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Best Conversational Analytics Tools

December 19, 2025

Best Conversational Analytics Tools

By Andrey Avtomonov, CTO at Kaelio | 2x founder in AI + Data | ex-CERN, ex-Dataiku · Dec 19th, 2025

Conversational analytics tools transform plain English questions into database queries, enabling business users to explore data without technical skills. Leading platforms like Querio AI connect directly to warehouses such as Snowflake and BigQuery, while Google's Looker uses Gemini for natural language processing. These tools achieve over 95% SQL accuracy and help organizations save 40-60 minutes per user daily.

Key Facts

• Market Growth: The conversational AI market will reach $31.9 billion by 2028, with worldwide GenAI spending hitting $644 billion in 2025

• Performance Standards: Modern platforms achieve 95%+ SQL accuracy with SOC 2 Type II compliance and 99.9% uptime guarantees

• Cost Ranges: Enterprise solutions start at $14,000 annually for Querio AI, while open-source options like Dataherald offer free deployment with engineering resources

• ROI Impact: Organizations report $3.70 return per dollar invested, with analysts saving 20 hours monthly on routine tasks

• Integration Depth: Tools connect to existing warehouses, semantic layers, and BI platforms without data movement, supporting dbt, Looker, and Snowflake environments

Every team depends on data to make decisions, yet getting answers still means Slack threads, tickets, and waiting on analysts. The gap between having data and actually using it continues to frustrate business users across organizations of all sizes.

Large language models and generative AI now enable chatbots and interactive virtual assistants that are smarter, more useful, and more conversational than before. These conversational analytics tools finally make governed data accessible at chat speed, letting anyone ask questions in plain English and get immediate, trustworthy answers.

This guide walks through the leading platforms in 2025, covering BI-centric natural language query tools, enterprise analytics copilots, real-time conversation intelligence for contact centers, and open-source options for developers. You will find evaluation criteria, head-to-head comparisons, and the benchmarks shaping this rapidly evolving category.

Why Do Conversational Analytics Tools Now Matter More Than Ever?

Conversational analytics tools let people explore governed business data by simply asking questions in plain English. Powered by natural-language-to-SQL engines, they translate a prompt into secure queries, return charts or explanations, and learn from every interaction.

Self-service applications for customers and employees have consistently featured poor user experiences that garner disappointing uptake. Traditional BI dashboards required technical skills and left business users dependent on data teams for even simple questions.

The shift is real. Conversational AI solutions have established a strong foothold in mainstream enterprise applications and have become an important part of related digital transformation and CX/EX strategies, according to IDC's research. Organizations are replacing older IVRs and manual systems for customer service, HR fulfillment, IT help desks, and conversational commerce.

Natural Language Querying in BI simplifies data access, empowering users to make quicker, data-driven decisions without technical barriers. The technology transforms everyday language into database queries through three main steps: language parsing, intent recognition, and query generation.

Key takeaway: The combination of LLM advances and enterprise governance requirements has made 2025 the inflection point for conversational analytics adoption.

How Should You Evaluate Conversational Analytics Platforms?

Choosing the right platform requires looking beyond marketing claims. Customer service and support leaders struggle to identify actionable chatbot metrics, minimizing their ability to drive chatbot evolution and expansion, and limiting their ROI.

Here is a practical evaluation checklist:

Accuracy: Does the tool generate correct SQL and return trustworthy answers? Modern tools achieve over 95% accuracy and process queries in seconds.
Governance: Does it respect existing permissions, row-level security, and masking? Enterprise environments need SOC 2 compliance at minimum.
Time-to-insight: Enterprise users report saving 40 to 60 minutes per day and being able to complete new technical tasks such as data analysis and coding.
Goal Completion Rate: How often are users receiving adequate information? This metric tracks the percentage of users who reach the goal the chatbot was designed to accomplish.
Integration depth: Does it connect to your existing warehouses, semantic layers, and BI tools without requiring data movement?
ROI measurement: Can you track cost savings and productivity gains? Look for platforms with documented returns.

Benchmarking chatbot performance metrics at one organization against that of its peers is not effective and can be misleading because chatbot type, design, and complexity vary widely by organization. Focus on internal baselines and improvement over time rather than industry comparisons.

Which BI-Centric NLQ Leaders Stand Out in 2025?

BI-centric natural language query tools sit on top of existing data warehouses and semantic layers, translating plain English into governed SQL without replacing your current infrastructure.

These platforms excel when organizations already have mature data stacks with tools like dbt, Looker, or Snowflake. Moving metric definitions out of the BI layer and into the modeling layer allows data teams to feel confident that different business units work from the same definitions, regardless of their tool of choice.

The semantic layer is becoming table stakes. The dbt Semantic Layer, powered by MetricFlow, simplifies defining and using critical business metrics in the modeling layer. This eliminates duplicate coding and ensures consistency across every downstream tool.

Querio AI

Querio AI targets enterprises needing live data warehouse connectivity. It connects directly to Snowflake and BigQuery, handles real-time queries, and starts at $14,000 annually.

Key capabilities include:

Direct integration with major databases using read-only, encrypted links
SOC 2 Type II compliance with 99.9% uptime guarantee
Context layer for defining table relationships, business metrics, and glossary terms
Unlimited viewer users included in base pricing
Dynamic dashboards turning raw data into visual charts

Querio users report an average return of $3.70 for every $1 invested. The platform meets enterprise security standards including CCPA compliance.

"What used to be weeks, now takes minutes!" noted one user describing the impact on their analytics workflow.

Google Looker Conversational Analytics

Looker's Conversational Analytics is a chat-with-your-data feature powered by Gemini. It empowers users to go beyond static dashboards and ask data-related questions in natural language, even with little or no expertise in business intelligence.

Setup requirements include:

Looker instance on version 25.2 or later
Gemini in Looker enabled for the instance
Users granted the gemini_in_looker permission

The platform supports querying Looker Explores and custom data agents. The Conversational Analytics API uses natural language to answer questions about structured data in BigQuery, Looker, Looker Studio, AlloyDB, and Cloud SQL.

The Code Interpreter translates natural language questions into Python code for complex analysis and visualizations. Data Residency support ensures data-at-rest remains within a single region for all Looker customers.

One limitation: Gemini for Google Cloud products can sometimes produce factually incorrect outputs, so validation is recommended for critical decisions.

What Are the Top End-to-End Enterprise Analytics Copilots?

Enterprise analytics copilots go beyond simple chat interfaces. They handle complex, multi-layered analysis that uncovers hidden relationships, from drilling into metadata to chaining insights together in a single fluid conversation.

These platforms address a real gap. Chat-based AI fails for 78% of queries due to issues like multiple definitions, the need for precise data interpretation, and seamless data integration requirements.

The best enterprise copilots combine conversational interfaces with automated workflows. AI agents seamlessly integrate into existing workflows, automating complex tasks and accelerating outcomes across every department, from sales and marketing to operations and finance.

Security and compliance are non-negotiable. Look for SOC2 Type2 certification, secure encryption in transit and at rest, row-level security, and flexible hosting options including on-premises deployment.

Tellius + Kaiya

Kaiya AI Agents provide a fully orchestrated analytics engine where you can use natural language to automate everything from simple metrics to advanced diagnostics. The platform dynamically selects data sources based on your query's time context.

Core capabilities include:

Session context retention across queries without restating parameters
Natural date/time parsing for time filters
Support for SUM, AVG, MIN, MAX, Top N / Bottom N, and nested groupings
Automatic dataset alignment and time frame management
Insights Agent that identifies key drivers and breaks down variances

Kaiya can distinguish between point-in-time comparisons, continuous growth trends, and event-based deltas. It translates natural language into precise backend logic without requiring Boolean operators.

The platform turns conversations into outcomes through AI-guided exploration, anticipated questions, and triggered automated workflows.

"Automating routine tasks freed up 20 hours a month for each analyst. They can finally focus on strategy, not data wrangling," reported one Tellius customer.

How Do Real-Time Conversation Intelligence Tools Serve Contact Centers?

Contact centers require a different breed of conversational analytics. These tools focus on live customer interactions rather than database queries, providing real-time coaching and experience monitoring.

Cogito, now part of Verint, is the only technology that analyzes over 200 voice signals in tandem with conversational context to improve CX outcomes while calls are happening. The platform continuously monitors customer and employee experiences across 100% of calls.

Key components include:

Coaching Bot: Provides real-time guidance to agents based on business priorities. A telco reduced call duration by 30 seconds while boosting sales using this capability.
CX/EX Scoring Bot: Delivers real-time measurement of customer and employee experience. A leading healthcare plan provider saw a 16% increase in NPS from interventions based on these insights.
Agent Copilot Bots: Automate manual contact center micro-workflows, increasing efficiency and capacity.

The Verint Open Platform leverages unified data and industry-leading AI at its core, helping companies connect the dots across the organization.

Open-Source & Developer-Focused Options

Developers building custom conversational analytics solutions have several open-source frameworks to consider. These tools provide flexibility and cost advantages but require more technical expertise to deploy and maintain.

QueryWeaver is an open-source Text2SQL tool that transforms natural language into SQL using graph-powered schema understanding. It exposes a REST API for managing database schemas and running Text2SQL queries. Licensed under AGPL, the project has gained traction with 244 stars on GitHub.

Dataherald is a natural language-to-SQL engine built for enterprise-level question answering over relational data. It allows setting up an API from your database that answers questions in plain English. With 3.6k stars and 256 forks, it has strong community engagement.

XiYan-SQL represents the state of the art in text-to-SQL accuracy. The framework integrates in-context learning with supervised fine-tuning, achieving 75.63% execution accuracy on the BIRD test and 89.65% on Spider. The XiYanSQL-QwenCoder models come in various sizes to meet different performance requirements.

Open-source options work best when:

You have engineering resources to customize and maintain
Existing commercial tools do not fit your specific use case
You need complete control over the deployment environment
Cost constraints prevent commercial licensing

Benchmarks & Research Shaping the Next Wave

Academic research and industry benchmarks continue pushing text-to-SQL accuracy and market growth. Understanding these developments helps evaluate vendor claims and anticipate where the technology is heading.

The UNITE benchmark tests text-to-SQL generalization across natural language questions, unseen database schemas, and novel SQL query structures. It contains questions from more than 12 domains, 3.9K SQL query patterns, and 29K databases. Compared to Spider, UNITE introduces approximately 120K additional examples and a threefold increase in SQL patterns.

Market projections are bullish. IDC forecasts the overall conversational AI market to reach over $31.9 billion in revenue by 2028 with a CAGR of 40.4%. Improvements in AI have resulted in measurable ROI from conversational AI software services.

Generative AI spending continues accelerating. Worldwide GenAI spending is expected to total $644 billion in 2025, an increase of 76.4% from 2024, according to Gartner.

Privacy concerns are also advancing the field. Differential privacy, a mathematical framework that quantifies privacy loss when data appears in a dataset, is gaining traction. NIST's Special Publication 800-226 provides guidelines for evaluating differential privacy guarantees, helping practitioners implement privacy-preserving analytics.

Choosing the Right Conversational Analytics Stack

The conversational analytics landscape in 2025 spans several distinct categories, each suited to different organizational needs.

For BI-centric natural language querying, platforms like Querio AI and Looker Conversational Analytics provide governed SQL generation on top of existing warehouses. These work best when you have mature data infrastructure and need to democratize access without compromising security.

Enterprise analytics copilots like Tellius with Kaiya handle complex, multi-layered analysis across departments. They excel when organizations need more than simple queries, requiring automated workflows and cross-functional insights.

Contact centers benefit from real-time conversation intelligence tools like Verint's Cogito, which provide live coaching and experience scoring rather than database analytics.

Developers building custom solutions can leverage open-source frameworks like Dataherald and XiYan-SQL, though these require engineering resources to deploy and maintain.

LLMs and genAI enable faster application delivery versus traditional methods, but these new technologies carry risks of hallucinations, jailbreaks, and bias. Every platform requires validation workflows and human oversight for critical decisions.

Kaelio approaches this space differently, acting as a natural language interface that sits on top of your existing data stack rather than replacing it. By working with your current warehouses, semantic layers, and governance systems, Kaelio learns from how people ask questions and helps data teams improve definitions and documentation over time. For organizations prioritizing correctness, transparency, and alignment with existing data governance, this coordination layer approach offers a path forward without ripping and replacing current infrastructure.

About the Author

Former AI CTO with 15+ years of experience in data engineering and analytics.

Frequently Asked Questions

What are conversational analytics tools?

Conversational analytics tools allow users to explore business data by asking questions in plain English. They use natural-language-to-SQL engines to translate prompts into secure queries, providing immediate and trustworthy answers.

Why are conversational analytics tools important in 2025?

In 2025, conversational analytics tools are crucial as they bridge the gap between data availability and usability, enabling real-time insights and decision-making without technical barriers. They integrate with existing data infrastructures, enhancing data governance and accessibility.

How do conversational analytics tools improve business operations?

These tools streamline data access, allowing users to make data-driven decisions quickly. They reduce dependency on data teams for simple queries, improve time-to-insight, and support enterprise governance requirements, ultimately enhancing productivity and decision-making.

What should you consider when choosing a conversational analytics platform?

Key considerations include accuracy, governance, integration depth, time-to-insight, and ROI measurement. It's important to ensure the platform respects existing data permissions and integrates seamlessly with your current data stack.

How does Kaelio differentiate itself in the conversational analytics space?

Kaelio acts as a natural language interface that integrates with existing data stacks, enhancing data governance and transparency. It learns from user interactions to improve data definitions and documentation, offering a non-disruptive solution for organizations prioritizing data correctness and alignment.