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MODULE · SIMBA INTELLIGENCE · EMBEDDED AI ANALYTICS

Deliver Embedded AI Analytics

Bring governed AI answers into your product experience, connected to live data and controlled by the same rules your application depends on.

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THE PROBLEM

Embedded AI Needs Trust

Customers expect AI answers to feel native, respect tenant boundaries, and stay grounded in governed data, not a generic widget pasted beside analytics.

Feel Native

Customers do not want a generic AI widget beside their analytics. They expect answers, explanations, and recommendations to feel like part of the product they use. If AI sits outside the experience, it becomes another disconnected place to check.

Right Answer Per Tenant

Customer-facing analytics must respect tenant boundaries, roles, and permissions. AI makes that harder because the interface feels open-ended, but the data cannot be. A customer should only get answers from data they are allowed to access.

Explain Without Inventing

Teams want AI to summarize trends, explain changes, and help users understand what they see. That only works when the answer is grounded in the same governed data behind the product. Otherwise the explanation is harder to trust than the chart.

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HOW IT WORKS

Embed Answers with Governed Control

Embedded AI analytics should feel natural in the product and controlled behind the scenes. Simba Intelligence maps product questions through an independent, unified semantic layer applying approved metrics, tenant rules, and live data.

Product Integration

Surface governed AI answers inside product workflows, dashboards, portals, and embedded analytics experiences.

Tenant-Aware Controls

Apply tenant, user, role, row, and column rules before product users receive answers.

Business Context Mapping

Map product language to approved metrics, dimensions, relationships, and customer-specific definitions.

Live Answer Computation

Compute answers from live enterprise data instead of relying on copied extracts or model memory.

Configurable Model Routing

Choose model paths for different workloads to balance speed, accuracy, reliability, cost, and deployment.

Reviewable Answer Logic

Expose the query path, business logic, and source context behind answers for review and troubleshooting.

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WHY US

AI Analytics Without Product Risk

Deliver a native product experience, keep tenant rules intact before every answer, and control deployment across on-premises, private cloud, and hybrid.

Native Product Experience

Embedded AI should support the product, not distract from it. Bring a governed answer layer into customer-facing workflows without forcing a separate AI destination.

Tenant Rules Stay Intact

A product experience is only trusted if every customer sees the right slice of data. Apply tenant and permission logic before the answer returns, protecting boundaries.

Controlled AI Deployment

Tune model choices for the job, including data source prep and query response. Align deployment with on-premises, private cloud, or hybrid, critical for regulated teams.

Build or Buy Embedded AI?

A prototype can answer a question. A customer-facing product needs governed answers, tenant controls, white-label fit, model choice, deployment flexibility, and answer review.
FeatureSimba IntelligenceBuild In-HouseGeneric AI Layer
Customer-facing AI answers
Tenant-aware permissions
Independent semantic layer
White-label product fit
Model choice by workload
On-premises or hybrid
Reviewable answer logic
Reusable governance layer

Frequently Asked Questions

What Is Embedded AI Analytics?

It brings AI-generated answers, explanations, summaries, and follow-up guidance into a product experience. Instead of a separate AI tool, the intelligence appears where users already analyze data and make decisions. The goal is more useful analytics inside the product, not another destination.

Who Is Embedded AI Analytics For?

Software companies, product teams, and data teams that want governed AI answers in customer-facing apps. It suits users who already rely on embedded dashboards, reports, portals, or analytics workflows. The buyer wants a better product experience without rebuilding the data access layer.

Where Would Embedded AI Analytics Appear in the Product?

Inside dashboards, data products, customer portals, operational workflows, or analytics views. Examples include plain-language explanations, metric summaries, variance analysis, or answers shown beside charts and KPIs. The AI should support the workflow, not interrupt it.

How Is This Different From Adding a Chatbot to Our Product?

A chatbot is one possible interface, but embedded AI analytics is broader. The answer might appear as a summary, explanation, recommendation, or guided insight inside the experience. The important part is not the chat box. It is the governed answer layer behind it.

How Do Tenant Permissions Work?

Tenant and user permissions are applied before an answer returns. A customer, admin, analyst, and support user may ask similar questions but get different results based on access rights. Embedded AI should follow the same tenant boundaries your product enforces.

Can AI Answers Go Beyond What Is Already in a Dashboard?

Yes, when connected to governed live data rather than a single dashboard surface. Users can ask for segments, filters, comparisons, or explanations that were not prebuilt. That flexibility is only safe when the answer still follows approved business logic.

Can We Choose Which LLM Is Used?

Yes. Simba Intelligence supports model configuration for the data source agent and query agent, so teams tune parts of the workflow separately. Current materials reference Google Vertex AI, Azure OpenAI, and AWS Bedrock. Balance speed, accuracy, reliability, and cost by workload.

Can This Support On-Premises or Regulated Product Environments?

Yes. Simba Intelligence is designed for on-premises, private cloud, and hybrid deployment, so teams align AI analytics with residency, customer policy, and infrastructure needs. A strong fit for regulated environments and any team avoiding a cloud-only AI dependency.

How Does This Work With Logi Symphony?

Logi Symphony provides the embedded analytics experience, including dashboards and customer-facing surfaces. Simba Intelligence provides the governed AI answer layer paired with that experience. AI capabilities come from Simba Intelligence, not Logi Symphony alone.

What Should Product Teams Plan Before Adding Embedded AI Analytics?

Start with the questions users ask, the metrics needing approved definitions, and the tenant rules that must never be bypassed. Then decide where answers appear and which workflows need AI first. The best projects begin with product experience and governance together.
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