01
Ask naturally
Describe the question in the language your team already uses.
Analytics that starts with a question
Ask your data. Get answers.
QueryMytext helps teams explore business data with natural language, then return useful analytics, tables, KPIs, and visualizations.
Built for teams that want clearer answers from the data they already manage.
Analytics workspace
Ask a question
Which product category grew fastest this quarter?
Quarterly growth
+24.8%
Result format
ChartSelected
KPIAvailable
TableAvailable
Product overview
A question-to-insight workflow
QueryMytext is being shaped as an analytics product, not a generic chatbot: questions are interpreted in context, data work happens through controlled paths, and answers are presented as usable analysis.
02
Query with context
Route the request to deterministic analytics or model-assisted reasoning.
03
Explore the result
Use a chart, KPI, or table to continue the conversation with the data.
Product direction
Useful analytics without unnecessary complexity
Core capabilities are designed to make data exploration more direct while keeping tenant boundaries, deterministic operations, and clear result formats in view.
Natural-language analytics
Frame a business question in plain language and receive structured ways to explore the answer.
NL-to-SQL direction
A tenant-aware path for turning relevant questions into controlled data queries.
Charts, KPIs, and tables
Present results in useful formats instead of a wall of generated text.
Multi-tenant by design
Separate tenant configuration and access boundaries support a focused workspace for each organization.
Reusable insight direction
Query history and repeatable analysis are part of the product direction as the workspace evolves.
Efficient AI usage
Route deterministic work away from LLMs when it does not need model reasoning.
Cost-aware AI architecture
Not every analytical operation needs an LLM
QueryMytext is designed to route work to the right tool: deterministic analytics where possible, model-assisted NL-to-SQL and reasoning where it adds value, and deterministic visualization for a clear result.
User question
Natural language
Query router
Deterministic analytics
Predictable calculations
LLM-assisted reasoning
Contextual query interpretation
Structured result
Chart · KPI · table
✓ Reduced token usage
✓ Faster paths for deterministic work
✓ More predictable visualizations
How it works
A deliberate path from data connection to exploration
Current and future data-source capabilities are presented transparently: databases are central today, while file-oriented sources such as Excel and CSV are part of the direction.
- 01
Connect data
Tenant data configuration is kept scoped to the organization.
- 02
Ask naturally
Start with the business question, rather than a query editor.
- 03
Query securely
Apply authorization and data boundaries before analysis.
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Explore insights
Continue with a result designed for decisions, not just text.
Use cases
Built around the questions teams already ask
QueryMytext can support practical data exploration across functions without inventing a new reporting language for every audience.
Sales analytics
Explore pipeline movement, regional performance, and the questions behind a forecast.
Operations
Spot bottlenecks, throughput changes, and recurring exceptions in day-to-day workflows.
Customer analytics
Investigate engagement, retention signals, and support patterns with a shared language.
Business reporting
Move from recurring manual questions toward explainable KPIs and structured views.
Security principles
Designed with tenant isolation and least privilege in mind
QueryMytext is a multi-tenant system. Its public product direction emphasizes boundaries that protect tenant configuration and keep access rooted in validated context.
↗Tenant-scoped authorization
↗Role-aware access controls
↗Isolated tenant configuration
↗Encrypted stored credentials
↗Read-only data access recommendation
↗Secure invitation and password-reset flows
↗Audit-oriented architecture
Engineering architecture
More than a chatbot interface
The system is intentionally separated into public, tenant, and platform surfaces, with a backend responsible for tenant-aware workflows and data-provider boundaries.
Public website
Tenant app
Super Admin
FastAPI backend
Platform database
Tenant data sources
Analytics & LLM providers
Request access
Bring a real data question.
Tell us what your team would like to understand. This public form is a frontend-only demonstration while the public request API is being prepared.