Workflow review
Map each workflow to a question type: Choice, Score or Noul. Flag what stays on your current model.
You leave with a go or no-go per workflow and the data the evaluation needs.
Evaluation and implementation
Make the production decision
with evidence.
Evaluate Jev against your current workflow. Establish where it meets your accuracy and cost requirements, then define the path to deployment.
From workflow selection to deployment
Each step ends with a decision. You can stop after any of them.
Shadow evaluation
Jev runs beside your current model on real traffic. Both answers logged, nothing switched.
You leave with accuracy by confidence band, latency, cost, a threshold plan and a switch or stay call.
Production migration
Confidence routing, fallback to your current model, version pinning, monitoring and a rollback switch.
You leave with code in your repo, a runbook and a re-test harness for new Jev versions.
Scope, deliverables and fees agreed before evaluation begins.
TypeSafe publishes the method.
We run it on your data.
| TypeSafe guidance | Where it happens |
|---|---|
| Shadow-run Jev beside current logic for 1 to 2 weeks. | Step 02 |
| Collect 20+ labeled examples with ground truth. | Steps 01 and 02 |
| Start with conservative thresholds, test on your data, adjust. | Step 02 |
| Automate low-risk paths first, escalate uncertain cases. | Step 03 |
| Do arithmetic, dates and filtering in code, not the model. | Steps 01 and 03 |
Forward Deploying
Talents From
Partners
Have a workflow in mind?
Share the operating context. We can help define the evaluation scope and the evidence your team needs.
- The decision your system makes today.
- Monthly volume and current operating cost.
- Accuracy requirements and review capacity.
Prefer to pick a time now? Open the calendar ↗
Context received.
We’ll follow up on the workflow and evaluation scope.
Continue to the field guide →Questions
Are you part of TypeSafe?
No. Decision Lab is an independent implementation partner. Jev is a TypeSafe AI product and is used under your own TypeSafe account.
Do we need Jev access before starting?
Not for the fit review. The shadow evaluation runs on your TypeSafe account. Jev is in early access; request access at console.typesafe.ai.
What data does the evaluation need?
Representative inputs from the workflow and the answers you expect. If labels do not exist yet, we build the set with your team in step 01.
Where does our data go?
It stays in your environment and your TypeSafe account. No copies are kept after the engagement.
What if Jev is not a fit?
The report says stay. You keep the harness to re-test when TypeSafe ships a new version.
Does Jev replace our LLM?
Only for decisions: pick, score, yes or no. Writing, code and open-ended reasoning stay on your current model. Most workflows end up split.