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51 Listings in Data Analysis Agents Available
What is Snowflake Cortex AI? Snowflake Cortex AI is a data cloud AI AI agent offering Snowflake Cortex AI with LLM functions, Cortex Analyst text-to-SQL and agents on governed data. Founded in 2023 and based in Bozeman, Montana, USA, Snowflake Cortex AI helps data teams on Snowflake automate data cloud AI work and get results faster. Key capabilities of Snowflake Cortex AI LLM functions in SQL Cortex Analyst Cortex Search Snowflake Intelligence agents Governed data access Natural-language questions How Snowflake Cortex AI works Snowflake Cortex AI takes text and database data as input and produces SQL, text and insights. It is powered by Multiple LLMs (managed) models, with the vendor managing prompts, models and updates. It connects to tools such as Snowflake, BigQuery, Databricks and Salesforce, so the agent works inside existing workflows. Who uses Snowflake Cortex AI? Snowflake Cortex AI is built for data teams on Snowflake. It suits teams that want LLM functions in SQL and Cortex Analyst without adding headcount, while keeping people in control of review and final decisions. Snowflake Cortex AI vs Databricks Genie Snowflake Cortex AI is often compared with Databricks Genie. Snowflake Cortex AI stands out for LLM functions in SQL and Cortex Search. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Akkio? Akkio is a generative BI AI agent offering a generative BI platform that lets agencies and teams analyze data, forecast and build reports with AI. Founded in 2019 and based in Cambridge, Massachusetts, USA, Akkio helps agencies and business teams automate generative BI work and get results faster. Key capabilities of Akkio Chat-based data exploration Predictive models Automated reports White-label dashboards Chart generation Scheduled reports How Akkio works Akkio takes tabular data and text as input and produces charts and predictions. 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 Akkio? Akkio is built for agencies and business teams. It suits teams that want chat-based data exploration and predictive models without adding headcount, while keeping people in control of review and final decisions. Akkio vs Julius AI Akkio is often compared with Julius AI. Akkio stands out for chat-based data exploration and automated reports. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Fullstory StoryAI? StoryAI is the AI-powered analytics layer of Fullstory, a digital experience platform that captures, understands and acts on behavioral data. It turns behavioral data into insights teams can trust, so they spend less time analyzing and more time deciding. Fullstory customers include Pizza Hut, Adobe, JetBlue, Chipotle, Grammarly and Duolingo. Key capabilities of Fullstory StoryAI Session summaries: AI summaries of what happened in sessions Session Replay: records and plays back user sessions Fullcapture: automatic, privacy-first data collection Heatmaps, funnels and journey maps: visual behavior analysis Guides and Surveys: in-product guidance, NPS and feedback Data activation: sends behavioral data to personalize experiences How Fullstory StoryAI works Fullstory automatically captures interactions on web and app experiences. StoryAI analyzes that data to summarize sessions and highlight friction, and teams then use replay, funnels and journey maps to confirm and act on what it finds. Who uses Fullstory StoryAI? Product, UX, e-commerce and customer experience teams in retail, finance, travel, food and beverage, SaaS and gaming use Fullstory. It also has product lines for employee experience and data activation. Fullstory StoryAI pricing Fullstory offers tiered plans with a free trial and demos, but the homepage does not list prices. Request current plan details from the vendor. Fullstory StoryAI alternatives Fullstory StoryAI is compared with Amplitude AI, Domo AI and Seek AI. Amplitude AI sits on product analytics events, and Seek AI converts questions to data queries.
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What is Mixpanel Spark? Mixpanel Spark is the generative AI feature in Mixpanel product analytics that lets users ask questions about product, marketing and revenue data in natural language. Spark builds the report without SQL or a chart builder. Mixpanel's own blog now titles it Spark AI, now Mixpanel Agent. Key capabilities of Mixpanel Spark Question-to-report: builds a report from a plain-language question Follow-up questions: refine and drill into results Visualizations: returns charts and reports Product, marketing and revenue questions: spans event data Request limits by plan: 30 on Free, 60 on Growth, 300 on Enterprise per month How Mixpanel Spark works A user types a question, Spark translates it into a Mixpanel query on the existing event data, and shows a report that can be refined with follow-up questions. At launch the vendor said it used generative AI through a partnership with OpenAI. Who uses Mixpanel Spark? Product managers, marketers and analysts who prefer not to build queries use Spark within Mixpanel. Mixpanel Spark pricing Mixpanel has a Free plan with up to 1M events per month, a Growth plan starting at $0 with volume-based pricing and an Enterprise plan with custom pricing. Spark request limits per month are reported as 30, 60 and 300 by plan. Mixpanel Spark alternatives Mixpanel Spark is compared with Pecan AI, Tellius and Gemini in BigQuery. Pecan AI builds predictive models and Tellius offers AI analytics over business data.
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What is Numerous.ai? Numerous.ai is an add-on for Google Sheets and Excel that brings ChatGPT-style capabilities into spreadsheet cells through a simple =AI function. It needs no API key setup and is built by a San Francisco company. Key capabilities of Numerous.ai =AI function: Query the model from any cell. Bulk classification: Categorize many rows at once. Content generation: Draft ideas, ad copy and social variants in cells. Summarization: Summarize text across rows. Data cleanup: Clean and categorize messy data. How Numerous.ai works Users install the add-on in Google Sheets or Excel and type the =AI function into a cell with a prompt and a reference. Numerous sends the request to the model and returns the answer, and the formula can be dragged down for bulk processing. It avoids duplicate queries and supports team collaboration. Who uses Numerous.ai? Content creators, digital marketers, students, researchers and product teams use it to organize ad copy, clean data and prototype prompts. Numerous.ai pricing The vendor lists Personal and Pro at $10 per month billed annually with 500,000 characters, and an Enterprise tier at $10 per month per user with 1 million characters per person. All plans include a 7-day trial for $1, and annual billing saves 57 percent. Numerous.ai alternatives Rows is a standalone AI spreadsheet, Pecan AI builds predictive models, and Formula Bot offers AI formulas for spreadsheets. Numerous.ai is distinguished by working inside the Sheets and Excel you already use.
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What is TextQL? TextQL is an enterprise data platform that maps your data, builds semantic understanding through an AI ontology called Ana, and automates work across complex data systems. It targets companies with legacy databases from SAP, Oracle and Teradata that carry decades of schema complexity. Named customers include Blackstone, Dropbox, Amazon, Scale AI and the NBA. Key capabilities of TextQL Ana ontology: reasons over complex data structures and improves with use Conversational analytics: natural language questions with transparent underlying SQL Data Apps: shareable applications built from governed ontologies Agents: autonomous systems that watch, decide and write results back to source systems Legacy system support: handles SAP, Oracle Fusion and Teradata schemas Regulated data controls: addresses HIPAA, PHI boundaries and 42 CFR Part 2 How TextQL works TextQL connects to your data warehouse, documents what the data means in the Ana ontology, answers questions with visible SQL, and then automates downstream work through agents. The vendor positions this as one system bought once instead of several integrated tools. Who uses TextQL? Data and analytics teams at enterprises, including healthcare, finance and technology companies, use TextQL, especially where SAP columns number in the hundreds. TextQL pricing TextQL does not publish pricing on its homepage. Contact the vendor for a quote. TextQL alternatives TextQL is compared with ThoughtSpot, Vanna AI and Julius AI. ThoughtSpot is a search-driven analytics platform, Vanna AI is open source text to SQL, and Julius AI serves individual analysis.
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What is Domo AI? Domo AI is the artificial intelligence layer of Domo, a cloud business intelligence and data platform. It lets teams ask questions of their data in natural language and build AI agents and workflows on governed Domo datasets. Key capabilities of Domo AI AI chat: Ask questions in natural language and get answers drawn from Domo datasets AI agents: Build agents that act on data and trigger workflows Data connectors: Pulls data from cloud warehouses, CRMs and ERPs into one place ETL and transformations: Prepare and join data before it reaches dashboards and AI Dashboards: Visual reporting with scheduled refreshes Governance: Access controls apply to the data that AI features use How Domo AI works Data is ingested into Domo through connectors, transformed with its ETL tools and stored in governed datasets. Domo AI then reads those datasets to answer questions, power agents and run workflows. Usage is metered in credits, which are consumed by data storage, updates, dashboard refreshes, API calls and AI features such as ML inference. Who uses Domo AI? Domo AI is used by business and data teams that already centralize reporting in Domo, from analysts building dashboards to operations teams automating data-driven actions. It suits organizations that want BI, data integration and AI in a single platform. Domo AI pricing Domo does not publish a price list and uses consumption-based credits instead of fixed tiers. It offers a 30-day free trial with full platform access and unlimited users, and sales quotes follow. Domo AI alternatives Alternatives include Snowflake Cortex AI, which brings AI functions into the Snowflake data cloud, Formula Bot, which generates spreadsheet formulas and analysis, and Gemini in BigQuery. Domo bundles connectors, dashboards and AI in one hosted platform.
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What is Tableau Agent? Tableau Agent, formerly Einstein Copilot for Tableau, is an AI analytics assistant embedded in Tableau that helps users explore data, build visualizations and write calculations with natural language. It is built on Salesforce's Einstein Trust Layer. Key capabilities of Tableau Agent Natural language exploration: ask questions and get visualizations Calculation help: create and document calculations in plain language Data prep assistance: available in Tableau Prep and Catalog on the Tableau+ edition Dashboard use: reached general availability in dashboards in June 2026 per third-party reports Automation: routine analytics steps Trust Layer: zero data retention with LLM providers How Tableau Agent works Tableau Agent runs inside Tableau Cloud web authoring and Prep. You describe a question or task in natural language and it proposes visualizations, calculations or steps. Prompts pass through the Einstein Trust Layer, which the vendor describes as providing zero data retention with LLM providers. Who uses Tableau Agent? Analysts and business users on Tableau Cloud who want faster exploration without writing every calculation. Tableau Agent pricing Salesforce did not return pricing to this review. Third-party sources say core Tableau AI is included with Tableau+ and Tableau Cloud subscriptions, so confirm with the vendor. Tableau Agent alternatives Databricks Genie and Rows offer natural language data analysis, Mixpanel Spark and Contentsquare CoPilot focus on product and experience analytics. Tableau Agent lives inside Tableau.
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What is Vizly? Vizly is an AI-powered data analysis tool that lets users upload datasets and explore them through natural-language questions. It writes and runs the analysis code behind the scenes and returns charts, tables and written explanations. Key capabilities of Vizly Chat-based analysis: Ask questions about an uploaded dataset in plain English. Chart generation: Produces charts and visualizations from a prompt. File uploads: Works on spreadsheets and data files you provide. Code execution: Runs analysis code in a coding environment on the paid Premium tier. Memory retention: Premium keeps context across a session for better follow-up answers. Student discount: The vendor offers students a 25% discount on request. How Vizly works You upload a spreadsheet or data file and type a question. Vizly translates the request into analysis code, runs it in a sandboxed environment and returns the result as a chart, table or text answer you can refine with follow-up messages. On the free trial, uploaded files are deleted after one hour of inactivity. Who uses Vizly? Vizly is aimed at students, researchers and analysts who want answers from data without writing code or learning a BI tool. It fits one-off analysis of spreadsheets more than governed company-wide reporting. Vizly pricing Vizly has a free Trial limited to 10 messages per month. Basic costs $19.99 per month (200 messages per month and stronger models), and Premium costs $29.99 per month with unlimited messages and priority support. Annual billing lowers these to about $16.67 and $25.00 per month. These figures come from third-party listings, not the vendor site. Vizly alternatives Alternatives include Julius AI, which is a similar chat-with-your-data analyst, ChatGPT with Advanced Data Analysis, which is a general assistant with a code sandbox, and Chat2DB, which targets SQL databases rather than uploaded files.
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What is Pecan AI? Pecan is a predictive AI agent for business teams that generates forecasts without data science expertise. It automates data preparation, model building and validation, and delivers predictions in days. Key capabilities of Pecan AI Churn prediction: Forecast which customers are likely to leave. Lifetime value modeling: Predict customer LTV. Lead scoring: Rank leads by likelihood to convert. Demand forecasting: Predict demand for inventory planning. Automated ML pipeline: No-code data preparation, modeling and validation. How Pecan AI works Teams connect a cloud warehouse or business source through secure connectors, and Pecan prepares the data, builds and validates models and shows results in explainability dashboards. Predictions are pushed into existing business tools. The vendor says most teams build a first model within hours to a day. Who uses Pecan AI? Marketing, sales, operations, customer success and finance teams use Pecan. The vendor reports a 28 percent average churn reduction and 15 percent better marketing ROAS among customers. Pecan AI pricing Pecan states that commitment-free plans are available, but specific pricing requires a demo consultation, so cost is quote-based. Pecan AI alternatives Tellius provides conversational analytics, Databricks Genie lets users query data in natural language, and Sourcetable is an AI spreadsheet. Pecan differs by focusing on automated predictive models for business teams.
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What is Formula Bot? Formula Bot is a spreadsheet analysis AI agent offering an AI data analyst and spreadsheet assistant that writes formulas and analyzes files. Formula Bot helps spreadsheet users and analysts automate spreadsheet analysis work and get results faster. Key capabilities of Formula Bot Formula generation Data analysis from files Charts Data cleaning Chart generation Scheduled reports How Formula Bot works Formula Bot takes text and spreadsheets as input and produces formulas, charts and text. 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 Formula Bot? Formula Bot is built for spreadsheet users and analysts. It suits teams that want formula generation and data analysis from files without adding headcount, while keeping people in control of review and final decisions. Formula Bot vs Julius AI Formula Bot is often compared with Julius AI. Formula Bot stands out for formula generation and charts. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Prophecy? Prophecy is an agentic data preparation and analysis platform where AI agents turn plain English business goals into visual data workflows that analysts can inspect and refine. It handles preparation, analysis, scheduling and monitoring. Customers include HSBC, JP Morgan, Microsoft, SAP, Ralph Lauren, Deutsche Telekom and Toyota. Key capabilities of Prophecy AI workflow generation: agents build pipelines from natural language Visual inspection: review and edit generated steps before deployment Cloud-native deployment: Databricks, Snowflake and BigQuery Open code: generates open code instead of a proprietary format Scheduling and monitoring: end-to-end operation Standalone mode: works without a warehouse How Prophecy works A user describes the goal, and Claude Code-powered agents generate a visual workflow that runs natively in the cloud data platform. Analysts inspect and refine it before deploying, and Prophecy validates and monitors runs. The vendor positions it against Alteryx's drag-and-drop approach. Who uses Prophecy? Data analysts and data engineering teams at enterprises use Prophecy to prepare data without hand-building pipelines. Prophecy pricing Prophecy offers a free Professional tier with no credit card. Enterprise Express includes 90-day onboarding support, and prices were not shown on the page reviewed. Prophecy alternatives Prophecy is compared with Alteryx, Akkio and Rows. Alteryx is the incumbent drag-and-drop analytics tool, and Akkio focuses on no-code predictive modeling.
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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.
