Let your entire team ask questions and get reliable answers from data — in plain language.
AI-powered conversational analytics that delivers accurate insights to everyone, while dramatically reducing ad-hoc query load on your data team. Built by senior experts who understand why most conversational analytics tools fail.
See conversational analytics done right
We build semantic clarity, NL2SQL guardrails, and evaluation so teams trust every answer.
Book a free call with our expertBusiness users cannot self-serve
Every question needs a ticket or Slack thread; decisions slow down.
Analysts overwhelmed by repetitive queries
Senior talent spends time repeating the same answers.
AI tools hallucinate or break
Generic NL2SQL misinterprets metrics and produces wrong SQL.
No semantic clarity
Without definitions and guardrails, AI answers are untrustworthy.
How conversational analytics delivered real business outcomes
An anonymized case study showing how AI-ready foundations unlocked instant answers, reduced costs, and improved decision velocity across the business.
"Most conversational analytics tools fail because they are built on shaky foundations. Without clear metrics, semantic layers, and data quality checks, even the best LLMs will confidently give you the wrong answer. Conversational Analytics gives your entire team reliable, self-service access to insights — while reducing the burden on your data team by 40–60%."
Radek Duha, Head of Data
The problems you are likely facing
Reliable conversational analytics needs semantic clarity, guardrails, and evaluation. Here is what blocks it most often.
Questions that should take 30 seconds take days because every request goes through the data team.
Senior analysts spend 40–60% of their time answering the same questions instead of strategic work.
NL2SQL and chatbots hallucinate numbers, misuse joins, or produce queries that break.
Without a semantic layer, “revenue” or “active user” means different things and AI cannot reason correctly.
Non-technical teams do not trust AI answers and avoid data-driven decisions.
Our Conversational Analytics process
Six senior-led steps to deliver accurate, explainable, and trusted answers.
Assess your environment, key metrics, and the questions teams ask most to map high-impact use cases.
Build or enhance the semantic layer with clear business definitions and relationships.
Implement and tune conversational analytics tooling for your stack so answers are accurate and safe.
Automated testing validates AI-generated queries and answers to prevent silent degradation.
Train business users to ask effectively and technical teams to operate and maintain the system.
Track accuracy, satisfaction, and performance to keep answers reliable as data and definitions evolve.
What you will get from Conversational Analytics
Self-service insights, trusted answers, and a data team focused on strategy instead of tickets.
Self-service insights for the company
Business teams get fast, accurate answers without waiting for analysts or writing SQL.
40–60% reduction in ad-hoc query load
Analysts focus on strategic work instead of repetitive requests.
Reliable AI answers teams trust
Semantic clarity and guardrails remove hallucinations and broken SQL.
Faster decision-making
Insights that took days now take seconds, accelerating product, marketing, sales, and ops.
Lower operational costs
Reduced analyst workload and optimized queries cut time and infrastructure spend.
Democratized data access without chaos
Non-technical users explore data while governance, security, and quality stay intact.
Who gets the most value
Where conversational analytics delivers the fastest impact.
Companies where data teams are overwhelmed by ad-hoc requests
Analysts spend more time answering repetitive questions than solving real problems.
Business teams that need faster access to insights
Product, marketing, sales, and ops want self-service analytics without SQL or tickets.
Organizations with inconsistent or unclear metrics
Different definitions of “revenue” or “active user” make AI answers unreliable.
Teams ready to adopt conversational analytics the right way
Those who know AI tools only work with solid data foundations and semantic layers.
Data leaders scaling impact without scaling headcount
Heads of Data, CPOs, and CTOs who want to democratize insights while keeping teams strategic.
FAQ
Answers before we start
Do we need perfect data infrastructure before implementing conversational analytics?+
No. You need clear definitions and reasonable quality. We start with an audit to identify gaps and build the foundations you need.
How long does a Conversational Analytics project take?+
Most implementations take 6–10 weeks depending on scope. The goal is fast deployment with reliable accuracy.
What tools or platforms do you use?+
We are platform-agnostic: custom NL2SQL engines, Metabase Copilot, or AI-native solutions based on your needs. Accuracy and maintainability drive the choice.
How do you ensure AI answers are correct?+
We build evaluation frameworks that test query accuracy, validate metric logic, and monitor hallucinations continuously. Every answer is traceable.
What if our business users don't trust AI-generated answers?+
We focus on transparency, semantic clarity, and explainability. Users see SQL and reasoning so they can validate and build trust.
Can this work with multiple data sources or warehouses?+
Yes. We design solutions across Snowflake, BigQuery, Postgres, or hybrids as long as a clear semantic layer connects them.
Next step
Speak directly with Radek Duha
A short expert call to evaluate your data environment and whether Conversational Analytics is the right move.
Clear guidance. Senior expertise. No sales talk.
