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Maximize ROI from Your Data and BI initiatives with Proactive Self-Service Analytics

Your BI tools are just the beginning. Unlock the full value of your data warehouse while maintaining control, security, and accuracy.Ā Ā 

Boost ROI on Data & BI Investment

Top 3 benefits for Data Teams:

Reduce Report and Dashboard Backlogs:

Let business users self-serve data using natural language. Free your team from constant ad-hoc requests so they can focus on strategic, high-impact projects.

Enterprise-Grade Security:

Deploy Chata.ai within your enterprise environment, ensuring full control over data privacy, governance, and compliance—keeping your proprietary data secure.

Zero Hallucinations:

Powered by proprietary technology, Chata.ai trains custom language models exclusively on your proprietary data, guaranteeing precise results with zero hallucinations.
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is SOC Type II Compliant

ā€œWith Chata.ai, we cut ad-hoc requests by 70%, allowing our data team to focus on strategic projects while delivering insights faster to business teams.ā€Ā 

 

– Name, Company

How we differentiate from BI tools and how we differentiate from LLMs:

Custom Language Models:

Train deterministic AI models tailored to your unique data structure for accurate, hallucination-free results.Ā 

Cost-Efficient Infrastructure:

Scale effortlessly with CPU inference, reducing costs compared to GPU-based solutions.

Want to learn more?

Conversational AI: The Technology Behind AutoQL

This white paper helps users understand the value of the technology we developed as we built AutoQL into a powerful conversational AI solution for database access.

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How Data Teams Utilize Chata.ai

Alleviate analyst data requests with easy-to-use self-service analytics. When business users can access the information in the data warehouse, they no longer need to disrupt data teams for information to make accurate decisions.

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AutoQL Pilot Process

The goal of the Pilot stage is to validate that Chata.ai has developed an effective, viable language model for the customer, as demonstrated through successful end-users experiences with the model.

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