VerdictKit is not affiliated with TypeSafe AI.

SportBot AI

Sports probabilities, fair odds and bankroll tracking

SportBot AI is a sports analytics platform for adults researching match probabilities and bookmaker prices. It compares model estimates with fair no-vig odds and includes sports chat, bankroll tracking, public methodology and settled model results. Available on web, iOS and Android. One free match analysis daily; Pro $18.99/month and Premium $39.99/month. Educational analytics with no guaranteed outcomes or wagers accepted. Submitted by SportBot AI Team. Logo: https://www.sportbotai.com/logo-icon.png

Yila AI

Yila AI research agents turn scholarly evidence and data into editable academic deliverables.

Yila AI is an academic research workspace for people whose work begins with scholarly material and needs to end in an inspectable deliverable. The product's research agent workflows cover finding papers, reading them closely, comparing evidence, analyzing data, and preparing research documents or presentations. A useful way to evaluate the platform is to look at what the researcher brings to a task and what form the resulting work should take. A question, a paper, a dataset, and a draft create different demands. Someone entering a new topic may need a reading path, while a group examining results may need a comparison of studies. A project with measurements may need analysis before it can support a written argument. Yila's task range gives the researcher several starting points in one academic research workspace, with the desired outcome guiding the choice. Assess the starting material For a literature task, the input can be a question or a research topic. The intended output may be a list of relevant sources, reading notes, or an editable literature review. Define what the review should explain before deciding how broad the search needs to be. A research agent can support discovery, but relevance still depends on the question, context, and kind of evidence that the author needs. When the starting material is an individual paper, reading has a different purpose. The researcher may need to understand a method, identify a result, or prepare for a discussion. PDF analysis can support organizing that material, while the author checks how a statement connects to the source. The official product also offers paper-based poster and presentation workflows, so the paper can inform several different outputs after the reading stage. Look at how papers become usable evidence Yila provides literature search, paper triage, and reading queues. These functions help define a route from a topic toward the sources that deserve more attention. A candidate list is useful when it helps the researcher decide what to examine next, rather than simply accumulating titles. The research agent workflow can organize discovery while leaving room to revise the scope as the question becomes clearer. Bilingual reading and structured evidence cards address the closer examination of a paper. A useful reading record distinguishes the study's question, methods, findings, and limitations. This makes it easier to revisit the original evidence during writing or discussion. In an academic research workspace, that reading record can contribute to a comparison or literature review without asking the author to treat a short summary as the entire paper. Compare before writing a claim Cross-paper evidence matrices are another published Yila workflow. They support looking at studies together, including the differences that affect interpretation. Two papers may share a subject while using different designs or measurements. A comparison should make those differences visible so that the author can decide whether findings support the same conclusion or answer different questions. Yila's literature review workflow is presented as synthesis from full papers with supporting citations and editable structure. A research agent can help organize themes, methods, and the strength of evidence. The author then checks the draft's claims and references against the intended argument. An editable literature review is useful because its organization and emphasis can be refined as the researcher's understanding develops. Connect a data question to its output The official data workflow accepts CSV and XLSX uploads. Yila describes profiling the data, choosing an analysis path around the question, and returning results, charts, reusable files, and code. The initial inspection helps establish what the material contains and which questions it can address. The research agent's contribution should be evaluated alongside the assumptions and interpretation of the chosen method. The data task also needs an output definition. A chart for a discussion, a results section for a manuscript, and a report for collaborators may need different levels of explanation. The researcher should inspect whether the analysis supports the claim being made and whether the visual presents that relationship clearly. An academic research workspace can keep data work and the documents it informs connected without removing the need for methodological judgment. Review the deliverable as a file someone will use Yila offers scientific figures, academic posters, and editable PowerPoint slides. These are communication tasks as well as generation tasks. A figure should make a mechanism or workflow understandable. A poster should give the reader a route through the question and evidence. A presentation needs a narrative that an audience can follow within the available time. A research agent can prepare material around those goals, and the author continues refining it for the audience. The editable format matters because the first output may need changes. A presenter can adjust the wording or emphasis of a deck, and an author can develop the structure of a literature review. PDF analysis and reading notes can provide supporting material for those revisions. Check that the output is useful for the next person or next task, including what needs to remain clear when the source material is no longer visible alongside it. Use requirements as part of the brief Yila presents manuscript section writing, journal-oriented polishing, and formatting against supplied rules. A journal requirement, thesis guide, or template can define the expected document. These inputs help make the task specific: the same underlying research may need a different arrangement depending on where it will be submitted. A research agent workflow can address preparation while the author retains control of the substance. Review terminology, numbers, references, and claim strength after any writing or formatting step. A well-formatted document still depends on the evidence it contains. In an academic research workspace, the question, supporting material, and deliverable should remain connected throughout the workflow. The product's editable outputs are a starting point for that review and revision process. Identify whether the task is a good fit Yila AI is relevant to graduate students, research groups, academic writers, and professionals dealing with scholarly papers or research data. The platform is most useful to evaluate against a concrete research task: organizing sources, comparing evidence, analyzing a spreadsheet, or preparing a scientific output. A literature review and a presentation can be related parts of the same project while requiring different workflows and review criteria. Current access information should be checked on the official site. The public pricing page lists paid subscriptions, but this entry does not assign a fixed allowance, model entitlement, or free-tier condition. Select the research agent workflow that fits the material available, inspect how the output supports the question, and continue refining the file for its audience. The direct official starting point for Yila AI is https://yila.ai.

Reverse Image Location

AI photo geolocation from visible scene clues

Reverse Image Location helps users estimate where a photo was taken by analyzing visible scene evidence such as signs, road markings, vegetation, terrain, architecture, vehicles, shadows, and map context. It is useful for OSINT verification, GeoGuessr-style practice, travel research, and geography learning, with results meant to be cross-checked against maps and satellite imagery.

Fiscus AI

AI-assisted financial operations for Turkish SMEs

Fiscus AI is a Turkish AI-assisted financial operations platform for SMEs. It brings e-invoice and e-archive preparation and status tracking, pre-accounting, receivables, cash-flow visibility, bank transaction review, and human-approved AI suggestions into one auditable workspace.

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PawCoach

Personalized dog-training plans from one short video

PawCoach turns one short dog-behavior video into a personalized five-day training plan. The analysis reviews visible context, body language, timing, and recovery so dog owners can start with a practical, reward-based next step. It is for everyday behavior and training support, not veterinary diagnosis or emergency care.

Hex

Collaborative data workspace with AI

Hex is a modern data workspace where teams write SQL and Python, build visualizations, and share interactive data apps — with an AI assistant that helps write queries and debug code.

MindsDB

AI tables for your database

MindsDB brings machine learning directly into your database. Build, train, and deploy AI models using SQL queries and connect predictions to your existing data workflows.

MonkeyLearn

Text analytics with no-code AI

MonkeyLearn provides no-code text analytics to classify, extract, and visualize insights from customer feedback, surveys, and support tickets using custom AI models.

Obviously AI

No-code AI predictions in minutes

Obviously AI lets anyone build and deploy machine learning models without code. Predict outcomes like churn, sales, and demand from your data with just a few clicks.

Tableau AI

AI-powered visual analytics

Tableau combines powerful data visualization with AI-driven insights. Ask questions in natural language and get instant charts, dashboards, and data stories from your business data.