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MODULE · SIMBA INTELLIGENCE · GOVERNED AI CHAT

Enable AI Chat With Your Data

Give teams a governed way to ask business questions in natural language and get answers computed from live enterprise data.

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CapterraG2

THE PROBLEM

Chat Needs Dashboard Trust

A chat box makes data feel accessible but not automatically trustworthy. Answers must stay inside approved metrics, permissions, and a tunable model path.

As Trusted as Dashboards

Analytics earns trust when logic is defined, repeatable, and reviewable. Users still need to ask follow-ups, compare segments, and investigate situations not built into a dashboard. Bring that same governed consistency to natural-language questions.

Stay in Definitions

A chat box makes data feel accessible but does not make the answer trustworthy. Without approved metrics, business rules, and permissions, it can return an answer that sounds right but does not match how the organization measures performance.

Tuned for the Job

Not every AI workload needs the same model strategy. A proof of concept may prioritize speed and cost; regulated workflows need accuracy, reliability, and controlled deployment. If model choice is fixed, teams lose the tradeoffs.

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HOW IT WORKS

Turn Questions Into Governed Answers

AI chat should feel flexible to the user and controlled behind the scenes. Simba Intelligence maps plain-language questions through an independent, unified semantic layer applying approved metrics, permissions, and live data.

Question Intake

Accept natural-language business questions from approved chat experiences, assistants, or internal workflows.

Business Term Mapping

Map user language to approved metrics, dimensions, relationships, time periods, and business definitions.

Permission-Aware Access

Apply user, role, row, tenant, and column controls before data is queried or returned.

Live Answer Computation

Compute answers from live enterprise data instead of pasted extracts, cached screenshots, or stale exports.

Tunable Model Strategy

Configure model choices for data prep and question answering to balance speed, accuracy, reliability, and cost.

Reviewable Output

Return answer context that lets teams inspect the query path, logic, and source data behind results.

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WHY US

AI Chat Without Data Shortcuts

Get dashboard-level repeatability, metrics that do not drift between answers, and control by workload across speed, precision, reliability, and deployment fit.

Dashboard-Level Repeatability

People trust dashboards when logic is fixed, governed, and easy to review. Bring that discipline to chat by resolving questions through an independent semantic layer.

Metrics That Do Not Drift

Chat confuses if answers depend on how the model reads a term. Apply shared business context before computing, avoiding competing versions of revenue, churn, or margin.

Control by Workload

Tune model choices across the workflow, including data prep and query response. Control speed, precision, reliability, cost, and deployment, especially on-premises.

Why Not Chatbot or Dashboard AI?

Chatbots and dashboard AI both support natural language. The difference is where answers come from, how much governed data exists, and whether one path serves many surfaces. [173/175]
FeatureSimba Intelligence Generic chatbot Dashboard-bound AI
Ask in plain language
Approved metric definitions
Permissions before data
Beyond prebuilt content
Follow-ups across data
Serves many AI surfaces
Live enterprise answers
Reviewable answer logic

Frequently Asked Questions

Who Is AI Chat With Data For?

Teams that need faster answers from governed business data without waiting for a dashboard change, ticket, or SQL query. Common users include operations, customer teams, product leaders, executives, and analysts reducing repetitive requests. The goal is trusted self-service, not uncontrolled access.

What Kinds of Questions Are a Good Fit?

Questions tied to governed data: metrics, trends, comparisons, segments, performance changes, and operational status. Users might ask why usage changed, which region is behind, or how results compare across product lines. Start with data that has clear ownership and approved definitions.

Can Users Ask Follow-Up Questions?

Yes. Users should be able to refine a question, narrow a segment, compare time periods, or ask why a number changed. Follow-up questions are where chat becomes a workflow, not just a search box.

How Do Permissions Work in an AI Chat Experience?

The answer should depend on who is asking. A regional manager, executive, analyst, and customer-facing user may ask similar questions but get different results based on permissions. Chat should never become a shortcut around row, column, tenant, or role rules.

How Do We Make AI Chat As Repeatable as Dashboards?

The answer needs to resolve through an independent, unified semantic layer, not the model alone. Simba Intelligence maps questions to approved metrics, permissions, and live data before returning a result. That keeps natural-language questions flexible without losing repeatability.

Can We Choose Which LLM Is Used?

Yes. Simba Intelligence supports model configuration for the data source agent and query agent, so data prep and answering are tuned separately. Current materials reference Google Vertex AI, Azure OpenAI, and AWS Bedrock. Balance speed, accuracy, reliability, and cost by workload.

Can This Run On-Premises or in Controlled Environments?

Yes. Simba Intelligence is designed for on-premises, private cloud, and hybrid deployment, so teams align AI data access with residency, infrastructure, and policy needs. Especially important for regulated environments and any team avoiding cloud-only AI.

What Role Do Analysts and Data Teams Play?

They remain central, defining the metrics, relationships, governance rules, and review processes behind the experience. AI chat should make that work reusable across more questions and users. It should reduce repetitive requests, not remove data expertise.

When Should Users Use Chat Instead of a Dashboard?

Use dashboards for recurring views, shared KPIs, and monitoring. Use AI chat for a follow-up, a different cut of the data, or when the user does not know which dashboard has the answer. The best approach is both, working from governed data.
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