Integration
Connect BigML and SQL Server
When New project fires in BigML, your agent runs Find Rows via Custom Query in SQL Server, or New or Updated Row in SQL Server to Get Resource Status in BigML. Describe the whole flow and it is built, tested on every scenario and kept running.
What the catalog holds today. Ask for anything missing and your agent reads the API documentation and adds it.
Build solutions, not workarounds.
What do you want BigML and SQL Server to do together?
Ask the assistant
Connect your AI client
Give your AI client this address.
https://mcp.apiant.aiOne connection does both: ask your client to act in an app once, or have it build an automation that keeps running. Any client that takes a remote MCP server. Nothing to install. Create your free account, then sign in once in your browser.
Set it up in your client
Claude Code
Run this in the directory you want to work in.
claude mcp add --transport http -s project apiant-ai https://mcp.apiant.aiStart Claude Code there, approve the server, and sign in in the browser page it opens.
Claude Code guide (opens in a new tab)Claude, web and desktop
Open Customize, then Connectors, then Add custom connector, and paste the address. On Team and Enterprise an owner adds it first.
Setup guide (opens in a new tab)Cursor
Add the address to your MCP configuration as a server URL. Cursor signs you in in your browser.
Setup guide (opens in a new tab)Zed
Open Settings, then AI, then MCP Servers, then Add Server, then Add Remote Server.
Setup guide (opens in a new tab)Codex and the ChatGPT desktop app
Open Settings, then MCP servers, then Add server, and choose Streamable HTTP. From the terminal, Codex takes the address with its own add command and then signs you in.
Setup guide (opens in a new tab)Any other MCP client
Paste the address where the client asks for a remote MCP server URL, and name the server apiant-ai where it lets you. APIANT's skills reach the client over the same connection.
Setup guide (opens in a new tab)BigML holds the sources, datasets and models your team trains with, while SQL Server holds the tables the rest of the business reads. Connecting them lets a new BigML project write itself into a SQL Server tracking table, and lets rows in SQL Server pull the current status of a model or prediction back from BigML.
Example flows
What BigML and SQL Server can do together
From BigML to SQL Server
When New project in BigML
then Find Rows via Custom Query in SQL Server
From SQL Server to BigML
Starting points drawn from the triggers and actions in the catalog today. Describe the flow you need, at any depth, and your agent builds it and tests every scenario before it ships.
Everything your agent can do with BigML and SQL Server
Triggers and actions 5 triggers · 11 actions
- New project
Triggered when a new project is created in BigML. Polls the BigML projects list endpoint and emits each project, deduplicated by its resource id.
- Get Resource Status
Retrieve a BigML resource by id (source/dataset/model/prediction) to poll its status and read results.
- List Datasets
List BigML datasets, optionally filtered/paginated.
- List Models
List BigML models, optionally filtered/paginated.
- List Sources
List BigML sources, optionally filtered/paginated.
- New or Updated Row
Triggers when a new row is added or modified.
- New or Updated Row (Custom Query)
Fires for each new or changed row returned by a SELECT statement you write against the connected SQL Server database.
- New Row
Triggers when you add a new row.
- New Table
Fires when a new table appears in the connected SQL Server database.
- Add Row
Add one row to a table on the connected SQL Server database.
- Delete Row
Delete the rows in a table whose chosen column equals a value.
- Find Row
Find the first row in a table whose chosen column matches a value.
- Find Rows via Custom Query
Runs a SELECT statement you write against the connected SQL Server database and returns every row it matches.
- Find Row via Custom Query
Runs a SELECT statement you write against the connected SQL Server database and returns the first row it matches.
- Run Custom Statement
Runs a statement you write against the connected SQL Server database that changes data rather than returning rows, and reports how many rows it changed.
- Update Row
Change column values on every row of a table whose chosen column matches a value.
No trigger or action matches that. Ask your agent to add it: it reads the API documentation and builds what you describe.
Each app on its own
Reviews
What our customers say
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Questions
BigML and SQL Server, answered
Can the automation tell whether a BigML project is already recorded in SQL Server?
Yes. The agent uses Find Row on your SQL Server tracking table to look for the project id, then adds a row when there is no match or updates the existing row when there is. Every scenario is tested before it goes live.
How do we follow a BigML model or prediction that is still being built?
A row change in SQL Server can carry the BigML resource id, and Get Resource Status retrieves that source, dataset, model or prediction so you can read its state. What you do with the result, such as writing it back with Update Row, is up to you.
What can start an automation from BigML?
Any of BigML's 1 trigger in the catalog, including New project. Describe the moment in plain language and your agent picks the right one.
What can your agent do in SQL Server when something happens in BigML?
Any of SQL Server's 7 actions, including Find Rows via Custom Query, Run Custom Statement, Update Row and Add Row. Your agent maps the data both ways and tests every scenario before it ships.
Can it also run from SQL Server to BigML?
Yes. When something happens in SQL Server, your agent can act in BigML too. This page covers both directions: SQL Server's triggers and BigML's actions are listed here as well.
Do I have to build a connector for BigML or SQL Server?
No. Both are in the catalog with their triggers and actions. You describe the automation; your agent builds it, tests every scenario and keeps it running.
Can my AI client use BigML and SQL Server without building an automation?
Yes. With your AI client connected to APIANT, ask it to do something in either app once and it runs that action on the spot. Ask for an automation and your agent builds it, tests every scenario and keeps it running. One connection covers both.
What happens when something fails?
Your agent works that out for you. It plans the failure paths along with the happy path, then tests every scenario before it ships.
Is there a free plan?
Yes. Every plan includes the agent, including free, and building from your own AI client spends no assistant credits.