Built for Jev. Open to other models.
Run decisions.
Across your data.
Classify and score your whole dataset.
Connect stages. Trace every answer.
Find customers showing cancellation intent
Example completeSwipe the map to explore →
Find articles about AI interviewing
Example completeSwipe the map to explore →
Find sales calls with buying intent
Example completeSwipe the map to explore →
Every node is a record at a step. Select one to follow its path.
From a folder of files to a repeatable workflow.
One state. Many questions.
Ask together.
Go deeper after.
Give the model a record and a set of questions. Independent questions share one request.
Need an answer before asking the next question? That’s your next stage.
What makes a stage“Four outages this month. Fix this or we will not renew.”
Cancellation intent?
Main concern?
Business impact?
The answer is only the beginning.
Follow the whole story.
Which records made it through? What did the model see? Open a node and inspect the input, questions and raw response.
Keep the path.
Follow records through groups, filters and model stages. Excluded rows stay visible.
Inspect the evidence.
See the source data, typed results, and provider request and response behind a decision.
Change it. Run it again.
Refine questions or switch models. Start a new run without overwriting the earlier one.
Find cancellation signals
Intent · Concern · Impact
Investigate the reasons
Selected customers + ticket history
Your workflow. Your models.
Start with a query.
Build from there.
Run a question set against your data. Save it as a stage, then connect it to the next one.
- Choose a model at each stage.
- Use hosted Jev or a supported self-hosted model, including Kev 4B.
- Let code handle the data.
- Group, join and filter between model calls.
- Keep your inputs versioned.
- Upload a fresh dataset. Earlier runs keep their original snapshot.
A few places to start.
Same engine.
Different questions.
Who’s showing signs of leaving?
Group tickets by customer. Find cancellation intent. Investigate the reason.
CONTENT & RESEARCHWhich articles actually matter?
Classify your collection. Keep relevant articles. Ask a more specific question.
INTERVIEW ANALYSISWhat does each transcript show?
Apply the same rubric across interviews. Inspect the evidence behind each score.
A few practical details.
What data can I bring?
Upload CSV, JSON, JSONL, TXT or Markdown. That can be exported support tickets, articles, account records, or call and interview transcripts. Audio needs to be transcribed before upload. Current default limits are 20 MB and 10,000 records per file.
Do I have to use Jev?
No. Choose a supported model for each stage. Jevreduce supports hosted Jev and self-hosted models such as Kev 4B through provider adapters. The model you use determines which primitives and limits are available.
How is this different from asking a chatbot?
A run applies the same versioned questions and rules across a dataset. The platform stores structured outputs and the path each record took, so you can inspect individual decisions and rerun a workflow with new questions or models.
Can I use Pylon, Linear or Fireflies data?
Start with a supported file export from your tools. Native connectors are planned; this page’s examples do not require a live connection to those services.
Does replay give the same answers?
Replay creates a new run using the pinned inputs and workflow definition, with any supported overrides you choose. Model answers can change. The original run remains available for comparison.
Your data. Your questions.
Every path visible.
Start with one dataset. See where it takes you.
Open jevreduce