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51 Listings in Data Analysis Agents Available
What is Gemini in BigQuery? Gemini in BigQuery is a set of Gemini AI features inside Google Cloud's BigQuery data warehouse. It helps users explore and understand data with data insights, and use BigQuery data canvas to discover, transform, query and visualize data in natural language. Core features include SQL and Python code assist and data preparation. Key capabilities of Gemini in BigQuery SQL code assist: generate and explain SQL Python code assist: help with Python in notebooks Data canvas: natural-language discovery, query and visualization with collaboration Data insights: automated queries generated from table metadata to find patterns Data preparation: AI assistance cleaning and transforming data Governed data access: works within BigQuery permissions How Gemini in BigQuery works Within BigQuery you describe what you want in natural language and Gemini generates SQL or Python, finds and joins table assets in data canvas, and visualizes results. Data insights proposes queries from table metadata. Analysts review and run the generated code against their governed data. Who uses Gemini in BigQuery? Data analysts, engineers and data scientists using BigQuery use Gemini to speed up query writing and exploration. Gemini in BigQuery pricing Google states that core Gemini in BigQuery features, including SQL and Python code assist, data canvas and data preparation, are available at no additional cost across editions. Standard BigQuery storage and compute costs still apply, and some AI features bill by AI processing or a Gemini Code Assist subscription. Gemini in BigQuery alternatives Gemini in BigQuery is compared with Copilot in Power BI, Qlik Answers, Domo AI, Vizly and Seek AI. Snowflake Cortex and Databricks Assistant are warehouse-native equivalents on other platforms.
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What is Neoway? Neoway is a Brazilian data analytics and AI company for business, founded in Florianopolis, that launched its SaaS platform in 2012. It became part of B3, the Brazilian stock exchange, in 2021. Key capabilities of Neoway Prevention of losses: Automated due diligence including KYC, KYE, KYP and KYS Fraud and AML: Fraud detection and anti-money laundering Capital markets: Market share monitoring and investment propensity analysis Marketing and sales: Business opportunity recommendations and sales forecasting Demand generation: Prospecting support CX1 and API: Platform or API delivery with customized dashboards How Neoway works Neoway delivers data intelligence through its CX1 platform or by API, with customized dashboards. The homepage does not describe the AI techniques or the data volume used. Who uses Neoway? Companies in 20+ industry sectors. Customers include Inter, BTG Pactual, Hapvida, General Mills, Creditas, Nestle, Santander, Mercedes-Benz and XP Investimentos. Neoway pricing Neoway does not disclose pricing on its homepage. It lists ISO 27701, 37001, 27001, 27018 and 27017 certifications. Neoway alternatives Alternatives include Serasa Experian for credit and data, Boa Vista for credit analytics, and Quod for credit bureau services.
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What is Sourcetable? Sourcetable is an AI spreadsheet for data analysis that connects to databases and business apps and lets you query and chart the data in plain language. It also supports formulas, Python and SQL for users who want more control. Key capabilities of Sourcetable Natural-language analysis: Ask questions of your data in plain English. Data connectors: 40+ connectors including PostgreSQL, BigQuery, Stripe and Google Ads. Automatic charts: Generate charts and visualizations from queries. Python and SQL: Python toolkit and SQL editor on Pro and above. Large files: Handle files up to 10GB. How Sourcetable works You connect a data source or upload a file, then ask the AI assistant to clean, join, analyze or chart the data. The assistant writes formulas, SQL or Python behind the scenes, and usage is metered in monthly credits, with extra credits purchasable. Enterprise plans add SOC 2 Type 2, HIPAA and PCI compliance. Who uses Sourcetable? Analysts, operators and founders who want answers from business data without heavy coding use it. Data scientists use the Python and SQL features on the Pro and Max plans. Sourcetable pricing Regular is free with 50 monthly credits. Pro is $29 per user per month with 2,000 credits, and Max is $100 per user per month with 6,000 credits and unlimited AI messages subject to guardrails. Extra credits cost $10 to $200 and do not expire. Sourcetable alternatives Rows is a spreadsheet with built-in AI and integrations, Julius AI analyzes data through chat, and ThoughtSpot offers search-based enterprise analytics. Sourcetable pairs a spreadsheet interface with many live connectors.
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What is Ascend.io? Ascend.io is an agentic data engineering AI agent offering an agentic data engineering platform where AI agents help build and operate data pipelines. Founded in 2015 and based in Palo Alto, California, USA, Ascend.io helps data engineering teams automate agentic data engineering work and get results faster. Key capabilities of Ascend.io AI pipeline agents Automated orchestration Data change detection Observability Automated monitoring Guided remediation How Ascend.io works Ascend.io takes data and code as input and produces pipelines and actions. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Snowflake, Databricks, BigQuery and dbt, so the agent works inside existing workflows. Who uses Ascend.io? Ascend.io is built for data engineering teams. It suits teams that want AI pipeline agents and automated orchestration without adding headcount, while keeping people in control of review and final decisions. Ascend.io vs Prophecy Ascend.io is often compared with Prophecy. Ascend.io stands out for AI pipeline agents and data change detection. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is ThoughtSpot? ThoughtSpot is an AI analytics platform where users search and ask questions of their data using natural language, including through its Spotter agent. It builds interactive dashboards on live connections to cloud data warehouses. Key capabilities of ThoughtSpot Natural-language search: Ask questions of data in plain language. Spotter AI agents: AI agents for analytics, with 25 queries per user per month on Pro. Live query: Live connections to Snowflake, Databricks and Redshift. Interactive dashboards: Dynamic Liveboards and actionable insights. Embedded analytics: Developer plan with API and SDK for embedding. How ThoughtSpot works ThoughtSpot connects live to cloud data sources, so users query current data rather than extracts. They type a question in natural language, and the system returns charts that can be saved to dashboards. ThoughtSpot says it does not meter LLM tokens, though external LLM provider fees apply if you bring your own. Who uses ThoughtSpot? Business users who need answers without writing SQL use ThoughtSpot, and product teams embed it in customer-facing applications through the Developer and Embedded plans. ThoughtSpot pricing Essentials is $25 per user per month billed annually for 5 to 50 users. Pro is $0.10 per credit. Enterprise is custom. Developer is free for one year, then flexible, and Embedded Enterprise is flexible. ThoughtSpot alternatives Veezoo is a conversational analytics tool, Seek AI answers data questions in natural language, and TextQL targets natural-language analytics for enterprises. ThoughtSpot is distinguished by its search-based interface and embedding options.
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What is Powerdrill? Powerdrill (marketed as Powerdrill Bloom) is an AI data analysis workspace for analyzing documents and databases in natural language. Its pitch is traceability: every number comes back with the page, the row and the figure behind it. Key capabilities of Powerdrill DataFact analyst: processes plain-language questions about your data Scheduled Agent: recurring automated analysis Source tracing: figures linked to page, row and source Memory: agents learn from previous analyses Data explorers: economic, financial markets, healthcare and shipment tracking data 120+ capabilities: research, market analysis and data exploration Private deployment: on-premise and private cloud options How Powerdrill works You upload files or connect data, ask a question in plain language, and Powerdrill returns analysis with the supporting page, row and figure. Scheduled Agents rerun analysis on a recurring basis, and memory lets agents carry context between sessions. Sensitive data can stay in an on-premise or private cloud deployment. Who uses Powerdrill? Powerdrill is used by analysts, researchers and business teams who need quick answers from spreadsheets, reports and databases and want to audit where numbers came from. Powerdrill pricing Free is $0 with 1,000 daily credits. Pro is shown at $13.27 per month, Plus at $26.60 and Premium at $132.67, currently at a 20% promotional discount. Annual billing includes 2 months free. Powerdrill alternatives Alternatives include Julius AI and Akkio for chat-based data analysis, Polymer for dashboards, and GPTExcel for spreadsheet formula help.
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What is Kadoa? Kadoa is an AI web data extraction AI agent offering AI agents that extract, transform and monitor structured data from websites without brittle scrapers. Founded in 2022 and based in Zurich, Switzerland, Kadoa helps data teams in finance and research automate AI web data extraction work and get results faster. Key capabilities of Kadoa Self-healing extraction Schema-based outputs Change monitoring Data pipelines Scheduled extraction How Kadoa works Kadoa takes URL and web pages as input and produces structured data. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Google Sheets, Snowflake, Zapier and Slack, so the agent works inside existing workflows. Who uses Kadoa? Kadoa is built for data teams in finance and research. It suits teams that want self-healing extraction and schema-based outputs without adding headcount, while keeping people in control of review and final decisions. Kadoa vs Browse AI Kadoa is often compared with Browse AI. Kadoa stands out for self-healing extraction and change monitoring. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Thunderbit? Thunderbit is an AI web scraper that extracts structured data from websites without CSS selectors or configuration. It works as a Chrome extension and exports data to Excel, Google Sheets, Airtable and Notion. Key capabilities of Thunderbit One-click scraping: No selectors or setup needed. Structured export: Excel, Google Sheets, Airtable and Notion. Scheduled scrapers: Available on paid plans. Web Scraper API: For applications and workflows. Credit-based usage: One credit per output row. Yearly billing: Save 20% with credits upfront. How Thunderbit works You open a page, let the AI read its structure and fields, and run the scrape. Output rows are exported to your destination, and each output row consumes one credit. Who uses Thunderbit? Sales, research and operations users. The vendor reports 200,000+ users and cites Harvard University, BCG, Adidas and MIT. Thunderbit pricing Thunderbit lists Free, Starter, Advanced, Web Scraper API and Business plans, and yearly billing saves 20%. The pricing text did not expose amounts. Third-party sources report a free tier of 6 pages per month and paid plans from about $15 per month, so confirm with the vendor. Thunderbit alternatives ScrapeGraphAI is an API and open-source AI scraping library, Browse AI is a no-code scraper with monitoring, and Octoparse is a visual scraper. Thunderbit works inside the browser.
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What is Hex? Hex is a collaborative data workspace that combines notebooks, published apps and an AI data agent for analytics. Data teams use it to explore data, build interactive apps and share results. Key capabilities of Hex Notebooks: Collaborative SQL and Python workspace. Published apps: Share interactive data apps. AI data agent: Complete analysis tasks with AI credits. Version history: 7-day to unlimited by plan. Scheduled runs and alerts: Available on Team and Enterprise. How Hex works Analysts build notebooks, turn them into published apps for stakeholders, and use the AI agent to help with analysis. AI features consume credits based on the effort a task requires, with monthly per-seat credit grants on Professional and Team. Who uses Hex? Hobbyists use the free Community plan, individual analysts use Professional, and data teams use Team or Enterprise. Hex pricing The vendor lists Community as free, Professional at $36 per editor per month, Team at $75 per editor per month and Enterprise as custom. A 14-day Team trial needs no card. Paid plans include credits for AI features. Hex alternatives Julius AI is a chat-based data analyst, Databricks Genie answers questions over lakehouse data, and Sourcetable is an AI spreadsheet. Hex is distinguished by notebooks that publish as apps.
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What is Databricks Genie? Databricks Genie is the conversational analytics layer of Databricks AI/BI, answering business questions in natural language over governed lakehouse data so users do not need SQL. It is available to Databricks customers whose data is in Unity Catalog. Key capabilities of Databricks Genie Natural-language Q&A: Ask data questions without SQL. Expert curation: Add instructions, example queries and semantic definitions. Agent monitoring: Test with feedback scoring and accuracy benchmarks. Deep analysis: Multi-step reasoning for exploratory questions. API embedding: Embed in apps, Teams, Slack or Glean. How Databricks Genie works Data experts curate a Genie space with instructions, example queries and semantic definitions for their organization's terminology. Business users then ask questions, and Genie answers using data governed by Unity Catalog so responses follow existing access controls and remain auditable. Who uses Databricks Genie? Business teams on Databricks that want self-serve analytics, and data teams that curate and monitor the agents, use Genie. Databricks Genie pricing Genie is billed at standard Databricks DBU rates within the Lakehouse Pro and Serverless tiers, with no separate list price on the product page. Cost therefore depends on compute consumption. Databricks Genie alternatives ThoughtSpot is a search-driven analytics platform, Hex combines notebooks with an AI data agent, and Julius AI analyzes uploaded files in chat. Databricks Genie is distinguished by running natively on governed Databricks data.
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What is Qlik Answers? Qlik Answers is an agentic AI assistant from Qlik that combines its analytics engine with large language models to answer questions in natural language. It handles both structured analytics data and unstructured content such as documents, PDFs and images, and shows citations for its answers. Key capabilities of Qlik Answers Natural language questions: ask in plain language and get contextual answers Unstructured data Q&A: answers from documents, PDFs and images Analytics integration: uses Qlik analytics apps as a source Citations and explainability: shows sources behind each answer Multi-step reasoning: agentic handling of complex questions Embeddable assistants: standalone or embedded in operational apps How Qlik Answers works Qlik Answers uses retrieval-augmented generation. It searches the knowledge sources and analytics apps you connect through Qlik, then generates an answer grounded in the retrieved content with source citations. The vendor positions it as plug-and-play, deployable in hours rather than months. Who uses Qlik Answers? Business users and analytics teams already on Qlik use it to question reports and document collections without building queries. Developers can embed it in applications. Qlik Answers pricing Qlik does not list a price on the product page. It directs visitors to request a demo or start a free trial. Qlik Answers alternatives Qlik Answers is compared with Snowflake Cortex AI, Sourcetable and Formula Bot. Snowflake Cortex AI works inside the Snowflake data cloud, while Sourcetable and Formula Bot are spreadsheet-centered.
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What is SheetAI? SheetAI is a Google Sheets AI agent offering a Google Sheets add-on that brings AI functions for generating, classifying and transforming data in cells. SheetAI helps marketers and operations teams automate Google Sheets work and get results faster. Key capabilities of SheetAI AI cell functions Bulk content generation Data classification Text extraction Formula generation Automated analysis How SheetAI works SheetAI takes text and data as input and produces text and data. 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 SheetAI? SheetAI is built for marketers and operations teams. It suits teams that want AI cell functions and bulk content generation without adding headcount, while keeping people in control of review and final decisions. SheetAI vs Numerous.ai SheetAI is often compared with Numerous.ai. SheetAI stands out for AI cell functions and data classification. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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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.
