Connect your databases read-only, auto-discover the business entities inside them, and let people and AI agents ask questions in plain language — answered locally, against your own data. No SQL. No data movement.
Organisations want AI agents and business users to ask questions of their data — but the data sits fragmented across databases, warehouses and files, and nobody wants to give an LLM raw access to it. Most GenAI pilots stall exactly here: the model is fine, the data context is missing.
The industry's answer is a semantic layer — a governed model of your business that grounds AI in what your data actually means. Enterprise knowledge-graph platforms (Stardog, Palantir-class tooling) prove the category works, at enterprise-programme cost: ontology teams, long implementations, contact-sales pricing.
Hand-building a semantic layer takes months of modelling work, and it's stale the day it ships. AgentData auto-discovers entities (Customer, Order, Product), metrics and dimensions across your systems, and puts them behind a governed review workflow — so the model of your business stays alive as your data changes.
The semantic model — table and metric names, never contents.
The plain-language question being asked, in any language.
Stays in your database. Queries execute locally, results go straight to the user.
A reviewed, versioned query — executed against your own infrastructure.
Built-in MCP server and REST API with per-user keys: list · describe · query_metric · query_nl. Claude, ChatGPT and your own agents connect directly.
Approve or reject discovered entities, version queries, full audit log — with query-approval workflows for sensitive answers.
One question across multiple sources — built-in federation, or scale out with Cube + Trino.
Natural-language querying in any language, with conversation memory — for executives, not just analysts.
Emits Cube + dbt-semantic YAML — the model you curate is portable, not proprietary.
Connect → profile → classify → cluster into entities → emit YAML → human review. First entities typically the same day.
AgentData is built for organisations that want AI grounded in their data this quarter — not after a knowledge-graph programme.
Knowledge-graph platforms (Stardog, Palantir-class) are powerful but sized for enterprise programmes — ontology specialists, months of modelling, opaque pricing. Warehouse-native SaaS layers cover Snowflake/Databricks/BigQuery only, in their cloud, not yours.
dbt/Cube models written by hand give control, but take months of engineering and go stale the day the source schema changes — and still need an NL/agent access layer on top.
Auto-discovers the semantic model from your databases (first entities same day), human-reviewed, emits portable Cube + dbt YAML, agent-ready over MCP/REST from day one — across operational databases and warehouses alike, self-hostable, fixed-scope delivery by the engineer who built it.
AgentData also powers our consultancy work: it's the AI-ready access layer we deploy inside data engineering engagements when plain-language access is part of the outcome.
A free 30-minute discovery call — no sales pitch, just an honest conversation about what AI and good engineering can do for your business.
Pick a time that works for you. We'll talk through your goals, current systems and where the first measurable win is.