Custom AI agent · Documents & Data
AI Database Query Generation Agent
Turn a business question into a query your team can inspect.
Translate approved business questions into bounded, explainable, read-only database queries with reviewable results.
- Less time translating questions into database syntax
- Safer filters and joins against approved schemas
- Reviewable results with source and query context
Starting at $300 USD/month
Starting price is $300 USD/month for a tailored solution. Your final proposal defines the scope, deliverables and payment terms. Data connections, query volume, semantic modeling, hosting, monitoring, security review and ongoing support are scoped and priced before work begins.
What is an AI database query generation agent?
An AI database query generation agent helps authorized teams retrieve information from approved data sources using plain-language questions and a known schema. It can identify relevant tables, propose filters and joins, explain assumptions, and present the generated query and result context for review. Tranki designs the workflow around read-only access, row and column permissions, query limits, and the people who approve or consume the output. It should make analysis easier to inspect, not become an unrestricted path to sensitive data or unreviewed changes.
What it can help query
Translate questions into query plans
Break a business question into the entities, fields, filters, joins, time range, grouping, and ordering needed to produce a testable retrieval plan.
Use an approved schema
Reference allowed tables, views, relationships, definitions, and field descriptions so the generated query matches the data model instead of guessing at names or meanings.
Explain assumptions and limits
Show how terms such as active, revenue, customer, or recent were interpreted, and flag missing definitions, ambiguous joins, incomplete data, or possible duplication.
Return reviewable results
Present the query, source context, row limits, freshness information, and relevant caveats so an analyst or owner can validate the result before relying on it.
Where it can help
Operations and support questions
Help approved users answer questions about workload, service volume, order states, inventory, or case trends without manually rebuilding every read-only query.
Finance and commercial analysis
Explore scoped revenue, pipeline, retention, or account questions with agreed definitions, date logic, permissions, and a review step for sensitive metrics.
Internal data discovery
Give teams a guided way to find available fields and relationships across a warehouse or reporting database while preserving ownership and access controls.
How it works
A step-by-step workflow your team can review before launch.
Define the safe query boundary
Tranki records the approved data sources, users, tables or views, sensitive fields, query type, row limits, timeout, and human review requirements.
Clarify the question and terms
The agent identifies the requested measure, population, time window, grouping, and business definitions, asking for clarification when a phrase could produce different results.
Build and inspect the query
It selects permitted sources, proposes joins and filters, shows the query plan and assumptions, and flags risks such as fan-out, nulls, stale data, or unbounded scans.
Run, review, and refine
An authorized workflow executes the approved read-only query, returns result context, and lets the owner correct the definition or request a narrower follow-up.
Built around your business
What we configure
- Approved databases, schemas, tables, views, semantic definitions, relationships, and field-level permissions
- Read-only roles, row limits, timeouts, sensitive-field masking, query review, and audit logging
- Business metric definitions, date and timezone rules, terminology, default filters, and result presentation
- Approved BI, warehouse, database, reporting, notebook, or internal application connections
What we need from you
- A documented or discoverable schema with owners, field descriptions, relationships, and business definitions
- Read-only credentials or an approved query service with least-privilege access and sensitive-data rules
- Named users or analysts who can review ambiguous definitions and approve results for their use
- A safe test environment, query limits, logging, and a process for reporting incorrect or unexpected results
Clear boundaries before launch
What it will not do
The agent must not bypass permissions, expose restricted fields, execute data-changing statements, infer sensitive attributes, or treat a plausible result as a verified business truth. It should use approved read-only sources, bounded queries, and explicit definitions. Financial, health, employment, identity, security, and other high-impact analysis requires qualified review and an access policy appropriate to the data.
How success is measured
Track successful question resolution, query review time, execution failures, clarification rate, result accuracy on a reviewed sample, permission violations prevented, slow or unbounded query attempts, stale-data incidents, and analyst corrections. Measure whether the workflow improves access to trustworthy information without increasing data exposure or misleading conclusions.
Frequently asked questions
What does an AI database query generation agent do?
It turns an approved user's question into a query plan and read-only query using the permitted schema, then explains assumptions and returns result context for review. It is not an unrestricted database administrator.
Can it write to or change our database?
The default scope is read-only retrieval. Any action that changes data would require a separately designed, authorized workflow with permissions, safeguards, testing, and human approval.
How does it know which tables and fields to use?
It uses the schema, relationships, descriptions, semantic definitions, and access rules provided or exposed through the approved connection. Missing or ambiguous definitions should be flagged rather than guessed.
Can it query sensitive information?
Only when the user, source, fields, and workflow are explicitly authorized. Row and column permissions, masking, audit logs, limits, and human review can be included in the implementation.
Will it always return the correct business answer?
No system should promise that. The agent can make query logic visible and testable, but data freshness, definitions, joins, and source quality still require review by someone accountable for the metric.
Can it connect to our warehouse or BI tool?
A connection can be included after we review the platform, access model, query service, semantic layer, volume, and required controls. Integrations are not automatically included in the starting price.
What is included in the $300/month starting price?
The starting price covers a scoped custom solution baseline. Your proposal defines the data sources, users, query limits, semantic definitions, security controls, integrations, monitoring, and support included.
