Let your business teams answer their own data questions in plain English, without filing a ticket or waiting on a developer. Natural Language to SQL
Schema-aware query generation with read-only validation and audit trails built in.
Database Query Automation is a natural language to SQL solution from KriraAI, an AI development company in India, that lets non-technical teams query databases by asking questions in plain English. It converts each question into validated SQL, runs it against your database, and returns readable results, so people get answers without waiting on a developer or data analyst.
Under the hood, the solution grounds a large language model in your actual schema using retrieval over table names, column definitions, and a business glossary, then generates SQL that a validation layer checks before it runs read-only. The result is a shift in who can access data: analysts stop clearing a backlog of one-off report requests, while operations, sales, and finance staff self-serve the numbers they need. Gartner projects that 75% of new analytics content will be contextualized through generative AI by 2027, and natural language to SQL is one of the clearest ways that shift reaches everyday business users.
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Accuracy in natural language to SQL depends on grounding, so KriraAI retrieves the relevant tables, columns, joins, and business definitions from a metadata catalog before the model writes any SQL. A validation layer then checks the generated query for syntax, applies access rules, and runs a read-only dry check, and ambiguous questions route to a human before execution. Because the same validation runs on every query, the silent join errors and wrong-column guesses common in hand-written ad hoc SQL are caught before results reach the user.
Users type a question into a chat interface embedded in Slack, Microsoft Teams, or your BI tool, and get back a table, a chart, and the exact SQL the system generated. Showing the underlying query means a data-literate reviewer can confirm the logic instead of trusting a black box. No SQL knowledge is required to ask, and no part of your schema has to be memorized.
The solution connects through secure connectors and APIs to PostgreSQL, MySQL, SQL Server, Oracle, Snowflake, and BigQuery, and it reads from the semantic models you already publish in Power BI, Tableau, or Metabase. Deployment runs inside your own cloud VPC on AWS, Google Cloud, or Azure, or on-premises, so query traffic and schema stay within your perimeter. Existing role definitions in your database carry through rather than being rebuilt in a separate tool.
A metadata catalog and semantic layer let the same interface serve dozens of tables and multiple database dialects without a rewrite per source. Model routing sends simple lookups to smaller, lower-latency models and reserves larger models for multi-join analytical questions, which keeps cost and response time predictable as usage grows. Frequently asked questions are cached, so repeat queries return without regenerating SQL.
KriraAI builds a business glossary that maps terms like active customer, churn, or net revenue to specific tables and logic, so the same question returns the same definition for every user. Onboarding covers how to phrase questions, how to read the generated SQL, and how to flag a wrong answer for correction. This governed layer is what keeps self-service analytics from splintering into conflicting numbers across teams.
Access is enforced with role-based access control and, where needed, row-level and column-level restrictions, so a user only queries data they are already permitted to see. Data is encrypted in transit and at rest, every generated query is written to a timestamped audit trail, and execution is read-only by default to prevent writes or deletes. The design aligns to SOC 2 and ISO 27001 controls, GDPR, and, for India-based data, the DPDP Act 2023.
A user types a plain-English question into the chat interface in Slack, Teams, or the BI tool, for example, "What was gross margin by region last quarter?" The NLP layer parses the intent, entities, and time frame.
The system retrieves the relevant tables, columns, joins, and glossary definitions from the metadata catalog, so the model works from your real data structure rather than a generic guess.
The model drafts the SQL query, then a validation step checks syntax, applies the user's access rules, and runs a read-only dry check. Questions that are ambiguous or exceed a set cost limit are flagged for review before they run.
The validated query runs read-only against the database, and the answer comes back as a table and chart alongside the exact SQL used.
Corrections and approved query patterns are saved to the catalog, improving accuracy on similar questions over time.
If your analysts are spending the week clearing ad hoc report requests, a scoped assessment will show which of those questions natural language to SQL can answer directly.
Map My Team's Data RequestsA [SIZE] software company in [REGION], name withheld under NDA, ran every data request through a two-person analytics team. Sales, support, and finance filed tickets for numbers as routine as pipeline by stage or tickets closed per week, and the queue meant answers often arrived after the decision had already been made.
KriraAI deployed its Database Query Automation solution inside the client's Azure environment, connected to their Snowflake warehouse and existing Power BI semantic models. We built a business glossary for their core metrics, set role-based access to match their existing data permissions, and embedded the query interface in Microsoft Teams.
Auto-generated dashboards that surface trends and anomalies without manual report building.
Learn moreA chat assistant that answers follow-up data questions in context across a session.
Learn moreScheduled, narrative reports built from your live data and metric definitions.
Learn moreDemand, revenue, and churn forecasts trained on your historical data.
Learn moreConnectors and transformations that keep your warehouse current and query-ready.
Learn moreA retrieval-augmented assistant that answers questions from internal documents and policies.
Learn moreNLP extraction that turns invoices, contracts, and forms into structured, queryable records.
Learn moreRole-based access, cataloging, and audit trails applied consistently across data sources.
Learn moreDatabase query automation, in this context, is a natural language to SQL system that converts a plain-English question into a SQL query, runs it, and returns the result. It removes the need for a person to hand-write SQL for routine data questions. KriraAI delivers it as a governed interface connected to your existing databases.
Accuracy depends less on the model alone and more on grounding and validation. KriraAI's natural language to SQL solution retrieves your real schema and business definitions before generating a query, validates the SQL, and shows the generated query so a reviewer can confirm it. Ambiguous questions are routed to a person rather than answered with a guess.
Yes, when access is governed. Queries run read-only by default, role-based access control limits each user to data they are already permitted to see, and every query is logged to an audit trail. Users never touch raw credentials or write access, so they cannot alter or delete records.
KriraAI's solution connects to common relational and cloud databases including PostgreSQL, MySQL, SQL Server, Oracle, Snowflake, and BigQuery. It reads semantic models from BI tools such as Power BI, Tableau, and Metabase, and the query interface can live inside Slack or Microsoft Teams. Deployment runs in your own cloud VPC or on-premises.
No, it changes what they spend time on. Natural language to SQL handles the repetitive, well-defined questions that fill an analyst's queue, which frees the analysts for data modeling, complex analysis, and maintaining the semantic layer. The people who understand the data still define the metrics the system relies on.
For India-based data, KriraAI can deploy entirely within your own infrastructure, so records and schema never leave your perimeter, which supports data residency requirements. The solution is designed to align with the DPDP Act 2023 through access controls, audit logging, and purpose-limited read-only access. Compliance specifics are scoped to your environment during setup.
