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Average price: 51 products listed
51 Listings in Data Analysis Agents Available
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What is Copilot in Power BI? Copilot in Power BI is a BI copilot AI agent offering Microsoft Copilot in Power BI that builds reports, writes DAX and answers questions about data. Founded in 2023 and based in Redmond, Washington, USA, Copilot in Power BI helps analysts on Power BI automate BI copilot work and get results faster. Key capabilities of Copilot in Power BI Report generation DAX assistance Narrative summaries Data Q&A Governed data access Natural-language questions How Copilot in Power BI works Copilot in Power BI takes text and data as input and produces reports and text. It is powered by OpenAI GPT (Microsoft) 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 Copilot in Power BI? Copilot in Power BI is built for analysts on Power BI. It suits teams that want report generation and DAX assistance without adding headcount, while keeping people in control of review and final decisions. Copilot in Power BI vs Tableau Agent Copilot in Power BI is often compared with Tableau Agent. Copilot in Power BI stands out for report generation and narrative summaries. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Amplitude AI? Amplitude AI is a product analytics AI AI agent offering Amplitude AI for asking product questions in plain language, automated insights and AI agents. Founded in 2023 and based in San Francisco, California, USA, Amplitude AI helps product and growth teams automate product analytics AI work and get results faster. Key capabilities of Amplitude AI Ask Amplitude Automated anomaly insights AI agents for growth Data quality help Governed data access Natural-language questions How Amplitude AI works Amplitude AI takes text and event data as input and produces charts 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, Databricks and Salesforce, so the agent works inside existing workflows. Who uses Amplitude AI? Amplitude AI is built for product and growth teams. It suits teams that want Ask Amplitude and automated anomaly insights without adding headcount, while keeping people in control of review and final decisions. Amplitude AI vs Mixpanel Spark Amplitude AI is often compared with Mixpanel Spark. Amplitude AI stands out for Ask Amplitude and AI agents for growth. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Graphy? Graphy is a data storytelling AI agent offering an AI data visualization tool that turns data into interactive charts with written insights for reports and decks. Graphy helps marketers and business teams automate data storytelling work and get results faster. Key capabilities of Graphy AI chart creation Written insights Interactive embeds Team boards Instant charts AI insights How Graphy works Graphy takes data as input and produces charts 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 Google Sheets, CSV, Excel and Notion, so the agent works inside existing workflows. Who uses Graphy? Graphy is built for marketers and business teams. It suits teams that want AI chart creation and written insights without adding headcount, while keeping people in control of review and final decisions. Graphy vs Flourish Graphy is often compared with Flourish. Graphy stands out for AI chart creation and interactive embeds. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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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 Kausa? Kausa is an AI analyst for commerce AI agent offering an AI analyst that finds root causes behind KPI changes for e-commerce and marketing teams. Founded in 2020 and based in Berlin, Germany, Kausa helps e-commerce and growth teams automate AI analyst for commerce work and get results faster. Key capabilities of Kausa Automated root-cause analysis KPI monitoring Driver explanations Actionable insights Automated insights Channel profitability How Kausa works Kausa takes analytics data 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 Shopify, Centra, Google Analytics and Meta Ads, so the agent works inside existing workflows. Who uses Kausa? Kausa is built for e-commerce and growth teams. It suits teams that want automated root-cause analysis and KPI monitoring without adding headcount, while keeping people in control of review and final decisions. Kausa vs DataGPT Kausa is often compared with DataGPT. Kausa stands out for automated root-cause analysis and driver explanations. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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Seek AI is a generative AI data analyst that lets business and data teams ask questions of their data in plain language and get accurate answers, without writing SQL. What Seek AI does Ask in natural language: converts business questions into queries (text-to-SQL) against your databases and warehouses. Conversational analytics: return answers and insights in a chat-style interface. Accuracy & governance: a semantic layer and controls to keep answers trustworthy. Self-service: lets non-technical users explore data without waiting on analysts. Who it's for Data and business teams that want self-service, natural-language access to enterprise data.
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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 a decision intelligence AI agent offering an AI decision intelligence platform with natural-language search and automated insights. Founded in 2016 and based in Reston, Virginia, USA, Tellius helps commercial analytics teams automate decision intelligence work and get results faster. Key capabilities of Tellius Natural-language search Automated root-cause insights Agentic analytics Pharma and CPG data Chart generation Scheduled reports How Tellius works Tellius takes text and database data as input and produces insights 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, PostgreSQL and Google Sheets, so the agent works inside existing workflows. Who uses Tellius? Tellius is built for commercial analytics teams. It suits teams that want natural-language search and automated root-cause insights without adding headcount, while keeping people in control of review and final decisions. Tellius vs ThoughtSpot Tellius is often compared with ThoughtSpot. Tellius stands out for natural-language search and agentic analytics. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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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 is a decision intelligence AI agent offering an AI decision intelligence app that gives enterprise leaders insights and recommendations. Founded in 2020 and based in Munich, Germany, Aily Labs helps large enterprise leadership teams automate decision intelligence work and get results faster. Key capabilities of Aily Labs Executive insight feeds AI recommendations Cross-function analytics Natural-language questions Developer SDKs Open-source components How Aily Labs works Aily Labs takes enterprise data as input and produces insights and recommendations. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Python, LangChain, LlamaIndex and OpenAI, so the agent works inside existing workflows. Who uses Aily Labs? Aily Labs is built for large enterprise leadership teams. It suits teams that want executive insight feeds and AI recommendations without adding headcount, while keeping people in control of review and final decisions. Aily Labs vs Tellius Aily Labs is often compared with Tellius. Aily Labs stands out for executive insight feeds and cross-function analytics. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Veezoo? Veezoo is a conversational analytics AI agent offering a conversational analytics platform that turns natural-language questions into governed answers from your data. Founded in 2016 and based in Zurich, Switzerland, Veezoo helps business users in mid-size and large companies automate conversational analytics work and get results faster. Key capabilities of Veezoo Natural-language questions Knowledge graph semantics Auto-generated dashboards Governed access Semantic layer support Chart generation How Veezoo works Veezoo takes text and database data as input and produces charts, tables 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, Databricks and PostgreSQL, so the agent works inside existing workflows. Who uses Veezoo? Veezoo is built for business users in mid-size and large companies. It suits teams that want natural-language questions and knowledge graph semantics without adding headcount, while keeping people in control of review and final decisions. Veezoo vs ThoughtSpot Veezoo is often compared with ThoughtSpot. Veezoo stands out for natural-language questions and auto-generated dashboards. 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.
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.