What platforms does Sisense support?

Sisense is available on Web App.

What are the limitations of Sisense?

Pricing is not published anywhere on the homepage, so cost has to be discovered through sales. Assumes real data engineering and security work; not practical for teams without developer resource. The value proposition is embedding inside a product, so internal-only BI buyers pay for capability they may not need.

Sisense

Name: Sisense
Author: Sisense

Embed governed AI analytics inside your own product

Advanced API

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Screenshot of Sisense, Embed governed AI analytics inside your own product

What is Sisense?

Sisense is an AI chat bot tool. Embedded analytics platform for SaaS products, combining governed metrics, multi-tenant security and natural language AI with SDKs and APIs for embedding dashboards and assistants under your own brand.

Sisense is an embedded analytics platform aimed at software companies that want to put AI-driven reporting inside their own products under their own brand. Rather than a standalone dashboard tool, it positions itself as a governed distribution layer: metrics, semantic definitions and access rules are defined once in Sisense, then served to both human users and AI assistants so that answers stay consistent and tenant-safe. The company cites more than 20 years of experience powering analytics in customer-facing products, with named customers including Seismic, PagerDuty, Philips and Cropin. The platform is built around governed semantics. Shared formulas keep approved definitions in one place, semantic enrichment gives models the context to interpret fields correctly, and row-level security plus multi-tenant architecture are enforced at the query layer so one customer's data cannot surface in another customer's answer. On top of that data foundation sit user-facing capabilities: a natural language assistant for asking questions, narratives that turn result sets into written summaries, explanation features that identify what drove a change, and pulse and forecast tools that flag where trends are heading. Implementation is deliberately flexible. Teams can embed a single widget or build a custom experience with the Compose SDK and APIs, generate embed code to speed up development, work across multiple frameworks and extend the interface with custom plugins. Sisense also advertises MCP and API connections so the same governed data can feed the AI tools customers already use, without locking the product into one AI experience. This is not a self-serve tool for a solo analyst. The buyers are product and engineering teams at SaaS companies commercializing analytics, and the work assumes data modeling, security configuration and developer involvement. Compared with BI suites sold to internal teams, Sisense trades broad dashboarding breadth for embedding depth and governance. Pricing is not published on the homepage, so budgets need to be qualified through a sales conversation.

How do you use Sisense?

1Book a demo and scope the embedding use case
Start with a guided demo session. Bring the product surface where analytics should appear and the customer segments that will use it, since Sisense is sold as an embedded platform rather than a self-serve signup.
2Model metrics with shared formulas and semantics
Define the measurements and dimensions your product reports on, using shared formulas so people and AI work from the same approved definitions. Semantic enrichment adds the context a model needs to interpret fields correctly.
3Configure multitenancy and row-level security
Set up tenant isolation and row-level access rules at the query layer before any customer sees an answer. This is the step that prevents one account's data appearing in another account's response.
4Embed with the Compose SDK and APIs
Choose the embedding pattern that matches your product, from a single widget to a fully custom interface. Compose SDK tooling can generate embed code and custom plugins extend the experience where needed.
5Turn on the assistant and narrative layers
Enable natural language querying so users can ask questions without navigating dashboards, and add narratives and explanations so results arrive with a written summary and the drivers behind a change.
6Connect external AI workflows through MCP or APIs
Expose the governed data to the AI tools your users already rely on. This keeps one set of definitions and access rules in place while intelligence appears across product surfaces and third party assistants.

Pros and cons

Pros

Strong governance story: shared formulas, semantic enrichment and row-level security applied at the query layerAI
Multiple embedding paths, from lightweight widgets to a Compose SDK and custom pluginsAI
Natural language assistant, narratives, explanations and forecasting cover both asking and understandingAI
MCP and API support lets the same governed data feed external AI tools and workflowsAI
Two decades of embedded deployments, with public case studies from Cropin, Barrios, Funraise and BigtincanAI

Cons

Pricing is not published anywhere on the homepage, so cost has to be discovered through salesAI
Assumes real data engineering and security work; not practical for teams without developer resourceAI
The value proposition is embedding inside a product, so internal-only BI buyers pay for capability they may not needAI
Marketing is heavy on positioning language, with little concrete detail on model choice or AI accuracy limitsAI
Feature depth across assistant, narratives, pulse and forecast means a longer learning curve than a simple charting libraryAI

How much does Sisense cost?

Support

Support runs through documentation and a demo request path; the public site does not describe specific support tiers or service level commitments.

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Integrations

MCP and API connections let Sisense data feed the AI tools and workflows users already rely on, and custom connector plugins extend the platform's data connectivity.

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Features

Governed semantics with shared formulas, semantic enrichment, multi-tenant architecture and row-level security; natural language assistant and search; narratives and explanations that describe results and causes; pulse, forecast and trend detection; Compose SDK, APIs, generated embed code and custom plugins; MCP and API connections to external AI tools.

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Frequently asked questions about Sisense

Does Sisense have an API?
Yes, Sisense offers an API.
How do you use Sisense?
The walkthrough on this page covers 6 steps: 1. Book a demo and scope the embedding use case 2. Model metrics with shared formulas and semantics 3. Configure multitenancy and row-level security 4. Embed with the Compose SDK and APIs 5. Turn on the assistant and narrative layers 6. Connect external AI workflows through MCP or APIs.
What platforms does Sisense support?
Sisense is available on Web App.
What does Sisense integrate with?
MCP and API connections let Sisense data feed the AI tools and workflows users already rely on, and custom connector plugins extend the platform's data connectivity.
What are the limitations of Sisense?
Pricing is not published anywhere on the homepage, so cost has to be discovered through sales. Assumes real data engineering and security work; not practical for teams without developer resource. The value proposition is embedding inside a product, so internal-only BI buyers pay for capability they may not need.