QUVI 3.0
Ask, and your data answers
Understands your businessUnifies data and documentsDelivers complete reportsTurns insights into actionUnderstands your businessData Analytics AI Agent
Trusted by enterprises with the most complex data, the only natural-language AI for data analytics
Proven with Korea's largest banks and public institutions, now expanding across industries
Wherever there is data, QUVI is already at work
Three things to check before you adopt
Inaccurate answers, difficult deployment, burdensome upkeep. We solve all three together
Accuracy, proven on the world stage
1st across Snow, Lite and DBT on Spider 2.0, the global NL2SQL benchmark
| Rank | Method | Organization | Score |
|---|---|---|---|
| 1 | QUVI-3 + Gemini-3-pro-preview | DAQUV | 94.15 |
| 2 | TCDataAgent-SQL with Contextual Scaling Engine | Tencent Cloud Big Data | 93.97 |
| 3 | Native mini | usenative.ai | 92.50 |
| 4 | Prism Swarm with Deepthink + Claude-Sonnet-4.5 | Paytm | 90.49 |
| 5 | QUVI-3 + Claude-Opus-4.6 | DAQUV | 86.28 |
| Rank | Method | Organization | Score |
|---|---|---|---|
| 1 | QUVI-2.3 + Claude-Opus-4.5 | DAQUV | 65.81 |
| 2 | EXA-SQL | — | 64.16 |
| 3 | ProSPy + Claude-4.5-Opus | Tencent Data Computing | 60.15 |
| 4 | ReFoRCE + o3 | Hao AI Lab x Snowflake | 55.21 |
| 5 | CoFD-SQL + GPT-5 | Samsung SDS Research | 54.66 |
NL2SQL (Natural Language to SQL) · turning a plain-language question into precise SQL to query your data (= Text2SQL)
Spider 2.0 · the hardest benchmark of its kind, testing how well a model turns complex real-world questions into SQL across enterprise databases with hundreds of tables
Ask, and your data answers
Ask in plain language: QUVI reads your business context and delivers accurate answers and insights as a ready-to-use report
Query, analyze and visualize structured data with NL2SQL
the intent
precise SQL
and insights
a report
Two layers: one understands your business, the other interprets meaning
It understands your company's terminology and business rules, and goes beyond the “what” to uncover the “why”
Which division grew VIP customer revenue the most this month, and why?
Division A posted the highest growth, with VIP customer revenue up 18% month over month. The main drivers were sales of new product A, a 27% rise in new customer acquisition, and growth in the capital region.
Ask follow-up questions and dig deeper with every answer
Beyond simple lookups, insights you can act on immediately
| Rank | Division | VIP revenue | MoM |
|---|---|---|---|
| 1 | Division A | $16.8M | +18% |
| 2 | Division B | $14.1M | +9% |
| 3 | Division C | $12.2M | +5% |
- Rising sales of new product A lifted VIP spend per customer
- A 27% increase in new VIP customers expanded transaction volume
- Growth concentrated in the capital region
| Channel | Repeat rate | Avg. order |
|---|---|---|
| Online | 62% | $2,400 |
| Partner | 54% | $1,900 |
| Offline | 41% | $1,500 |
- 48% of online VIPs use subscription delivery (offline: 12%)
- Customers who used personalized coupons repurchase 1.4x more often
- More customers return within 30 days of their first purchase
Every answer builds into a report tailored to you
Turn data into reports simply by asking, then refine the content and charts through conversation
| Rank | Division | VIP revenue |
|---|---|---|
| 1 | Division A | $16.8M |
| 2 | Division B | $14.1M |
| 3 | Division C | $12.2M |
| Channel | Repeat rate | Avg. order |
|---|---|---|
| Online | 62% | $2,400 |
| Partner | 54% | $1,900 |
| Offline | 41% | $1,500 |
Set a report as a dashboard and a single refresh pulls the latest data again
You choose where to ask
Ask directly in QUVI, from the AI you already use, such as ChatGPT or Claude, via MCP, or from your own system via API
Connects straight to the database you already run
No new database, no data migration. Start in the environment you already have
Simple to deploy, reliable to operate
QUVIBot, your AI engineer, builds the semantic layer, applies changes and fixes errors for you
Even unwritten in-house rules are saved as rules once you explain them in chat.
Semantic-layer updates don't require retraining the model, so every change can be reviewed, applied or rolled back
You choose how to deploy
Pick the scope that fits your in-house team, security requirements and budget
License the software only, and your in-house team builds and runs it
- QUVI software license
- You design the semantic and analytic layers in-house
- Training and documentation for build and operations
DAQUV engineers handle the entire implementation within your private network
- QUVI installed on your internal network
- Integration with in-house databases and an air-gapped LLM
- We build the semantic and analytic layers end to end
Combine QUVI with your product or implementation services and deliver it to your customers
- Connect your own screens via API and AI agents via MCP
- Works with any business system: ERP, MES, portals and more
- Your own UI and branding stay untouched
Not sure which fits? Talk to us about deployment →
Get in Touch
See how QUVI works with your data
02-889-8425
business@daquv.com