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Products โ€บ Intelligence โ€บ ConverseDataIQ ยท Chat with Data
๐Ÿ’ฌ ConverseDataIQ โ€” Chat with Data Plain English Real-Time ยท Trusted

Just ask your data. Get the answer.

No ticket. No SQL. No waiting. Real-time. Right from the source.

ConverseDataIQ is the conversational front-door to your data โ€” for everyone, not just analysts. Ask in plain English. Get an answer you can trust, with the chart, the table, the insight, the recommendation, and the data story behind it. Conversations stay anchored to the asset, save automatically, and pick up where you left off.

Built for the 80% of business users who've been locked out of data because the answer always lived behind a ticket. With ConverseDataIQ, the answer lives in the conversation. Wherever you need it. Whenever you need it.

See a Conversation โ†’ Request Demo โ†“ Download Brochure PDF
๐Ÿ’ฌ
ConverseDataIQ
Chat with your data using AI
Active
๐Ÿ“„
Q4 Sales by Region
CSV ACTIVE
conversation: Revenue Review
"Which region beat plan in Q4 โ€” and by how much?"
๐Ÿค–
EMEA beat plan by $4.2M (+18%) โ€” the only region above target. North America finished โˆ’6% short despite a strong December.
Q4 Revenue vs Plan ยท 4 regions
PLAN NA โˆ’6% EMEA +18% APAC โˆ’1% LATAM +4%
๐Ÿ’ก Insight โœ… Recommendation ๐Ÿ” Lineage ยท 4 sources
โœ๏ธ Ask a follow-up about your dataโ€ฆ โžค
ConverseDataIQ in production
Plain English
Zero SQL ยท Zero Tickets
Real-Time
Live from sources
Trusted
Lineage ยท Governance
Insight + Story
Not just numbers
Private VPC
Your data stays yours
Why ConverseDataIQ Exists

Most data dies in a queue nobody reads.

Eighty percent of business users are locked out of data โ€” not because the data isn't there, but because the path to the answer runs through a ticket, a sprint, a meeting, and a "we'll get back to you on Tuesday." By Tuesday, the question has changed.

ConverseDataIQ collapses the queue. The user asks. The system reads the live source. The answer comes back in seconds โ€” with the chart, the table, the insight, the recommendation, and the data story. Then the next question, and the one after that. The conversation is the workspace.

Ask. Get the answer. Trust it. Decide. Move on.

The old way
A simple question becomes a three-week project.
01 ยท TICKET
User opens a request. Joins the queue. Slack thread. Spec doc. Three back-and-forth clarifications. The analyst still doesn't know which "customer" you mean.
02 ยท WAIT
Days pass. Analyst writes SQL against three systems. Joins are wrong twice. Numbers don't tie to last quarter's report. Re-check. Re-run.
03 ยท DEAD ANSWER
A static screenshot lands in your inbox. The number's already stale. You have a follow-up question. Back to ticket #1.
What You Can Do

Four ways to talk to your data.

Every conversation is grounded in a real data asset โ€” a dataset, a query, a blended source, or a published data product. The system reads it live, every time.

๐Ÿ”
Mode 1
Explore
"What's in this asset? What questions can I ask?" The system describes the data, suggests starter questions, and shows you what's possible โ€” before you've typed your first real query.
โ†’ "What can I analyze with this dataset?"
๐Ÿ“Š
Mode 2
Analyze
Ask the question. Get the answer with the right chart auto-chosen, the table behind it, an insight written in plain English, and a recommendation. Not just a number โ€” a story.
โ†’ "Which region beat plan in Q4 โ€” and by how much?"
๐Ÿ”„
Mode 3
Iterate
"Now break that down by product line." Context carries forward. Follow-ups don't restart the conversation โ€” they build on it. Suggested next questions appear under every answer.
โ†’ "Now break this down by product category."
๐Ÿ’พ
Mode 4
Reuse
Conversations save. Re-open one tomorrow and pick up exactly where you left off. Share the conversation with a teammate โ€” they see the same answers, traced to the same sources.
โ†’ Open "Q4 Revenue Review" ยท 12 turns saved
A Conversation, Not a Query

You don't open a report. You just ask.

Three real business questions, three full answers โ€” chart, table, insight, recommendation, and the next question already cued up. Watch it cycle below.

Live Conversation
Revenue Advisor
powered by ConverseDataIQ โ€” the system picks the chart, the table, and the story.
3 questions ยท 1 conversation
Revenue Performance
Customer Health
Service Recovery
๐Ÿ“„ Asset in context Q4 Revenue ยท Customer Health ยท Service Tickets โ€” three datasets, one conversation. Live from source.
๐Ÿค–
Revenue Advisor
Active ยท ConverseDataIQ
Q1 of 3
โœ๏ธ
โžค
How It Works

From question to trusted answer.

Five steps. Seconds, not days. Every step grounded in your live source โ€” no static export, no stale dashboard.

1
๐Ÿ’ฌ
User asks
In plain English. From any device. Anchored to a chosen data asset, or open across many.
โ†’
2
๐Ÿง 
System interprets
The intent is parsed against your semantic layer. "Customer" means what your team means. Joins are governed.
โ†’
3
โšก
Query runs live
Against your warehouse, lake, or DuckDB ADL โ€” directly from source. No copy. No cache. Always current.
โ†’
4
๐Ÿ“Š
Answer composed
Right chart auto-chosen, table behind it, plain-English insight, recommendation, and the data story โ€” all in one card.
โ†’
5
๐Ÿ”„
Context carries
Follow-up keeps the thread. Conversation saves. Pick it up tomorrow. Share it with a teammate. Never start over.
What ConverseDataIQ Does

More than chat. A complete answer.

๐Ÿ—ฃ๏ธ
Plain-English query
Ask the way you'd ask a teammate. Zero SQL, zero menu hunting. The system translates intent to a governed query against your real schema.
  • Natural language end-to-end
  • Resolves ambiguous terms via semantic layer
  • Asks for clarification only when truly needed
๐Ÿ“Š
Auto-visualized answer
The system picks the right chart for the question โ€” bar, line, scatter, heatmap, or just a number. You don't pick. It picks. You just ask.
  • Bar ยท line ยท area ยท pie ยท table ยท KPI tile
  • Smart axis scaling and legend placement
  • Override with "show me as a line chart" anytime
๐Ÿ’ก
Insight + recommendation
Numbers don't decide. Stories do. Every answer comes with a plain-English insight, a recommended action, and the data story behind both.
  • What changed and why it changed
  • Recommended next move with confidence level
  • Supporting evidence inline
๐Ÿ”„
Context-aware follow-ups
"Now break that down by region." "What about last quarter?" The conversation keeps the thread. Suggested next questions appear under every answer โ€” the analyst-in-the-room experience.
  • Multi-turn memory across the conversation
  • Suggested next questions auto-generated
  • One-tap drilldown into any answer
๐Ÿ”Œ
Asset-anchored conversations
Every conversation lives against a chosen data asset โ€” a dataset, a query, a data blend, or a published data product. You always know what you're talking to.
  • Pin a conversation to one asset, or many
  • Conversation history saves automatically
  • Reopen tomorrow โ€” full context preserved
๐Ÿ›ก๏ธ
Trusted by construction
Every answer carries lineage โ€” what sources, what joins, what filters, when last refreshed. RBAC-aware. Won't return rows the user can't see. Audit log per conversation.
  • End-to-end lineage on every answer
  • Row-level security honored automatically
  • Full audit trail per user, per turn
Direct from Source

One conversation. Every source.

ConverseDataIQ doesn't move your data and doesn't store a copy. It reads live from whatever you've already got โ€” warehouse, lake, file asset, or a blended view across multiple systems. The same conversation can span DataLens dashboards, the Analytical Data Lake (ADL), Data Frames, raw queries, and blended datasets โ€” without you choosing which.

The platform handles the routing. You handle the question.

  • Live reads from Snowflake, Databricks, BigQuery, Redshift, Postgres, MotherDuck, DuckDB
  • File assets from S3, GCS, Azure Blob, SharePoint, Box
  • Blended queries across multiple sources via Data Blends
  • Published Data Products from Reeve are first-class conversation targets
  • Zero data copy ยท zero data movement ยท semantic layer governs all of it
๐Ÿฆ†
DuckDB / ADL
native
โ„๏ธ
Snowflake
cloud DW
โšก
Databricks
lakehouse
๐ŸŸฆ
BigQuery
cloud DW
๐ŸŸฅ
Redshift
cloud DW
๐Ÿ˜
Postgres
OLTP ยท OLAP
๐Ÿ“„
CSV ยท Parquet
file assets
๐Ÿ”—
Data Blends
cross-source
๐Ÿ“ฆ
Data Products
via Reeve
Use Cases

Where ConverseDataIQ earns its keep.

๐Ÿ‘ค
Self-service for business users
Operations ยท Finance ยท Marketing ยท Sales
"Which region beat plan in Q4?" "What's our top-10 customer health?" "Why did support tickets spike last week?" Business users get the answer in seconds โ€” without filing a ticket, without learning SQL.
โœ“ Decisions in minutes, not the next sprint cycle
โšก
Faster exploration for analysts
Data analysts ยท BI teams
Hypothesis check before writing the long-form notebook. Validate "is the pattern even there?" in three turns of conversation, instead of two hours of SQL. The discovery phase collapses to minutes.
โœ“ Hypotheses tested same-day, not same-week
๐Ÿ“ˆ
Executive decision support
CEO ยท CFO ยท Board prep
Six executive questions across six domains in fifteen minutes. The whole pre-board prep โ€” fund performance, budget vs actuals, workforce readiness, risk โ€” answered in one sitting. The dashboard you don't have to build.
โœ“ Board-ready answers without a board-prep team
๐Ÿ”—
Cross-dataset analysis
Anyone needing data from 2+ systems
"Compare Salesforce pipeline against Netsuite billings against Zendesk satisfaction." Three systems, one conversation. ConverseDataIQ reads each live, blends them in-flight, and returns a single answer. No export-to-Excel reconciliation.
โœ“ Cross-system answers in one query, not three meetings
๐Ÿ›ก๏ธ
Compliance & audit insights
Risk ยท Audit ยท Compliance teams
"Show me failed login attempts by region last 90 days." "Which entitlements were modified outside change-window?" Compliance teams get answers from security and governance data without an analyst translation step.
โœ“ Audit answers without a 3-day SQL detour
๐Ÿ”„
Iterative analysis
Anyone who has a follow-up
The most underrated use case. The first answer is rarely the final one. ConverseDataIQ keeps the thread โ€” "now break this down by product," "exclude trial accounts," "what about last year." The conversation builds the answer.
โœ“ Five turns to the right answer, not five tickets
Why ConverseDataIQ

Not a chatbot bolted onto a dashboard.

ConverseDataIQ is the conversational layer of a complete data platform โ€” grounded in your semantic layer, your governance, and your live sources.

Semantic-layer grounded
"Customer" means what your team means. The semantic layer disambiguates entities, metrics, and joins โ€” so the answer is right the first time, not after three clarifications.
Live, never cached
Every answer comes from the live source. No stale snapshot. No yesterday's number. If the data changed five minutes ago, the answer reflects it.
Insight + recommendation, not just the chart
A bar chart isn't a decision. ConverseDataIQ delivers the plain-English insight and a recommended next move โ€” every time, on every answer.
Asset-anchored conversations
Every conversation is pinned to one or more data assets. Conversations save, share, and reopen with full context. The conversation is the workspace.
Lineage on every answer
What sources. What joins. What filters. When last refreshed. Click to see the trace. The auditor and the analyst see the same picture.
RBAC + audit by default
Won't return a row the user can't see. Full audit log per conversation, per turn. Private VPC deployment available โ€” your data never trains anyone's model.
ConverseDataIQ + AI Data Analyst

The Analyst chooses the answer. ConverseDataIQ delivers it.

ConverseDataIQ is the conversational module. The AI Data Analyst is the agent that decides โ€” for any given question โ€” whether the right answer is a chat reply, a chart from DataLens, a narrative from Narratix, a generated query from ConverseSQL, or a published Data Product from Reeve.

You ask the question. The Analyst routes it. ConverseDataIQ delivers the conversation. Same semantic layer, same governance, same trust โ€” across every module.

Meet the AI Data Analyst โ†’
Open-ended question Data Analyst Agent
๐Ÿ‘ค
"How are we tracking against the FY26 plan?"
Routed to ConverseDataIQ. Reading the FY26 Plan dataset live. Composing answer with revenue trend chart, variance table, insight on Q4 acceleration, and recommendation for FY27 commit framing.

3 follow-up questions queued โ€” break down by product, compare to peers, show the bookings vs revenue gap.

Stop filing tickets. Start asking.

See ConverseDataIQ run against your own data โ€” your warehouse, your lake, your published data products. Plain-English questions. Trusted answers. Insight, recommendation, and a data story on every reply. Thirty-minute demo, live against your sources.

Request a ConverseDataIQ Demo โ†’ Meet the AI Analyst โ†“ Download Brochure PDF
Or email us directly at sales@xaqua.io