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51 Listings in Data Analysis Agents Available
What is Contentsquare Copilot? Contentsquare Copilot refers to Sense, the contextual AI assistant inside Contentsquare's experience intelligence platform. Sense helps teams analyze behavior, get fast answers and choose next steps. The platform serves about 3,000 enterprise and mid-market brands and 1.3 million websites, including Audi, Specsavers, EasyJet and John Lewis. Key capabilities of Contentsquare Copilot Natural-language analysis: ask a question and Sense sets up the dataset and visualization Next-best-action: explains what to do after the analysis Multi-session summaries: recaps of user behavior highlighting friction on web and mobile Friction detection: surfaces errors and slowdowns in real time Voice of Customer AI: generates and summarizes surveys Mobile app analysis: taps, swipes and gestures without manual tagging How Contentsquare Copilot works Contentsquare captures entire user journeys with no manual tagging. Sense reads that data, answers plain-language questions, summarizes sessions and recommends actions. Analysts and product teams review the findings before changing a site or app. Who uses Contentsquare Copilot? Digital, product, UX and marketing teams at enterprise and mid-market brands use Contentsquare. It has a 4.7 rating on both G2 and Gartner according to the vendor. Contentsquare Copilot pricing The vendor offers a free tier and enterprise plans, with details on its pricing page. Sense is available to Experience Analytics customers, and plan limits should be confirmed with the vendor. Contentsquare Copilot alternatives Sense is compared with Copilot in Power BI, Qlik Answers, Domo AI, Vizly and Seek AI. Those are general data analytics assistants, while Sense works on behavioral experience data.
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What is Ajelix? Ajelix is a spreadsheet and BI AI agent offering an AI spreadsheet and BI toolkit for formulas, scripts, templates and automated dashboards. Based in Riga, Latvia, Ajelix helps Excel and Sheets users and SMB teams automate spreadsheet and BI work and get results faster. Key capabilities of Ajelix Formula generator VBA and script writer AI dashboards Template library Formula generation Automated analysis How Ajelix works Ajelix takes text and data as input and produces formulas and dashboards. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Microsoft Excel, Google Sheets, CSV and PowerPoint, so the agent works inside existing workflows. Who uses Ajelix? Ajelix is built for Excel and Sheets users and SMB teams. It suits teams that want formula generator and VBA and script writer without adding headcount, while keeping people in control of review and final decisions. Ajelix vs GPTExcel Ajelix is often compared with GPTExcel. Ajelix stands out for formula generator and AI dashboards. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Rows? Rows is a spreadsheet platform that combines a familiar grid with data integrations and AI analysis. Users import business data, extract tables from documents and websites, and run AI Tasks on it. The company recently joined Superhuman. Key capabilities of Rows AI Tasks: Monthly AI actions for extracting and analyzing data: 5 on Free, 200 on Plus, 1,000 on Pro. Data integrations: Connect accounts to pull live business data. Document extraction: Pull tables from PDFs and other documents. Website table import: Import tables from web pages into a sheet. Automation: Data table automation, daily on Plus and by the minute on Pro. How Rows works Users pull data in through integrations, file imports or extraction, then ask AI Tasks to analyze or transform it inside the spreadsheet. On paid plans data tables refresh automatically on a schedule, daily on Plus and by the minute on Pro. Version history runs 7 days on Free and 2 years on Pro. Who uses Rows? Business teams that build reports and models from data scattered across tools use Rows. Enterprise buyers get SAML SSO, custom AI models and a dedicated account manager. Rows pricing Rows lists Free at $0, Plus at $8 per user per month, Pro at $79 per month plus $8 per user, and custom Enterprise pricing. Free includes 5 AI Tasks per month, Plus 200, Pro 1,000. Rows alternatives Pecan AI is a predictive analytics tool, Numerous.ai adds AI functions to Google Sheets and Excel, and Tellius is an analytics platform. Rows is a standalone spreadsheet with integrations built in.
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What is Polymer? Polymer is a dashboards AI agent offering a tool that turns spreadsheets and data sources into AI-generated, shareable dashboards. Polymer helps marketers, agencies and SaaS teams automate dashboards work and get results faster. Key capabilities of Polymer AI dashboard generation Embedded analytics Data connectors Conversational insights Chart generation Scheduled reports How Polymer works Polymer takes spreadsheets and database data as input and produces dashboards and insights. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Snowflake, BigQuery, PostgreSQL and Google Sheets, so the agent works inside existing workflows. Who uses Polymer? Polymer is built for marketers, agencies and SaaS teams. It suits teams that want AI dashboard generation and embedded analytics without adding headcount, while keeping people in control of review and final decisions. Polymer vs Tableau Polymer is often compared with Tableau. Polymer stands out for AI dashboard generation and data connectors. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Climatiq? Climatiq is a carbon intelligence platform that offers an AI-powered calculation engine and an emission factor database. It helps organizations measure and reduce carbon footprints across business processes. Data Studio supports emission factor search and filtering, and PCF Studio calculates product carbon footprints. Key capabilities of Climatiq Emission factor database: searchable core dataset in Data Studio PCF Studio: product carbon footprint calculations with ISO 14067 documentation Calculation engine: AI-assisted factor matching and calculations API access: available on Enterprise Spreadsheet integrations: Excel and Google Sheets Premium datasets: ecoinvent, Carbon Minds and IEA as add-ons How Climatiq works Users search emission factors in Data Studio or calculate product carbon footprints in PCF Studio. Enterprise customers call the API to automate calculations with custom volumes. Results come with auditable documentation, and premium datasets can be added. Who uses Climatiq? Sustainability teams, consultants and developers building carbon accounting features use Climatiq. Consultants using data commercially must pick the consultant option, and accredited non-profits and academic institutions can get free plans. Climatiq pricing Starter is free for non-commercial use. Data Pro is EUR 3,000 per year, PCF Pro starts at EUR 4,900 per year with 25 auditable PCFs, and Enterprise is custom. No credit card is needed for Starter. Climatiq alternatives Climatiq is compared with Pecan AI, Tellius and Numerous.ai, which are general analytics tools, whereas Climatiq specializes in emission data.
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What is Vanna AI? Vanna AI is a text-to-SQL AI agent offering an open-source Python framework and hosted agent that generates accurate SQL using RAG on your schema. Founded in 2023 and based in United States, Vanna AI helps developers building data apps automate text-to-SQL work and get results faster. Key capabilities of Vanna AI RAG-trained SQL generation Open-source framework Any LLM or database Web and Slack apps Semantic layer support Chart generation How Vanna AI works Vanna AI takes text and database schema as input and produces SQL, tables and charts. It is powered by Any LLM (selectable) models, with the vendor managing prompts, models and updates. It connects to tools such as Snowflake, BigQuery, Databricks and PostgreSQL, so the agent works inside existing workflows. Who uses Vanna AI? Vanna AI is built for developers building data apps. It suits teams that want RAG-trained SQL generation and open-source framework without adding headcount, while keeping people in control of review and final decisions. Vanna AI vs Defog Vanna AI is often compared with Defog. Vanna AI stands out for RAG-trained SQL generation and any LLM or database. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is DataGPT? DataGPT is a data analysis AI agent offering an AI analyst that answers business questions and explains metric changes from your data. Founded in 2022 and based in San Francisco, California, USA, DataGPT helps business and growth teams automate AI data analysis work and get results faster. Key capabilities of DataGPT Conversational analysis Metric change explanations Anomaly detection Automated insights Semantic layer support Chart generation How DataGPT works DataGPT takes text and database data as input and produces text and charts. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Snowflake, BigQuery, Databricks and PostgreSQL, so the agent works inside existing workflows. Who uses DataGPT? DataGPT is built for business and growth teams. It suits teams that want conversational analysis and metric change explanations without adding headcount, while keeping people in control of review and final decisions. DataGPT vs Tableau Pulse DataGPT is often compared with Tableau Pulse. DataGPT stands out for conversational analysis and anomaly detection. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Chat2DB? Chat2DB is a SQL client AI agent offering an AI database client that writes SQL from natural language and builds reports across databases. Founded in 2023 and based in Hangzhou, China, Chat2DB helps developers and data analysts automate AI SQL client work and get results faster. Key capabilities of Chat2DB Text-to-SQL Multi-database client AI reports and dashboards SQL explanation Semantic layer support Chart generation How Chat2DB works Chat2DB takes text and database data as input and produces SQL and charts. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Snowflake, BigQuery, Databricks and PostgreSQL, so the agent works inside existing workflows. Who uses Chat2DB? Chat2DB is built for developers and data analysts. It suits teams that want text-to-SQL and multi-database client without adding headcount, while keeping people in control of review and final decisions. Chat2DB vs DBeaver Chat2DB is often compared with DBeaver. Chat2DB stands out for text-to-SQL and AI reports and dashboards. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Vannevar Labs? Vannevar Labs is a national security AI AI agent offering AI software that helps national security teams collect, translate and analyze foreign data. Founded in 2019 and based in Palo Alto, California, USA, Vannevar Labs helps defense and intelligence agencies automate national security AI work and get results faster. Key capabilities of Vannevar Labs Foreign language analysis Open-source data collection Mission workflows Analyst tools Real-time alerts Multilingual analysis How Vannevar Labs works Vannevar Labs takes text and documents as input and produces insights. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Palantir, Microsoft Azure, AWS GovCloud and Splunk, so the agent works inside existing workflows. Who uses Vannevar Labs? Vannevar Labs is built for defense and intelligence agencies. It suits teams that want foreign language analysis and open-source data collection without adding headcount, while keeping people in control of review and final decisions. Vannevar Labs vs Babel Street Vannevar Labs is often compared with Babel Street. Vannevar Labs stands out for foreign language analysis and mission workflows. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Blackbird.AI? Blackbird.AI is a narrative intelligence company that helps organizations identify and defend against disinformation and coordinated manipulation before it scales. Key capabilities of Blackbird.AI Constellation Platform: Analyzes narratives, influencers, networks, bot campaigns and audiences Constellation Dashboard: Visual monitoring of narrative risk Narrative Feed: Stream of emerging narratives Constellation API: Programmatic access Compass Context: Harmful narrative detection Compass Vision: Deepfake and AI-generated image detection RAV3N tools: Research and reconnaissance How Blackbird.AI works Blackbird.AI monitors social media, news, the dark web and other channels across 25+ languages, analyzing text, images and memes. Constellation clusters narratives and identifies the actors and bot networks pushing them, while Compass checks specific claims and images. Who uses Blackbird.AI? Corporate intelligence, healthcare, finance, critical infrastructure, media, consumer products and public sector teams use it. It was named a top Market Shaper in narrative intelligence by Gartner, per the vendor. Blackbird.AI pricing Blackbird.AI does not publish pricing. Blackbird.AI alternatives Alternatives include Graphika for network analysis, Babel Street for risk intelligence and Vannevar Labs for national security. Blackbird.AI focuses on narrative attacks.
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What is GPTExcel? GPTExcel is a spreadsheet formula AI agent offering an AI tool that generates and explains Excel and Google Sheets formulas, scripts and SQL from plain language. GPTExcel helps spreadsheet users automate spreadsheet formula work and get results faster. Key capabilities of GPTExcel Formula generation Formula explanation VBA and Apps Script SQL queries Automated analysis How GPTExcel works GPTExcel takes text as input and produces formulas and code. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Microsoft Excel, Google Sheets, CSV and PowerPoint, so the agent works inside existing workflows. Who uses GPTExcel? GPTExcel is built for spreadsheet users. It suits teams that want formula generation and formula explanation without adding headcount, while keeping people in control of review and final decisions. GPTExcel vs Formula Bot GPTExcel is often compared with Formula Bot. GPTExcel stands out for formula generation and VBA and Apps Script. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Hotjar AI? Hotjar is a website behavior analytics tool, now part of Contentsquare, that combines heatmaps, session replay and surveys to show how visitors use a site. Its Sense AI layer summarizes heatmaps and recordings and answers questions about the data in plain language. Key capabilities of Hotjar AI Heatmaps: including Attention Maps that show real engagement beyond clicks Session Replay: watch real user sessions Surveys and feedback: collect user feedback on pages Sense AI: AI summaries of heatmaps and recordings plus a chat interface with recommended actions Error and performance monitoring: catch JavaScript errors and see how load times affect behavior Funnel analysis: visualize drop-off points How Hotjar AI works You add the tracking script to a site, and Hotjar records sessions and aggregates clicks, scrolls and attention into heatmaps. Sense AI then summarizes what it found and lets you query the data in natural language. An LLM Connect (MCP) feature lets ChatGPT, Claude and Copilot query your data directly. Who uses Hotjar AI? Product, UX, marketing and conversion teams that want to see where visitors struggle without engineering help. Hotjar AI pricing The free plan includes 200,000 sessions per month, heatmaps, session replay, error monitoring and up to 100 survey responses per month, with no credit card required. Paid plan prices were not shown on the page reviewed. Hotjar AI alternatives Tableau Agent, Copilot in Power BI, Qlik Answers and Tellius are business intelligence assistants for analyzing data you already have, while Hotjar captures on-site behavior itself.
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AI data analysis agents let teams query, analyze, and visualize data using natural language, surfacing insights, trends, and answers without writing SQL or building dashboards manually. This guide explains what they are, how they work, what matters, and how to choose one.
AI data analysis agents let teams query, analyze, and visualize data using natural language, surfacing insights, trends, and answers without writing SQL or building dashboards manually. This guide explains what they are, how they work, what matters, and how to choose one.
AI data analysis agents connect to your data and let users ask questions in plain language, 'why did revenue drop last quarter?', then generate queries, run the analysis, and return charts, summaries, and explanations.
They go beyond traditional BI by automating the analytical work itself: writing queries, identifying trends and anomalies, suggesting follow-up questions, and producing narrative explanations of what the data shows.
The category spans conversational analytics layered on existing warehouses and BI tools, automated insight and anomaly detection, and agentic analysts that plan and execute multi-step analyses. Buyers weigh accuracy, data governance, and trust in AI-generated conclusions.
A user asks a question in natural language; the agent interprets intent against a semantic model of your data, generates and runs queries, and returns results as charts, tables, and plain-language explanations, often with suggested follow-ups.
Platforms combine an LLM with a semantic layer (definitions of metrics and relationships), query generation, visualization, and guardrails that constrain what data is accessed and how results are framed.
Data teams connect sources, define the semantic model and metrics, set permissions, and review accuracy. Business users then self-serve answers, while analysts focus on deeper, governed work.
Ask questions in plain language and get answers, charts, and explanations without writing SQL.
Surface trends, outliers, and drivers automatically, alerting teams to what changed and why.
A defined model of metrics and relationships keeps answers consistent and aligned to trusted definitions.
Generate charts and plain-language summaries so insights are easy to understand and share.
Plan and execute multi-step investigations, joining data, testing hypotheses, and explaining findings.
Row- and column-level access controls ensure users only see data they're allowed to.
Business users get answers instantly without waiting on analysts or learning SQL.
Automated analysis and anomaly detection surface what matters in seconds, not days.
Deflecting routine queries lets analysts focus on complex, high-value work.
A semantic layer ensures everyone uses the same trusted definitions and numbers.
Anomaly detection flags issues and opportunities before they show up in a report.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| Conversational BI layers | NL querying over existing warehouse/BI | SMB to enterprise | Self-service on trusted data | Needs a good semantic model |
| Automated insight engines | Anomaly and trend detection | Any | Proactive, hands-off insight | Tuning to reduce noise |
| Agentic data analysts | Multi-step investigation and reporting | Mid-market to enterprise | Handles complex analysis | Requires validation and guardrails |
| Embedded analytics AI | NL analytics inside products | SaaS and enterprise | Insights in-context | Developer integration effort |
Retail & E-commerce: Analyze sales, inventory, and customer behavior in plain language.
Financial Services: Surface trends and anomalies with governed, auditable access.
Technology: Give product and growth teams self-service answers from data.
Healthcare: Analyze operational and outcome data with strict access controls.
Manufacturing: Monitor production and supply metrics and flag anomalies early.
Professional Services: Track utilization, margins, and project metrics conversationally.
Test on your data and questions. Verify the agent returns correct, explainable answers, accuracy is the whole point.
Confirm support for a semantic layer so metrics are consistent and answers map to trusted definitions.
Check native connectors to your warehouse, databases, and BI tools.
Verify row/column-level access controls, SSO, and compliance for sensitive data.
Look for shown queries and reasoning so analysts can validate AI conclusions.
Understand seat vs. query pricing and how it scales with users and data volume.
Data agents are moving from answering single questions to running multi-step investigations and producing full analyses.
Tighter semantic layers and explainability are making AI-generated insights trustworthy and auditable.
Proactive, agentic monitoring will surface issues and recommended actions before anyone asks.
Buyers should prioritize accuracy, semantic governance, strong access controls, explainability, and transparent data handling.
An AI data analysis agent connects to your data and lets users ask questions in plain language, then generates and runs the queries, returns charts and summaries, and explains the findings. It automates the analytical work, querying, trend and anomaly detection, and narrative explanation, so business users can self-serve answers and analysts focus on deeper work.
Traditional BI requires building dashboards and writing queries; users consume pre-built reports. AI data agents let anyone ask new questions in natural language and get answers, charts, and explanations on demand, and they proactively surface trends and anomalies. The best approach pairs an AI agent with a governed semantic layer so answers stay accurate and consistent.
Treat them as fast, reviewable analysis rather than gospel. AI can produce confident but wrong answers, so choose tools that show the underlying queries and reasoning, ground answers in a semantic model of trusted metrics, and let analysts validate. Accuracy and explainability should be top selection criteria.
Most connect to data warehouses, databases, and BI tools, and work best when your data is reasonably organized and a semantic model defines key metrics. Some can analyze uploaded files or smaller datasets directly, but enterprise use typically assumes a warehouse and governed definitions.
Reputable platforms offer SSO, row- and column-level access controls, encryption, and compliance certifications, ensuring users only see data they're permitted to. Confirm whether your data is used to train shared models and where it's processed, especially for regulated or sensitive data.
A semantic layer defines your metrics, dimensions, and relationships, what 'revenue' or 'active user' actually means, so AI-generated answers map to trusted, consistent definitions. Without it, the agent may interpret questions inconsistently or produce numbers that don't match official reports.
Common models are per-seat subscriptions or usage-based (per query/compute), often with tiers for connectors, governance, and advanced agentic features. Estimate your user count and query volume, and factor in semantic-model setup effort, to compare true cost.
Prioritize answer accuracy and explainability on your data, semantic-layer support and governance, native connectors, security and access controls, and pricing that scales with users and queries. Run a proof of concept on real questions and validate results against known numbers before rolling out.
