Pipelines that load, model and check your data, on a schedule in code.
Ingestion, models, checks and schedules in Python, in the pipeline/ folder of your repository. The platform builds the graph, runs it on managed compute and shows the lineage of every table.
Pipeline jobs with their schedules, assets and runs. Start one by hand, or let the schedule in code run it.
Ingestion
Ingestion assets load any API by day through a connection, with retries and backfills. Re-running a day replaces it exactly.
Models
Models are Python dataframe expressions compiled to warehouse SQL, with declared schemas, tiers and partitions.
Checks
Checks run with the pipeline and record their result next to the table, where lineage and agents can see it.
Schedules
Schedules are declared in code beside the jobs, with a timezone and the partitions each run covers. Pause or resume them from the CLI.
Lineage and run history
See what feeds what, which run produced a table and what failed upstream, in the console and the CLI.
Managed runs
Runs on managed compute. There are no clusters, workers or orchestrator to operate.
In code
import segmentstream as srevenue = s.job("revenue_pipeline", assets=[revenue_summary], include_upstream=True)defs = s.definitions( assets=[raw_orders, normalized_orders, revenue_summary], checks=[positive_order_amounts], jobs=[revenue], schedules=[ s.schedule( "revenue_morning", job=revenue, cron="0 6 * * *", timezone="Europe/London", partitions=s.LatestPartitions(3), ), ],)Use it, build on it, or have it built
Use it
Run, backfill and inspect jobs from the console or the CLI.
Build it with your agent
Your agent writes ingestion assets, models and checks, one per file, and validates the graph before it deploys.
Have our engineers build it
Our engineers build the first pipelines with you, from the tools you already run.
Replaces Fivetran, dbt Cloud, Airflow and Hand-written alert queries.
Works with
Solutions built with it
Read more
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- 8 October 2026MCP gateway vs raw MCP servers: how to run a company through any agentEvery tool your company uses now has an MCP server, and the usual way to give an agent access is to plug them all in. It demos well and breaks on permissions, rules, context and the next agent you try. An MCP gateway over your company's ontology gives every agent one door, with your own actions, roles and history behind it.
- 7 October 2026Read this before you cancel HubSpot or SalesforceCompanies are cancelling CRM contracts and having AI agents write their own. Writing the CRM is now the easy part. What decides whether it survives is the layer underneath it: objects, permissions, actions, history and safe access for agents. That layer is called an ontology.
- 3 October 2026AI agents can build your BI. Here's why they get the numbers wrong.Benchmarks keep finding that agents writing SQL against raw tables give confident wrong answers, while the same agents answering through a semantic layer get nearly everything right. What that means for how you build analytics now.
Start a pilot.
Want pipelines set up for you? Our forward-deployed engineers build it with you in your workspace, as code you own.
- One solution, on your data, in your own workspace
- First version live in two weeks
- Parity proven before anything is switched off
- Fixed scope and price, credited to your contract if you continue
Not ready for a pilot? Send the form anyway with your question. An engineer will reply, not a sales rep.
How the pilot worksYour company’s software, built by agents, owned by you.
$ segmentstream workspace create acme --region eu --subdomain acme
▸ Created workspace acme · database, warehouse and a private git repository
$ segmentstream workspace WORKSPACE-ID clone acme && cd acme
▸ your agent writes the five folders and runs the checks
$ git add -A && git commit -m "First version"
$ segmentstream deploy
▸ app, ontology API and MCP tools live for your invited members
Our engineers build the first version on your data in two weeks, at a fixed price.
Or join the waitlist for self-serve:
Self-serve opens to the waitlist first.