Conversational Analytics in Embedded Analytics

ChatGPT changed how people expect to interact with information. You type a question and get a clear answer in seconds. This simple pattern now shapes expectations for every part of every app, and embedded analytics follows suit. Users want direct answers to their data questions without extra steps. Conversational analytics delivers this experience. 

Reporting still helps users explore data, yet many want a faster way to reach insights they already have in mind. They want to form a question and get the chart that matches it, just as they get ideas from ChatGPT. This shift affects every industry because it removes friction for users with different technical skill levels. As a result, many SaaS product teams now look at conversational analytics as a core part of their future analytics layer. 

73% of tech leaders plan to expand their use of AI next year, showing how quickly expectations are changing. Natural language makes analytics easier to use and expands access to insight beyond power users. It also changes how customers judge product value, because faster answers lead to faster decisions. 

The idea sounds simple, but most conversational analytics software fails inside real SaaS products. Many tools rely on external AI services, creating new privacy, control, and data exposure issues. These risks are hard to accept in customer-facing analytics layers. This is why teams need to understand how conversational analytics work at the architectural level before adding it to their roadmap. 

What is Conversational Analytics 

Conversational analytics lets users ask questions about data using natural language and receive answers in the form of charts, metrics, or summaries. Instead of building reports or navigating editors, users describe what they want to see. The system translates that request into a visualization or insight using the same logic that powers the rest of the analytics layer. 

A simple example looks like this. A user opens a SaaS application and asks, “Show monthly churn by plan.” The product responds with a chart that follows the same filters, permissions, and data rules as every other dashboard in the app. No report building. No schema knowledge. Just a direct answer to a business question. 

This sounds straightforward, but delivering it inside a customer-facing product introduces constraints that most tools are not designed to handle. That is where most gaps appear. 

Why Most Conversational Analytics Tools Do Not Fit SaaS Products 

Many teams see the promise of conversational analytics, but most tools do not match the needs of real SaaS products. They create new risks, break core product rules, or force teams to depend on external systems they cannot control. These issues appear early in implementation and grow fast as the user base expands. 
 

External AI Services Push Data Outside Your Environment 

Most conversational analytics software depends on cloud-hosted models processing user prompts and metadata outside your environment. This breaks the security posture of SaaS platforms that handle sensitive records. 51% of tech leaders identify security as their top development challenge for 2025. Routing analytics queries through third-party models increases this risk and creates new compliance concerns. 

Generic Models Cannot Follow Product Security Rules 

External models can’t apply your row-level security or tenancy logic. They do not know which customer, role, or group should see which fields. A user may ask a simple question, yet the model may pull data that violates your internal rules. This breaks trust and increases the support burden on your team. 

Conversational Analytics Must Match Product UX 

Most generic tools provide a chat window that doesn’t align with your product. They introduce layouts, elements, and flows that feel disconnected from the rest of the app. This weakens the experience and forces teams to maintain inconsistent UI layers. SaaS products need a conversational workflow that fits into their existing embedded analytics experience. 

SaaS Teams Lose Control Over AI Behavior and Output 

Generic conversational analytics software often creates unpredictable results. It may return irrelevant fields, invent metrics, or build charts that don’t follow your product logic. This makes the feature unreliable and increases the risk of incorrect decisions. Product teams need predictability, especially when analytics shape business outcomes. 

These challenges show why conversational analytics must run inside your own environment.  

Why Conversational Analytics Must Run Inside Your Environment 

SaaS leaders need a model that supports conversational analytics without weakening security or losing control of the product. Many tools fail because they add another layer outside your environment. A better model keeps everything close to your data, your rules, and your users.  

Keep All Data and Logic Inside Your Own Environment 

A secure approach keeps all processing inside your network. Your application sends a request to an internal service. That service communicates with a language model using your own credentials. Your raw data never goes to a vendor’s server. This turns conversational AI analytics from a risky add-on into a controlled workflow. It also helps teams meet strict governance rules without slowing users down. 

Use Your Existing Data Model Instead of Letting the Model Write SQL 

Many conversational analytics software tools create SQL directly from user prompts. This is risky. It bypasses security rules and often produces unpredictable results. A stronger approach generates dashboard definitions or visualization configurations instead of raw SQL. The request then moves through your existing authentication, row-level security, and filtering logic. This keeps access rules consistent and predictable across all queries. 

Treat Natural Language as an Intent Layer, Not Just a Chat Window 

A modern system treats natural language as a flexible command layer. Users can ask to create dashboards, add widgets, apply filters, or summarize visuals. These conversational analytics examples show how intent drives the workflow. A question asked in a chat panel, search bar, or context menu triggers the same internal logic. This creates a consistent experience across your product fitting cleanly with your existing AI-powered analytics workflows.  

Make AI Easy to Trust with Testing, Scoring, and Guardrails 

AI must behave predictably when used within a SaaS product. A strong system includes relevance scoring, controlled prompts, and clear output rules. It also allows teams to test different models against known dashboards to evaluate accuracy and speed. 

A model built around these principles gives teams full control over conversational analytics. The next step is to understand how security fits into this approach and why it shapes every design choice. 

The Security Layer: Keeping AI, Data, and Analytics Under Your Control 

Security becomes the biggest risk when teams move from AI demos to production conversational analytics. Users want quick answers, but customers expect strict control over their data. Many tools ignore this gap. They generate SQL through an external model, send it outside your environment, and hope the vendor keeps everything safe. 

Reveal takes a different route. It keeps the entire conversational analytics workflow inside your product’s security perimeter. No raw data leaves your environment, and the AI layer respects every rule you already enforce. 

Keep AI Next to Your Data, Not in a Vendor’s Cloud 

Most conversational analytics software sends user prompts to a cloud model which then writes SQL. This breaks the security chain, because: 

• The model doesn’t know your user permissions. 

• It can’t enforce row-level security. 

• It may expose fields or patterns you would never disclose. 

Reveal avoids this pattern completely. The AI runs through your own cloud account or on your own infrastructure, and your app remains the only system that talks to the model. The model receives metadata, never raw data. This keeps ownership and control where it belongs: with your team. 

Generate Dashboards Through Your Existing Security Model 

Reveal never lets AI generate SQL. Instead, it uses its SDK DOM to turn natural language into a dashboard JSON definition. That definition moves through the same server lifecycle used for every dashboard in your product. This ensures all existing controls apply: 

• Authentication 

• Data source items 

• Row-level security 

• Filters 

• User context 

If a user can’t see a metric in a normal dashboard, they can’t see it through conversational analytics either. This is one of the core reasons teams choose Reveal for secure conversational AI inside embedded analytics. 

Add a Second Security Layer for AI Access 

Reveal adds another layer of control on top of your existing security model. You decide which datasets AI can work with and which remain off-limits. This includes: 

• Table and view whitelisting. Limit AI to specific datasets within each data source. 

• Metadata overrides. Map domain terms like “job ticket” or “case code” to underlying fields without changing the schema. 

• Intent-level controls. Allow dashboard creation, editing, summaries, or analysis only where it makes sense. 

These options create a predictable and safe environment for conversational analytics. You get the flexibility of natural language without giving power to a model that doesn’t understand your product, your rules, or your compliance needs. 

How Conversational Analytics Actually Works Inside a Product 

Seeing conversational analytics in action helps you understand what the feature can actually do inside a live product. The video below walks through the Reveal workflow step by step, showing how natural-language queries turn into secure dashboards, summaries, and updates in real time. It shows the exact behaviors your users can expect inside an embedded environment. 

Where Conversational Analytics Fits Inside Embedded Analytics 

Most users open your app with a purpose. They want a quick answer or a clear view of what changed since yesterday. Dashboards help, but they do not cover every question people ask during their workday. This is where conversational analytics becomes useful. It fills the space between exploration and action by giving users a direct way to ask for what they need. 

Conversational analytics fits well into existing workflows because the data behind your dashboards stays the same. It simply gives users a faster way to reach it by working across your connected data sources without forcing them to understand how those sources are structured. 

Faster Answers Inside Existing Dashboards 

Users often open a dashboard to check trends and key metrics. They see enough to know what changed, but they still need one more detail. This is when conversational analytics becomes the quickest option. They can ask for a breakdown by country, a comparison with last month, or a list of top performers without building a new view. 

A short query is often easier than clicking through menus or switching dashboards. The user keeps focus on the task and avoids the friction of deeper exploration. 

Helping Non-Technical Users Build What They Need 

Many users know the outcome they want but do not know how to assemble the dashboard that delivers it. They do not understand tables, joins, fields, or aggregates. Conversational analytics removes that barrier. A simple question can return a chart, table, or widget that matches what they had in mind. 

This helps users who rely on the app every day but are not comfortable with the full editor. It also reduces pressure on your support and product teams. When users can ask a question in plain language, they do not need help navigating the schema. 

Examples include: 

• Managers who want a quick regional breakdown. 

• Operators who need to check volume or exceptions. 

• Analysts who want a starting point before refining a dashboard. 

Reducing the Friction Between Data and Action 

Users often need a small change rather than a new dashboard. They may want to add a widget, adjust a filter, or generate a simple report. Conversational analytics helps them do this without interrupting their workflow. 

It fits naturally into the flow of embedded analytics, allowing users to take action while keeping their work’s context in front of them. They do not need to leave the screen, open a builder, or search through menus. 

This makes the product feel faster and more supportive. Once users experience this level of convenience, they expect it everywhere. 

Reveal’s Approach: Flexible, Secure Conversational Analytics for SaaS 

Most teams reach the limits of generic conversational analytics software once they try to ship it inside a real product. They need fast responses, accurate insights, and a secure architecture that respects their existing rules. Reveal handles these needs by keeping AI inside your environment, giving developers full control, and delivering a predictable experience for every user. 

Reveal doesn’t touch your data. Your app communicates with your chosen model through your own cloud account or infrastructure. Nothing leaves your environment, and the AI layer works inside the same controls you already enforce through your embedded dashboards. 

AI That Runs Inside Your Environment 

Reveal keeps both your data and your AI workflow under your control. The system uses your authentication, your cloud account, and your governance model. This avoids the security gaps that appear when analytics vendors send prompts and queries to external services. 

Key advantages include: 

• You choose the model: Azure OpenAI, local small language models, or other providers. 

• AI receives metadata, not raw data. 

• Reveal never sees your queries, datasets, or results. 

• All AI features are opt-in and fully configurable. 

• Predictable performance because everything runs next to your data. 

This gives your team a level of control that most AI-powered analytics platforms can’t match. 

A Secure Architecture for Real Products 

Reveal uses its SDK DOM to convert natural-language requests into safe dashboard definitions instead of SQL. Every result moves through the Reveal server lifecycle, so your existing rules enforce themselves at every step. 

You get: 

• Row-level security for every query. 

• All filters and user context are applied automatically. 

• Control over which tables and views are available to AI. 

• Metadata overrides for domain terms used by your customers. 

• Safe execution because the model never writes SQL. 

This removes the most common failure points in conversational analytics and keeps your compliance posture intact. 

Designed for Developers and Product Teams 

Reveal works as a true embedded solution, not a disconnected chatbot. You can place AI features anywhere in your UX and control the entire experience through the SDK. This gives you the freedom to fit conversational analytics into your product on your terms. 

Reveal supports product teams with: 

• A full API surface for chat, summaries, dashboard edits, and analysis. 

• Control over each AI intent based on the workflow you want to enable. 

• A clean path for adding insights into any screen in your app. 

• A fixed yearly cost that does not grow with your users. 

• A secure and scalable base for AI-powered analytics. 

This is how teams improve adoption, increase customer retention with Embedded Analytics, and accelerate their ability to reduce time-to-market for new features. It also supports long-term growth through product analytics revenue and data monetization opportunities. 

Reveal is built for products that rely on data. It keeps your security model intact, supports your team with predictable tooling, and delivers a conversational analytics experience users can trust.