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QUVI 3.0

Talk to your data. No SQL needed

Anyone can be a data analyst now

QUVI 3.0/kjuːvi/

Chosen by enterprises handling the most complex data — the only NL2SQL solution

First validated in the most sensitive financial data environments, now expanding across industries

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Wherever there is data, QUVI is being used See customer stories →

Two barriers holding back adoption

Either the accuracy falls short, or deployment is too difficult
QUVI 3.0 solves both at once

  • Accuracy Standard AI doesn't know your business.
    Without your terminology, rules, and data structure, it keeps getting answers wrong.
  • Deployment Complexity Setting up an NL2SQL agent takes dedicated engineers and months of work.
    And after launch, maintaining accuracy as business rules change is a never-ending job.

QUVI 3.0's accuracy is world-recognized

Ranked #1 overall on the Spider 2.0 NL2SQL benchmark Swept all categories: Snow · Lite · DBT

RankMethodOrganizationScore
1QUVI-3 + Gemini-3-pro-previewDAQUV94.15
2TCDataAgent-SQL with Contextual Scaling EngineTencent Cloud Big Data93.97
3Native miniusenative.ai92.50
4Prism Swarm with Deepthink + Claude-Sonnet-4.5Paytm90.49
5QUVI-3 + Claude-Opus-4.6DAQUV86.28
RankMethodOrganizationScore
1QUVI-2.3 + Claude-Opus-4.5DAQUV65.81
2EXA-SQL—64.16
3ProSPy + Claude-4.5-OpusTencent Data Computing60.15
4ReFoRCE + o3Hao AI Lab x Snowflake55.21
5CoFD-SQL + GPT-5Samsung SDS Research54.66

The secret to accuracy: a 3-tier architecture

Three components work in synergy to achieve global #1

1
Semantic LayerThe data interpretation foundation for understanding each company's unique business logic
2
Prompt EngineeringCutting-edge context engineering techniques to accurately capture the intent of questions
3
SQL Generation EngineIn-house rule-based engine that compensates for AI uncertainty, ensuring reliability and accuracy

Why a Semantic Layer matters

Three hidden ambiguities in a single question

"How did my team perform this month?"

My teamDepartment? Division? Unit?
Which team is unclear
PerformRevenue? Profit? Bookings?
Which metric is ambiguous
This month1st to today? To end of month?
Time range is undefined

QUVI never guesses

It understands context and answers precisely, based on definitions in the semantic layer

QUVI Thinking...
"My team"Check current user John Smith's departmentSales Team 2
"Perform"Maps to 3 definitions of 'performance' in the semantic layer for SalesDefault for Sales : 'sum of payment-confirmed revenue'
"This month"Since "performance" was asked, use confirmed data, not forecasts1st of current month ~ today
'Total payment-confirmed revenue for Sales Team 2 this month'

We build the Semantic Layer so you don't have to

Meet QUVIBot — your 24/7 AI engineer

  • Auto Test Automatically runs registered test questions
  • Root Cause Analysis Identifies errors and derives improvement directions
  • Re-verification Re-tests after modifications to ensure quality
  • Auto Fix Self-corrects the semantic layer and prompts

Easy to deploy. Easier to maintain.

Anyone can work with data like an expert

"Building a semantic layer takes forever"→QUVIBot generates and updates it automatically
"Accuracy is off — where do I even start?"→QUVIBot finds the cause and fixes it on its own
"Every fix breaks something else"→Built-in version control lets you roll back safely
"Schema changed — do we start over?"→Just add tests. QUVIBot handles the rest

QUVI adapts to your environment

Security without worry, configuration without limits

  • Data stays on-premise Run open-source models on your own servers
    No data sent to external APIs
  • Choose your tech freely Competitors force specific LLM·cloud·DB combinations
    QUVI lets you freely mix and match
  • Keep your existing DB Connect directly to your existing database
    with zero data migration
  • Pass security audits JWT + API Key dual authentication,
    with per-user access control

Get in Touch

See how QUVI works with your data

02-889-8425

business@daquv.com

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