Gluesync AI Hub · Query Studio AI · gluesync.ai
SQL AI is Gluesync's answer path for everyone who already works in SQL. Inside the Control Plane, Query Studio AI drafts and completes statements from the schema it already knows. From any JDBC client, the gluesync.ai virtual table turns a question into one result row with the answer, a reviewable SQL proposal, and a conversation you can continue. Your models, your keys, the caller's permissions, read-only by default.
Two surfaces, one contract
Inside the Control Plane
A chat that already knows your schema and a Tab autocomplete that finishes the statement you started. The draft lands in the editor; you review it, then run it under Query Studio permissions.
From any JDBC client
A virtual table Core Hub owns and Query Forge serves like any other. DataGrip, DBeaver, Tableau, or a batch job asks a question in SQL and reads the answer back as a row.
How it works
Ground
SQL AI reads the schemas, tables, columns, and keys discovered through the agents Gluesync integrates, plus the Enterprise brain's graph and memory. Name a pipeline, agent, schema, or table in the question and Core Hub resolves the scope; explicit IDs win when you pin them.
Draft
Query Studio AI drops the draft into the editor and completes the statement you are typing with suffix-only ghost text. gluesync.ai returns proposed_sql in the row. Either way the SQL is visible, explainable, and yours to edit.
Review and run
Execution follows the caller's Core Hub role, the per-user data-access matrix, Query Studio's read-only defaults, and PII masking. There is no AI service account, and no path around the permissions you already set.
The gluesync.ai row
The shortest valid query is a question. Add spark_agent to put a published agent behind the answer, provider_id to pin a model from the vault, or conversation_id to continue the exchange. Only equality predicates on these columns are accepted; everything else fails with a SQLSTATE your code can handle.
answerproposed_sqlcitationsconversation_idpipeline_id · agent_idprovider_id · spark_agentSELECT answer, proposed_sql, conversation_id
FROM gluesync.ai
WHERE question = 'in the crm-sync pipeline, find customers
with duplicate emails in dbo.CUSTOMERS'
AND spark_agent = 'Ops helper';SELECT EMAIL, COUNT(*) FROM dbo.CUSTOMERS GROUP BY EMAIL HAVING COUNT(*) > 1; b5acd3d2-7c9d-4100-8296-25b10132ebe0 Capabilities
Two surfaces, one contract: the question is grounded in the schema the caller can see, the SQL is a proposal until a person or a governed job runs it, and the answer never carries more than the caller could read.
Query Studio AI
Open the helper from the Query Studio launcher and ask for the data you need. The draft uses the database, schema, and editor context already visible in your workspace.
Query Studio AI
Press Tab while you write. The helper proposes what comes next as ghost text and never rewrites what you already typed.
gluesync.ai
Query Forge serves gluesync.ai like a regular table. SELECT with a question predicate, read one row back. Prepared parameters are accepted, so applications bind the question instead of concatenating it.
gluesync.ai
Mention a pipeline, agent, schema, or table in the question and Core Hub grounds the answer on the strongest unique match, returning the resolved pipeline_id and agent_id in the row.
gluesync.ai
spark_agent names a published AI Studio agent by id or unique name. Query Forge applies its instructions and the intersection of its allow-list with the read-only catalog.
gluesync.ai
Pass the conversation_id from the previous row in the next WHERE clause and the follow-up keeps the context: limit that query to active customers, add the region, explain the join.
Safety
Columns classified as personal data are tagged in Query Studio, masked in the grid, copies, and exports, and never sent to the model in clear. SQL AI only uses read-only tools and never runs its own proposal.
Scheduling
Save the reviewed SQL as a Query Studio query. Chronos runs saved queries or custom SQL on a schedule, from a platform event, from a webhook, or as a step in a chained event, and Spark can reopen the same library.
Reach
Query Studio speaks the dialect of each connected engine, and MongoDB connections accept JSON, mongosh, or SQL. SQL AI drafts in the dialect of the agent you selected.
What enterprises do with it
SQL AI does not ask the business to adopt a new tool. It gives the tools they already trust a way to ask, and gives IT a contract it can govern.
BI and reporting
A Tableau or DataGrip user runs a gluesync.ai query from the connection they already have. No HTTP client, no API key handling, no copy-paste between tools. The answer and its SQL draft arrive as columns.
Analyst onboarding
A new analyst opens Query Studio on a 900-table ERP replica, asks which tables carry the customer identifier, and gets a grounded draft instead of a week of guessing. Tab completion turns intent into joins they can read.
Legacy estates
Gluesync already replicates from the systems no AI vendor connects to. SQL AI reasons over the schema Gluesync discovers and queries the replica read-only, so the mainframe ledger answers a business question without a new agent on the host.
Data quality
Ask for duplicate customer emails, orders without a customer, or rows that drifted between source and target. Review the proposed SQL, save it as a Query Studio query, and let Chronos run it every night.
Compliance
classify_table and classify_schema return labels only. Tagged columns are masked in the grid, copies, exports, and model prompts, and a per-user data-access matrix decides who may unmask a column for one session.
Applications
Bind the user's question as a JDBC parameter, read answer and proposed_sql from the result set, keep conversation_id for the follow-up. Your application gains an AI answer path with the database driver it already ships.
Operations
spark_agent = 'Ops helper' puts a published Spark agent behind the row. Summarize replication lag, explain a stuck entity, or list yesterday's dead letters without leaving the SQL client that is already open.
Federated questions
Query Forge already joins data across isolated Gluesync agents. SQL AI grounds the question on every schema visible to the caller's token, so a cross-system answer does not need a warehouse load first.
FAQ
No. Query Studio AI places the draft in the editor and gluesync.ai returns it in the proposed_sql column. A person, a saved query, or a Chronos Query Studio action runs it, under the caller's permissions and Query Studio's read-only defaults.
Both surfaces use the bring-your-own LLM vault configured in AI Studio: OpenAI, OpenAI-compatible endpoints, Anthropic, Azure OpenAI, or a local Ollama model that never leaves your network. Credentials stay encrypted in Core Hub. gluesync.ai accepts provider_id to pin a provider per query.
No. The virtual table only offers the read-only MCP tools the caller's token may already use, never elevates permissions, and never runs proposed_sql. The worst a question can do is cost tokens.
Schema names, column names and types, the question, and tool results the caller could read anyway. Columns classified as PII are masked before they reach the model, and Query Studio data-access rules decide which schemas and tables take part in grounding.
gluesync.ai fails with SQLSTATE 22023 and lists the candidates rather than picking one. Rephrase so one name wins, or pin the scope with pipeline_id and agent_id together. When nothing in the question resolves, the query runs with empty grounding instead of failing.
No. Query Studio AI and the generic gluesync.ai helper work with a configured provider alone. spark_agent is optional: name a published AI Studio agent by id or unique name when you want its instructions and allow-listed read-only tools behind the answer.
Gluesync AI Hub
The agent platform inside your data control plane: your models, Core Hub tools, and existing permissions.
Explore → Foundation Enterprise brainThe shared schema graph, memory, skills, and company tools behind every Spark agent and every AI client you connect.
Explore → Workspace AI StudioChat with governed Spark agents, publish immutable versions, route across your models, and govern spend from one workspace.
Explore → Automation AI WorkflowsPublished agents fired by Chronos schedules, platform events, webhooks, chained steps, and your own applications.
Explore → Outcomes AI use casesWhat data, operations, engineering, security, and business teams actually do with Spark agents on their own data.
Explore → Agent layer SparkThe Gluesync AI agent layer: Core Hub MCP tools, documentation and knowledge tools, and the caller's permissions on every call.
Explore →Gluesync AI Hub
Bring your own model, keep your own keys, and give every SQL client in the company an answer path that stays inside your permissions. Query Studio AI and gluesync.ai are included in Gluesync 2.3.