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AI SQL Agent Development Services

Ask your database questions in plain English.

Natural-language querying over large, complex databases - with intelligent SQL generation, safety guardrails and business-context awareness.

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Your data holds the answers, but SQL sits between your team and those answers. We remove the bottleneck without removing the guardrails.

We build agents that translate business questions into correct, efficient SQL against your real schema - resolving table and column names, handling joins, and grounding every query in your business definitions rather than generic logic.

What we build

Conversational analytics, done safely

Accuracy and governance are the hard parts — that's where we focus.

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    Natural-language to SQL

    Users ask in plain language; the agent generates schema-aware SQL and returns results, charts and explanations.

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    Schema & semantic grounding

    We map business terms to your tables via a semantic layer and example query pairs for higher accuracy.

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    Safety guardrails

    Read-only connections, allow-listed operations and a validation layer that catches bad or unsafe queries before execution.

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    Self-correction

    When a query errors, the agent inspects the failure and retries - improving reliability on complex questions.

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    Auto visualisation

    Results are turned into the right chart or table automatically, with the generated SQL shown for transparency.

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    Governance & audit

    Role-based permissions, PII redaction and full audit trails of who asked what and which SQL ran.

How we deliver

A path from idea to production

A pragmatic engagement model that de-risks adoption and gets a working system in front of your users fast.

  • 01

    Schema discovery

    We introspect your database and document tables, relationships and the business meaning behind them.

  • 02

    Semantic layer

    We encode metrics, synonyms and example questions so the agent speaks your business's language.

  • 03

    Guardrails & eval

    We add validation, permissions and an evaluation set to measure accuracy on your real questions.

  • 04

    Rollout

    We integrate into Slack, a web app or your BI tool, then monitor accuracy and refine over time.

Tools & stack

Technologies we work with

We stay model- and vendor-flexible, choosing the stack that fits your data, budget, and compliance needs.

  • PostgreSQL
  • MySQL
  • Snowflake
  • BigQuery
  • Redshift
  • SQL Server
  • LangChain
  • Vanna AI
  • Semantic layers
  • Anthropic Claude
  • OpenAI
  • MCP database servers

Where it fits

Use cases & industries

For every team that waits on the data team to answer a question.

  • Self-serve analytics

    Let business users answer their own data questions without writing SQL.

  • Executive dashboards

    Ad-hoc questions beyond the pre-built dashboards, answered in seconds.

  • Ops & support

    Front-line staff look up records and metrics conversationally.

  • Finance & revenue

    Explore cost, usage and revenue data with business-defined metrics.

  • Product analytics

    Query usage and funnels without a data-team ticket.

  • Data exploration

    Analysts prototype queries faster with an AI pair.

Common questions

No. We default to read-only connections and allow-list only safe operations, so the agent can query but never modify your data.

We ground the agent in your real schema and a semantic layer, validate every generated query before running it, and let it self-correct on errors.

Yes. We enforce role-based and row/column-level access so each user only ever sees data they're authorised to see.

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