Governed Analytics

Governed AI analytics for enterprise data teams.

Provide natural-language access to data without losing SQL transparency, strict access controls, or auditability.

Workflow Value

From dense data to defensible action.

Business users wait days for SQL queries

When every operational question requires a data engineer to write a custom SQL query, decision-making grinds to a halt. We implement SemeLabs to empower business users with natural-language querying without breaking governance.

The problem with generic Text-to-SQL

Generic text-to-SQL tools can join the wrong tables, use the wrong business definition, or ignore permission boundaries. Our systems use approved semantic context, verified joins, visible SQL, and validation questions before teams rely on the output.

Governed SQL transparency & validation

Every plain-English question generates an inspectable, diffable SQL query. The user sees the answer and the visualization, while the data team can instantly verify the underlying logic. Zero black-box answers.

Architected for zero data movement

We deploy within your VPC. The system connects to Snowflake, BigQuery, or PostgreSQL using read-only credentials. It enforces your existing Row-Level Security (RLS) policies by injecting user identity context into every generated query.

Agent Workflow Architecture

How the AI agent system works behind the page.

Every solution is implemented as a controlled workflow, not a loose chatbot. The agent operates inside approved data scopes, produces inspectable outputs, and routes judgment back to the right human owner.

Scope the job

Define the exact workflow, input sources, business rules, user roles, output format, and what the AI agent is allowed to do.

Retrieve the right context

Pull only approved documents, records, ERP context, control libraries, or playbooks before the agent drafts or acts.

Produce source-visible output

Generate findings, matrices, notes, SQL-backed answers, or queues with source references, exception reasons, and confidence signals.

Validate before expansion

Measure reviewer edits, pass/partial/fail outcomes, time saved, exception quality, and adoption before moving to adjacent workflows.

FAQ

Frequently asked questions

Does this require moving our data?

No. We connect directly to your existing data warehouses (Snowflake, BigQuery, Postgres, etc.) using read-only credentials. Zero data movement is required.

Can it handle complex, messy database schemas?

Yes. We implement a semantic layer that maps your complex database schema into clear business definitions, allowing the AI to generate accurate queries.

Automate one repeatable workflow.

Bring the workpaper, evidence review, or diligence process that consumes the most hours. We will map a practical AI-assisted pilot around your methodology.