WisdomAI Embedded Agentic Analytics puts colour into white-labelled AI

White-labelled AI

Companies can use this Embedded Agentic Analytics toolset to create white-labelled OEM agentic analytics services, which they can then present as “own brand” agentic analytics service in a bid to gain wider and greater customer recognition and confidence, all without building it themselves.

Embedded Agentic Analytics comes bundled with conversational BI and AI-powered dashboards. Unlike embedded static dashboards and in-house AI builds (that WisdomAI claims can take years, which would probably still take weeks or perhaps months) and still (more claims here) stall at around 50% accuracy, WisdomAI embeds in weeks via iFrame, React SDK, or GraphQL API.

iFrame, defined

For completeness here, let’s remember that an iFrame is an HTML element that embeds a secondary website inside another page; a React SDK provides pre-built components and hooks to integrate external services into apps; and A GraphQL API allows clients to request exactly the specific data they need from servers. 

“Standing up our own AI Analytics solution would have taken years and AI analytics isn’t our core business. We need our team focused on loan origination point-of-sale,” said Mukesh Jha, GM at Blend, a company that (perhaps unsurprisingly) works in the loan origination space.

Blend is actually a “digital origination platform” powering mortgage and consumer lending for banks, credit unions, and independent mortgage banks across the US.  In 2025, lenders processed $1.3 trillion in loan applications on Blend’s platform, or roughly 1 in 6 American mortgages. Blend needed a way to turn that volume of activity into real-time BI, both for its own operations and for the lenders running their businesses on its platform.

Chatting up conversational BI 

The Wisdom team say that its platform is the only embedded platform where conversational BI and AI-powered Dashboards ship together.

Customers get infinite drill-down (where users click a high-level summary metric to view the detailed granular data underneath) and deep, multi-step analysis across the product’s proprietary data sources, with the ability to securely federate third-party data sources (as explained above) alongside.

Three embedding options are on offer here. Users can go live in days with iFrame, gain full customisability in weeks with the React SDK, or build a fully custom UI on the GraphQL API. Theming, filtering, and real-time updates ship out of the box. The Wisdom R&D team also continuously ships new AI capabilities.

WisdomAI Embedded Agentic Analytics is designed to clear customer security reviews on first pass. Embedded customers get single-tenant VPC deployment options for full data isolation, JWT-based cookieless authentication, row-level security enforced at query time, and certifications include SOC 2 Type II, HIPAA, and GDPR compliance.

Out-of-the-box agents

Every embedded deployment exposes a governed MCP endpoint that agents can access, scoped per tenant, and backed by the same Adaptive Context Engine that powers in-app analytics. External agents inherit the same accuracy, RLS, and audit trail as in-app users,  so whatever ships today still works when customers’ agents start hitting the product.

Core features include:

Adaptive Context Engine for 95%+ accuracy: The context engine auto-learns metric definitions, entity relationships, and business vocabulary across each customer’s data. Every embedded surface inherits this context, so accuracy scales with usage, not engineering effort

Tenant and user management for multi-tenant SaaS:  JWT-based cookieless authentication with silent token refresh, row-level security enforced via token claims at query time, and role-based access control with a flexible hierarchy for multi-tenant deployments.

AI-native surfaces with shared accuracy and governance: Embedded chat, dashboards, and Analytics Agents all share the same accuracy, security model, and audit trail, so what a user sees in a dashboard, an agent, or a conversation is consistent and trustworthy.

Customers bring their own LLM keys and deploy in single-tenant, multi-tenant, or on-prem configurations. WisdomAI never trains models on customer data, which is a non-negotiable for regulated buyers in financial services, healthcare, and security.

Are agents now middleware?

Taking stock of this whole story, can we say that WisdomAI’s developments feel like embedded BI now moving into agentic middleware?

The core product updates do back up this suggestion i.e. less dashboard fatigue, more conversational abstraction layered over federated enterprise data… and a signal for AI-developers that this is a shift away from building bespoke analytics stacks toward orchestrating governed AI surfaces via APIs, SDKs and MCP endpoints. In basic terms, this means developers become AI conductors, not ETL plumbers.