Typed decisions with probability and confidence
Choice and score questions return typed values along with probabilities and confidence, allowing software to evaluate results without parsing a free-text answer.
TypeSafe: Jev documentation
Extremely fast AI model for structured decisions, classification and routing within automated workflows, rather than traditional chat or writing tasks.
Editorial review: unknown
Facts checked: Oct 18, 2026
Translated: Oct 18, 2026
Practical value, usability and value for money, equally weighted.
Development and automation teams that want to automate many recurring decisions such as lead scoring, support routing, content QA, or topic classification with clear thresholds.
Jev delivers machine-readable decisions instead of prose. That can make automations faster and cheaper, but it does not replace validation of actual decision quality.
If employees need to chat with AI, write copy, research, or solve open-ended tasks through language. For business-critical decisions, Jev should first run alongside the existing process.
Choice and score questions return typed values along with probabilities and confidence, allowing software to evaluate results without parsing a free-text answer.
TypeSafe: Jev documentationMultiple clearly bounded questions are checked in parallel against the same context in a single API call so classification and subsequent routing can assess separate criteria.
TypeSafe: Jev documentationThe provider describes token-based billing, allowing large classification runs to be costed according to input volume processed and requiring your own volume estimate beforehand.
TypeSafe: Jev documentationFacts checked: Oct 18, 2026
Client: no · Server: no
No MCP information found. No source
Facts checked: Sep 30, 2026
US provider with US hosting; no DPA or transfer mechanisms found. DPA: no · EU hosting: no · Headquarters: USA · Source
Facts checked: Sep 30, 2026
Free plan: unknown
Usage-based billing. Check the vendor's source for base fees, advance payments and the costs of additional services.
An ongoing free plan is not clearly confirmed. Trials and one-off credits do not count.
Facts checked: Oct 5, 2026
Start in Shadow Mode with 500 previously processed leads or support cases. Compare Jev's decisions with historical outcomes, then define rules for automatic actions and manual reviews.
Define fixed categories for new contact inquiries, such as purchase interest, support and partnership. Have Jev return the category and confidence and route uncertain cases to a manual collection list. Set the approval threshold using your own examples instead of accepting an arbitrary confidence value as a quality guarantee without checking.
TypeSafe AI: JevDescribe the qualification criteria you actually need to your developer, such as a suitable industry and a specific need in the inquiry form. Jev can use these to deliver structured decisions for downstream software. Regularly check counterexamples for rejected or uncertain inquiries, so valuable prospects do not disappear from your sales list because of overly broad categories.
TypeSafe AI: JevSources: Oct 18, 2026
Development and automation teams that want to automate many recurring decisions such as lead scoring, support routing, content QA, or topic classification with clear thresholds.
If employees need to chat with AI, write copy, research, or solve open-ended tasks through language. For business-critical decisions, Jev should first run alongside the existing process.
Typed decisions with probability and confidence; fast parallel classification and routing; usage-based pricing for large data volumes
Start in Shadow Mode with 500 previously processed leads or support cases. Compare Jev's decisions with historical outcomes, then define rules for automatic actions and manual reviews.
Free access: no verified information · Price: no verified information. Pricing details and the documented check date are in the pricing section of this page.