Classify, score and grade any record with a decision model.
Decision models for classification, inside your pipelines. An AI video company uses it in production to grade every demo request from A to D within hours.
Typed questions, typed answers
Ask typed questions about any record: a yes or no, a choice between grades, or a score. Each answer comes back as a probability.
The standard in plain text
Criteria are written in plain language. Change the text and the next run applies the new definition, with no model to retrain.
Inside your pipelines
Runs inside managed pipelines and stores every answer next to the data it judged, where you can check it against real outcomes.
Nothing to set up
The platform chooses the model and holds the credential. Declare the connection; there is nothing to configure.
In code
client = s.evaluation_client("evaluation")result = client.evaluate( state={"job_title": "Head of Learning & Development", "company": "Acme"}, questions={ "grade": { "type": "choice", "instructions": "Which grade does this demo request match?", "criteria": {"A": "...", "B": "...", "C": "...", "D": "..."}, }, },)grade = result["answers"]["grade"]["choice"]Use it, build on it, or have it built
Use it
Declare the evaluation connection; there is nothing to configure.
Build it with your agent
Your agent writes the questions and criteria for any record your business needs to judge.
Have our engineers build it
Our engineers build the scoring with you and check it against your past outcomes.
Replaces Per-client ML training pipelines and Brittle scoring rules.
Works with
Solutions built with it
Read more
- 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.
- 2 October 2026How to leave GA4 without losing a single reportYou don't have to rip out gtag to own your web analytics. Change where it sends, keep GA4 running while you rebuild, and switch only when the numbers match.
- 1 October 2026Lead scoring without a training pipelineGrade every demo request against standards written in plain language. The standards are drafted from your own CRM history, applied by a decision model, and changed by editing text instead of retraining.
- 30 September 2026The future of ad traffic verification with SegmentStream and JevAd traffic verification is still sold as a black box for $60–100k a year. The next generation runs next to your conversions, is checked against real outcomes, and is decided by a model built for decisions.
Start a pilot.
Want ai evaluation 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.