Sisense Ltd.
UnclaimedSisense is an AI-powered embedded analytics platform that enables developers to embed secure, governed analytics into applications under their brand.
Overview
Sisense is an AI-powered embedded analytics platform that enables developers and product teams to embed analytics and AI capabilities directly into their applications. The company’s mission is to empower app builders by delivering analytics that are accessible within branded product experiences, helping teams deliver data-driven insights to end users without leaving the application. Sisense serves software makers and organizations building customer-facing products, offering a scalable, secure way to connect data from multiple sources, model it, and present it through embedded analytics. The platform emphasizes a developer-first approach with APIs and software development kits, governance and security in multi-tenant environments, and the ability to ship analytics that match a brand’s look and feel. Sisense positions itself as a foundation for product-led analytics, designed to accelerate innovation and turn data into a competitive edge while maintaining control over definitions, metrics, and access. By grounding analytics in governed data and providing intuitive experiences such as customizable dashboards and narrated insights, Sisense aims to make analytics a natural part of product experiences across industries and company sizes. The result is a scalable, cloud-enabled solution that reduces time to insight, supports rapid iteration, and enables decision-makers to act on data with confidence.
Mission statement
We’re building a world where analytics fuel the applications that drive business forward.
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What we offer
Sisense AI Analytics Platform
Unlock actionable insights embedded directly into applications with Sisense AI analytics platform.
Pricing not published
www.sisense.com/ai-analytics-platform/Market segments
Market size by segment
Growth potential (CAGR)
Embedded analytics
Embedding interactive, branded, and role-based analytics into customer-facing applications to deliver real-time insights, AI-powered recommendations, and scalable in-product dashboards for ISVs and product teams.
Products: Sisense AI Analytics Platform
Augmented analytics
AI-driven insight generation, natural-language querying, and agent automation that surface answers and recommendations from behavioral data.
Products: Sisense AI Analytics Platform
Enterprise semantic layer
Centralized semantic definitions and reusable business logic that deliver consistent, governed metrics across BI tools, AI agents, and applications to enable metric portability and BI modernization.
Products: Sisense AI Analytics Platform
Embedded analytics and white-labeling
Embed real-time dashboards, KPIs, and zero-click insights into web, mobile, and products via APIs and white-labeled delivery to surface analytics inside customer workflows.
Products: Sisense AI Analytics Platform
More information about our offering
Sisense AI Analytics Platform
Sisense AI analytics platform is a cloud-enabled analytics foundation that enables developers and product teams to embed AI powered analytics into applications. It supports code-first, low-code, and no-code approaches with Compose SDK and APIs to embed, customize, and scale analytics within products. The platform emphasizes governance, security, and multi-tenant isolation, enabling end users to ask questions, receive natural language explanations, and act on insights directly inside their workflows. It offers semantic enrichment, assistant interactions, and the ability to connect to external AI tools via MCP, delivering AI powered dashboards, narratives, and forecasts while keeping definitions and access under control.
Pricing not published
- Transform Questions Into InsightsEmpower users to interact with data naturally, elevating the analytics experience through AI-driven responses.
- Integrate Intelligence DirectlySeamlessly incorporate insights into applications, driving engagement through embedded analytics.
- Ensure Data SecurityMaintain strict control over data access and governance, ensuring compliance and security in analytics.
- Reduces Operational BurdenManaged services ensure consistent performance and updates, allowing teams to focus on analytics rather than infrastructure.
- Ensures Continuous OperationsRound-the-clock support and monitoring help prevent disruptions and improve user satisfaction.
- Protects Sensitive DataRobust security measures ensure data integrity and compliance, building trust with users.
- Enhance User EngagementAllow users to interact with analytics using plain language, making data insights more accessible and intuitive.
- Implement Quick ChangesDevelopers can create dynamic queries, charts, and filters in real-time, adapting to changing data landscapes.
- Ensure Stability and ScaleUtilizes a robust platform framework to deliver consistent performance and accommodate growth.
- Generate Actionable InsightsLeverage advanced generative AI techniques to automate insights generation, predicting trends and patterns to enhance data-driven decision-making.
- Connect Data EasilyAccess a diverse range of data sources seamlessly with over 200 connectors, enabling streamlined data integration and analysis.
- Enable Real-Time InsightsLeverage live connections to cloud data warehouses for real-time analytics without the headache of data duplication.
- Ensure Data SafetyGoverned access and strict security protocols protect your data and maintain compliance with industry standards.
- Integrate AI Tools SecurelySeamlessly connect AI tools with governed data models, ensuring secure and structured insights.
- Simplifies Data AccessEmpowers users to easily model complex data systems without requiring extensive technical skills, enhancing productivity.
- Embed Seamless AnalyticsEasily integrate analytics into applications, ensuring a consistent brand experience and functional flexibility.
- Enhance AI UnderstandingProvide AI with the context needed to deliver accurate insights through enriched data semantics.
- Engage Users With AIAdvance user experience through an interactive dialogue with AI, making analytics more accessible.
- Facilitates GrowthAdaptable resources allow organizations to grow without delays, ensuring performance remains optimal as demand increases.
- Flexible and Versatile IntegrationEasily incorporate analytics across various web frameworks to suit diverse development environments.
- Accelerate Development TimeAutomates coding processes, allowing teams to focus on higher-level tasks and innovation.
- Enhance Analytics CapabilitiesIntegrate AI technologies to turn data into actionable insights more effectively.
- Ensure Metric ConsistencyUtilize a robust semantic layer to maintain consistent metrics and access controls, ensuring reliable analytics across all user interactions.
- Leverage AI Within ProductEnable users to interact with their data through natural language queries and receive AI-driven insights directly within the product environment.
- Secure Multi-Customer DeploymentsImplement strong security measures, ensuring tenant isolation and safeguarding data across multiple customer instances.
- Automates Data PreparationLeverage AI to streamline the data preparation process, allowing users to focus on analytics while reducing manual tasks.
- Enhances Data FlexibilityCombine and analyze data from different systems seamlessly, providing a comprehensive view for informed decision-making.
- Align with Brand IdentityCreate cohesive look and feel by customizing visualizations to adhere to your branding guidelines.
- Predicts Trends and AlertsUtilize advanced forecasting to project future trends and receive alerts for anomalies, helping users stay proactive in their decision-making.
- Enables Conversational QueriesEmpower users to interact with analytics through natural language inquiries, simplifying the data exploration process.
- Personalize User ExperienceCustomize analytics visuals and interactions, aligning them with the product's branding for a cohesive user journey.
- Enhances Team EfficiencyFacilitates data sharing across departments, improving decision-making and accountability.
- Streamline Development ProcessEnhances communication and reduces friction between teams to speed up the embedding of analytics.
- Extend FunctionalityDevelop unique functionalities that cater specifically to business needs and enhance the user experience.
- Integrate External AI ToolsSeamlessly connect to third-party AI tools using APIs and the Multi-Cloud Platform (MCP) while maintaining strict governance over data access and metrics.
- Empower Users to Analyze DataProvide users with the tools they need to explore data and generate their own insights independently within the embedded analytics platform.
- Customize Analytics ExperienceBrand the analytics experience to align with your product's aesthetics, utilizing customizable components and theming options.
- Facilitates Advanced AnalysisPerform sophisticated data analysis and transformations directly within Sisense, integrating findings seamlessly into your workflows.
- Streamlines WorkflowReduce manual effort and improve efficiency by leveraging Sisense's automation capabilities in data handling.
- Integrates AI Tools SeamlesslyFacilitate effective collaboration by connecting your Sisense data to the AI tools your users already rely on, enhancing analytic workflows.
- Provides Clear ExplanationsTransform complex data into understandable narratives that clearly explain the drivers of the metrics, enhancing user comprehension.
- Customizes User ExperienceCreate personalized data views while maintaining a unified data model, catering to specific user needs without duplication.
- Customizes User ExperienceAllow users to personalize their analytics experiences, driving engagement and usability through tailored dashboard options.
- Stay UpdatedFrequent updates help users leverage the latest features and enhancements, ensuring optimal performance.
Sources
Methodology and sourcing behind the figures and links shown above.
Embedded analytics
Primary estimate based on a published projection (cited on embeddable.com) that the embedded analytics market grows from $23.4B (2025) to $101B (2035), implying ~15.8% CAGR. Supporting sources show smaller, earlier estimates (Reveal: $0.8B in 2019 to $1.3B in 2023 at ~10–15% CAGR) and a narrower IoT-embedded-analytics report with much higher CAGR (KBV: $1.4B in 2023 to $14.4B in 2031, 35.1%), indicating growth varies by scope and vertical.
Augmented analytics
Estimates based on published market reports in the search results: ResearchAndMarkets (via GlobeNewswire) reports an estimated market value of USD 16.6 billion in 2023 and a 28.0% CAGR to 2030; Mordor Intelligence projects USD 41.87 billion in 2025 and a 23.17% CAGR to 2030. I use the 2023 reported size (16.6B) and a midpoint CAGR (~25%) between the reported 23.2% and 28.0% to express growth potential.
Enterprise semantic layer
Search results show the enterprise semantic layer is positioned as a newly critical layer for enterprise AI and analytics (AtScale blogs, AtScale 2026 State report mention, CDOTrends article, Digetiers thought leadership), indicating accelerating demand but no explicit TAM figures in the provided results. Using internal market reasoning: treat the semantic layer as a specialized enterprise-software segment (SaaS/managed) addressing large enterprises. A conservative sizing assumption: a near-term commercial base of ~10,000 mid-to-large enterprise customers adopting paid semantic-layer solutions (or modules of larger platforms) with average vendor spend of roughly $100–200k/year yields an approximate market of $1.0–2.0B (midpoint ~$1.5B). Growth potential (CAGR ~28%) reflects rapid adoption driven by generative/agentic AI needs for governed, portable metrics, strong vendor investment and partnerships (AtScale integrations with Snowflake/Databricks/etc.), and comparisons to adjacent high-growth subsegments (metadata/knowledge-graph/AI-governance tooling), which have shown multi‑decade-high growth rates as enterprises operationalize AI.
Embedded analytics and white-labeling
Estimate uses an explicit 2026 embedded-analytics market figure from an industry article (77.52 billion USD) and a high-growth CAGR from a syndicated market report on augmented/AI-driven analytics (27.35%). Augmented analytics overlaps with embedded and white-label use cases, so the Mordor CAGR is applied as representative growth potential while the Querio figure is used for the current market-size estimate.
- Sisense Ltd.
- Sisense AI Analytics Platform
- "the embedded analytics market is expected to grow from $23.4B in 2025 to nearly $101B by 2035."
- "Embedded analytics market is valued at $800 million in 2019 and is projected ... to reach an estimated $1.3 billion market in 2023"
- "2023 USD 1.4 Billion 2031 USD 14.4 Billion CAGR 35.1%"
- Estimated Market Value (USD) in 2023 $16.6 Billion
- Market Size (2025) USD 41.87 Billion; Market Size (2030) USD 118.72 Billion; Growth Rate (2025 - 2030) 23.17%
- In 2026, embedded analytics is a must-have for SaaS products, with the market expected to reach $77.52 billion.
- Estimated to grow to USD 127.18 Billion by 2031, at a CAGR of 27.35%.
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