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Average price: 47 products listed
47 Listings in Data Analysis Agents Available
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What is Copilot in Power BI? Copilot in Power BI uses generative AI to help people get the most from their data. Business users can chat with their data, find content, ask for ad hoc analysis and summarize reports. Report authors can create and edit reports and write DAX queries, and model authors can get recommendations on their semantic models. Key capabilities of Copilot in Power BI Chat with your data: standalone Copilot (preview) finds and analyzes any report or semantic model you can access. Report Copilot pane: ask questions or summarize an open report; generally available. Copilot in apps: app-scoped answers, with verified answers prepared by app authors (preview). Report creation: create and edit report pages from prompts. DAX and descriptions: write DAX queries and measure descriptions. Semantic model help: spot unclear structures and refine measures in web modeling (preview). Summaries: report summaries, including in subscriptions. How Copilot in Power BI works When a question relates to data in the semantic model, Copilot uses that model to answer. Otherwise it answers from the large language model's general knowledge. Microsoft recommends preparing the semantic model for AI first, and says repeated identical prompts on an unchanged model may be answered from cache within 24 hours. Who uses Copilot in Power BI? Copilot in Power BI is for business users, report authors and model owners in organizations already on Power BI. It requires an administrator to keep the Copilot tenant setting on, and it is not supported in sovereign clouds. Copilot in Power BI pricing Copilot requires paid Fabric capacity (F2 or higher) or Power BI Premium (P1 or higher). Trial capacities and free SKUs are not supported, and a Power BI Pro or Premium Per User license alone is not enough. Token consumption drives billing against capacity, measured in capacity units. Copilot in Power BI alternatives Databricks Genie lets business users ask questions of data in the Databricks lakehouse, so it fits teams centered on Databricks rather than Power BI. Sourcetable is an AI spreadsheet that analyzes data with natural language. Contentsquare CoPilot analyzes digital experience data rather than business reporting models.
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What is Amplitude AI? Amplitude AI is the set of AI agents and tools inside the Amplitude digital analytics platform. It lets teams ask product questions in plain language and run AI agents, AI Feedback and MCP access on top of analytics, session replay, experimentation and guides. Key capabilities of Amplitude AI AI Agents: Agents that work on analytics questions and insights Plain-language questions: Ask product questions without building charts AI Feedback: Analyzes customer feedback at scale MCP access: Connect external AI tools to Amplitude data Product analytics: Event-based analysis, cohorts and retention Session replay: 10K monthly replays on the free plan Experimentation and guides: A/B tests, guides and surveys on the same platform How Amplitude AI works Amplitude collects event data from your product, then agents and natural-language queries run analysis on it. Teams get answers, cohorts and insights without writing queries, and can feed the same data to other AI tools through MCP. Pricing is based on event volume. Who uses Amplitude AI? Product, growth and analytics teams. Amplitude also runs a Startup Scholarship giving qualifying companies a free Growth plan for one year. Amplitude AI pricing The Free plan includes 2M events per month with no credit card. Plus starts at $0 for the first 2M events and scales to 70M events. Growth and Enterprise use custom event-based pricing. Amplitude AI alternatives Alternatives include Tableau Agent for BI, Snowflake Cortex AI for warehouse-native AI and Pecan AI for predictive analytics. Amplitude centers on product behavior data.
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What is Graphy? Graphy is an AI tool that turns raw data into interactive charts for data storytelling, aimed at non-technical users. It accepts live Google Sheets links, CSV and Excel files, pasted values and API calls, and it reports more than 300,000 users. Key capabilities of Graphy AI chart creation: Builds charts from raw data and surfaces key insights with visible reasoning. Chat interface: Ask questions of the data and generate specific charts. Chart types: Bar, line, pie, funnel, heatmap and more. Custom branding: Custom colors and fonts, with brand kits on Business. Annotations: Collaborative notes to add context. Embeds and export: Embed charts in sites and documents or export them. Sheets refresh: Auto Google Sheets refresh on Business. How Graphy works You load data from a Google Sheet, CSV, Excel file, pasted values or an API, and the AI analyzes the dataset and proposes charts and insights. You then adjust styling, add annotations and share or embed the result in slides, posts, documents or team updates. Who uses Graphy? Marketers and business teams who report on data without analyst support. Platforms that want charts inside their own product can use the enterprise SDK and API option. Graphy pricing Free costs $0 and allows 5 charts with limited AI use. Plus is $16 per user per month with unlimited charts, unlimited AI, custom colors and logo removal, with a 20% annual discount. Business is $36 per user per month and adds auto Google Sheets refresh, password-protected links and brand kits. Graphy alternatives Chat2DB and Veezoo are query-by-chat tools for databases, while DataGPT and TextQL are conversational analytics for warehouses. Graphy focuses on shareable, embeddable charts.
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What is Dataminr? Dataminr is a technology company that delivers real-time event, threat and risk intelligence using AI. It monitors 1M public data sources and uses more than 50 proprietary LLMs, with multimodal fusion AI for detection, generative AI for analysis and agentic AI for context. It serves over 100 US government agencies and two-thirds of the Fortune 50. Key capabilities of Dataminr Dataminr for Corporate Security: protects people, facilities and operations Dataminr for Cyber Defense: ties threat intelligence to internal exposure and business impact First Alert: breaking event detection for public sector agencies Dataminr for News: breaking news discovery used by 1,500+ newsrooms Multimodal detection: fuses text, image and other signals Real-time alerts: delivers notifications as events emerge How Dataminr works Dataminr ingests public data from about 1M sources, uses multimodal fusion AI to detect emerging events, applies generative AI to summarize them and agentic AI to add context, then alerts customers in real time. Who uses Dataminr? Corporate security, cyber defense, public sector and newsroom teams use Dataminr. The vendor reports a $318 million contract with the US Department of War for First Alert Advanced. Dataminr pricing Dataminr does not publish pricing. Contact the vendor for a quote. Dataminr alternatives Dataminr is compared with Graphika, Blackbird.AI and Vannevar Labs. Graphika maps online networks, Blackbird.AI analyzes narrative risk, and Vannevar Labs serves defense intelligence.
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What is Graphika? Graphika is a decision intelligence platform that analyzes digital networks to reveal narratives, influence patterns and coordinated activity across online platforms. It delivers analyst-validated findings to enterprise and government customers. Key capabilities of Graphika Cross-platform collection: Connects fragmented activity across platforms and communities. Network mapping: Reveals relationships, coordination and behavioral patterns. Signal prioritization: Flags emerging narratives gaining momentum. Decision-ready intelligence: Analyst-validated findings with key actors and momentum indicators. Scam and fraud detection: Surfaces coordinated fraud activity. Geopolitical and cyber monitoring: Tracks risk narratives. How Graphika works Graphika connects activity from many platforms, maps the relationships among accounts and communities, and prioritizes signals that are gaining momentum. Analysts validate findings before they reach customers, so output pairs key actors and momentum indicators with human review. The vendor frames the goal as showing how narratives spread and which actors drive amplification. Who uses Graphika? Customers named by the vendor include Google, Meta, DARPA, NATO and Australian government agencies, plus financial institutions, media organizations and research institutions such as Stanford and Oxford. Use cases span trust and safety, crisis communications and brand influence mapping. Graphika pricing Graphika does not publish prices. The vendor directs interested parties to request a demo for pricing. Graphika alternatives Related tools include Vannevar Labs, ForceMetrics, Babel Street, Climatiq and Julius AI, which cover national security intelligence and data analysis.
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What is Tellius? Tellius is an enterprise analytics platform that builds recurring AI workers to run complete business jobs, such as variance reviews, payer audits and invoice exception handling. Its assistant, Kaiya, answers questions in plain language and powers the agents. It serves commercial pharma, market access, finance, procurement, manufacturing and supply chain teams. Key capabilities of Tellius Kaiya assistant: answers questions with evidence and flags changes proactively. Connect: provides AI-assisted data integration, preparation and a semantic layer. AI Insights: runs root cause, key driver, cohort and anomaly analysis. Visualizations and narratives: produces live dashboards and written summaries. AI workers: learn a team's definitions and rules, run on refreshed data, check each figure and route judgment calls to people. Kaiya Everywhere: delivers answers in Teams, Slack, email, Excel, PowerPoint and Salesforce. How Tellius works Tellius connects to data sources, builds a semantic layer, and trains workers on your definitions and methods. Workers run on a schedule, verify figures and trace them to rows, rules and approvers. The vendor says a job is typically set up in two to three weeks. Who uses Tellius? Commercial pharma and life sciences, consumer goods and eCommerce, healthcare, software and financial services teams. The vendor cites Novo Nordisk, PepsiCo, Agios and Bayer as customers. Tellius pricing Tellius does not list prices on its main page. The vendor offers a 30-day free trial and a pricing page. Tellius alternatives TextQL lets teams ask data questions in natural language over a warehouse. Veezoo is a natural-language analytics tool built on a semantic layer. Seek AI offers conversational analytics for enterprises.
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What is Babel Street? Babel Street is an agentic risk intelligence platform that uses AI agents to uncover hidden risks and produce actionable intelligence. It serves national defense and law enforcement customers as well as global enterprises and fintech leaders. Its products include Babel Street Match, Insights Investigator and Babel Street Data. Key capabilities of Babel Street Babel Street Match: AI-driven identity matching and alias resolution Insights Investigator: investigation workflow automation Babel Street Data: data ingestion and contextualization Multilingual processing: handles global, multilingual data Risk detection: real-time detection through governed agents Dark and surface web intelligence: threat monitoring across both How Babel Street works Babel Street ingests multilingual data, resolves identities and aliases, and lets governed agents surface risk for analysts to investigate. The platform is API-first and modular, so teams can use Match, Insights or Data separately. Who uses Babel Street? Defense, law enforcement, sanctions compliance, vendor risk and insider threat teams use Babel Street. A case study says CIRAT cut investigative time by 50% with Insights Investigator. Babel Street pricing Babel Street does not publish pricing. Contact the vendor for a quote. Babel Street alternatives Babel Street is compared with Dataminr, Blackbird.AI and ForceMetrics. Dataminr focuses on real-time event alerts and Blackbird.AI on narrative risk.
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What is Aily Labs? Aily Labs makes AI Decision Intelligence software that connects siloed company data to give employees real-time insights and recommendations. Its suite has modules for functions including Finance, R&D, Manufacturing and Sites, Supply Chain, Quality, Go To Market, Procurement, Regulatory Affairs, Ethics and People, and an iOS app. Key capabilities of Aily Labs Decision intelligence suite: insight and recommendation modules by business function. Cross-function analytics: connects data siloed across departments. Natural-language questions: employees ask questions and get answers. Executive feeds: surfaces insights to leaders. Mobile app: an iOS app is available. How Aily Labs works Aily connects to the company's data sources, builds a view across functions and delivers insights and recommendations to users, from executives to frontline staff. The vendor says setup takes about one day and mentions connecting to Outlook. Who uses Aily Labs? Organizations in healthcare and life sciences, consumer goods, luxury and fashion, media and tech, retail and services. Aily Labs pricing Aily Labs does not publish pricing; contact the vendor. Aily Labs alternatives TextQL lets business users query data in natural language, a narrower analytics use case. Veezoo is a natural-language analytics interface for business data. Vanna AI is an open-source text-to-SQL framework for developers.
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What is Veezoo? Veezoo is an agentic analytics platform that lets business users ask questions about their data in natural language and get governed answers. It positions itself as hallucination-free because a Knowledge Graph semantic layer, not a language model, defines the SQL that runs. Key capabilities of Veezoo Knowledge Graph semantic layer: Captures business definitions, metrics, KPIs and governance rules Veezoo Query Language: Compiles questions into deterministic SQL through rule-based logic Agentic monitoring: Agents investigate anomalies and trigger alerts Role, column and row security: Multi-layered governance on every query BI-as-code: Knowledge Graphs stored in Git repositories with version control Delivery channels: Answers in Slack, Teams, Excel and PowerPoint MCP and API access: Connects to Claude, ChatGPT, Gemini and Python How Veezoo works Data teams model metrics and business terms in the Knowledge Graph, which sits on top of a connected warehouse. When a user asks a question, Veezoo translates it into Veezoo Query Language and compiles that into SQL, so the same question returns the same governed answer. It queries data in place and does not ingest it or train AI on it. Who uses Veezoo? Business teams that want self-service analytics, and data teams that maintain governed semantic layers. Published customers include AXA, Bayer, BMW, Coop and Hahn Air. Veezoo pricing Veezoo offers a free trial. Enterprise pricing is not published and requires a consultation with the vendor. Veezoo alternatives Alternatives include ThoughtSpot for search-driven BI, Vanna AI for open-source text-to-SQL and Hex for collaborative notebooks. Veezoo differs by relying on a rule-based semantic layer for SQL.
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What is ScrapeGraphAI? ScrapeGraphAI is a web scraping and data extraction service that uses LLMs. It offers scrape, extract, search and monitor operations on a credit-based API. Key capabilities of ScrapeGraphAI Scrape: From 1 credit for markdown to 25 for branding analysis. Extract: Structured extraction at 5 credits base. Search: 2 to 5 credits per result. Monitor: Base cost plus 5 credits when changes are detected. Stealth toggle: Adds 5 credits for anti-bot bypass. Rate limits by plan: 10 to 5,000 requests per minute. How ScrapeGraphAI works You call the API or use Python and Google Sheets integrations to request scrapes or extractions. Each operation consumes credits, which never expire and can be topped up. Who uses ScrapeGraphAI? Developers and data teams extracting web data for apps and analysis. ScrapeGraphAI pricing Free gives 500 one-time credits at 10 requests per minute. Starter is $20 per month for 10,000 credits, Growth is $100 for 100,000, Pro is $500 for 750,000, and Enterprise is custom. ScrapeGraphAI alternatives Thunderbit is a no-code AI scraper, and Formula Bot and Numerous.ai focus on spreadsheet AI. ScrapeGraphAI is API-first.
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What is Snowflake Cortex AI? Snowflake Cortex AI is Snowflake's set of AI capabilities that run on data inside the Snowflake platform. The vendor lists agents, AI functions in SQL, natural-language-to-SQL and access to large language models including Anthropic Claude, Meta Llama and Mistral Large 2 through serverless functions and APIs. Processing stays within Snowflake's security perimeter. Key capabilities of Snowflake Cortex AI Cortex Agents: orchestrate across structured and unstructured data to retrieve and synthesize insights. Cortex AI Functions: run AI over text, images and audio using SQL. Cortex Analyst: converts natural-language questions to SQL. Snowflake CoWork: a personal work agent that answers questions about enterprise data and acts in tools like Slack, Gmail, Jira and Salesforce. Snowflake CoCo: a data-native AI coding agent, formerly Cortex Code. LLM access: models including Claude, Llama and Mistral Large 2. AI Gateway and Training: Cortex AI Gateway and Cortex Training. How Snowflake Cortex AI works Teams call AI functions from SQL or build agents that query structured tables and unstructured documents without moving data out of Snowflake. Cortex Analyst turns business questions into SQL against defined data models. Built-in policies, access controls, guardrails and observability apply to AI workloads the same way they apply to other data. Who uses Snowflake Cortex AI? Data engineers, analysts and application teams already on Snowflake use it to add AI without exporting data. Organizations not on Snowflake would need to adopt the platform first. Snowflake Cortex AI pricing The product page does not describe pricing and links to Snowflake's pricing options. Snowflake generally bills on consumption, so check the pricing page for current rates for Cortex features. Snowflake Cortex AI alternatives Domo AI adds AI features to the Domo business intelligence platform, aimed at dashboards and reporting rather than in-warehouse functions. Akkio offers no-code predictive analytics and data analysis for business users, without being tied to a data warehouse. Amplitude AI applies AI to product analytics events, a narrower analytics domain than a general data platform.
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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.
Tech stacks
See where data analysis agents fits in a complete stack, with the other software, AI agents and services each business needs.
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.