Jev Studio, Decision Rules for Incoming Text

Many teams make the same decisions about incoming text every day: Is this
request urgent? Which team takes this ticket? Does this review need a personal
reply? The rules behind those decisions are rarely written down. They live in
the heads of the people who apply them.

Jev is TypeSafe AI's decision model: it answers typed questions about a text,
as yes or no, a pick from a list, or a level on a scale, and says how sure it is
each time.

Jev Studio turns such rules into a shared artifact anyone can check. The
product owner writes the decision as a question in her own words and sees right
away what Jev answers and how sure it is. She labels her own cases, sees how
often the rule is right and where the line between yes and no should sit. Once
the rule holds, engineering gets the ready request, typed TypeScript code, and
the labeled cases as tests.

Product and engineering work on the same artifact. The rule is proven before
anyone builds it, and the same cases guard it in code afterwards. That puts quality
into the first product conversation.

Jev answers typed questions in a fraction of a second for a fraction of a
cent. A language model stays where text really needs to be written. The
question assistant in the studio says so too: it proposes fitting questions, or
explains why another tool fits better.

• Product discovery with personas and the questions they ask every day, before the first screen existed
• A studio to write, ask, and check rules on labeled cases, with a suggested line between yes and no
• A question assistant that drafts checkable questions with a sample text and cases from a description, or recommends a better tool
• Handover to engineering as a ready request, typed TypeScript code, and tests from the labeled cases
• A bilingual landing page with recorded Jev answers, a waitlist, and invitation-only sign-in
• Operations in my own homelab with branch protection, automated updates, backups in two places, and drilled restores

Jev Studio is also proof of my Agentic Operating Model. It started with
personas and the questions they ask every day, then moved in thin end-to-end
slices instead of a big plan. Inside the harness, agents carry the work through
tests, independent review, merge, deployment, and the check in production.
People decide direction, budgets, and anything that reaches the outside world.

Operations are part of it from the start: branch protection with required
checks, automated dependency updates with a cooldown, encrypted backups in two
places with drilled restores, and invitation-only sign-in with a self-hosted
human check.

See Jev Studio and join the waitlist. The conversation gets interesting when
your team makes recurring decisions about incoming text that live in people's
heads or in long prompts today.