System One decision architecture for AI agents
Frontier models generate. Jev decides. Jevineering is the paid practice of wiring Choice, Score, and Noul into agent loops so routing, gates, and scoring stop burning generation budget.
Starting at $2,500 USD for a diagnostic. No Stripe checkout on this page — you talk to us first.
The leak
Every agent team we talk to has the same pattern: an expensive frontier model sits in a loop answering yes-or-no, picking a worker, scoring relevance, or gating a tool call. Jev (TypeSafe) is a cheap System One model built for those structured questions — about $0.042 per million input tokens, $0 output.
We do not sell Jev itself. We sell the architecture and the install: evidence state, insertion points, confidence thresholds, and a fleet that stays cheap as it grows. Jevineering is independent and is not affiliated with TypeSafe.
Commercial offers
Prices are USD starting bands, not guarantees. Scope and quote after a short call.
For teams with live agents who suspect frontier spend is leaking into yes/no, routing, and scoring.
For teams ready to wire Jev into one production loop — routing, gates, compaction, or tool selection.
For orgs running more than one production agent who need architecture reviews as the fleet grows.
Downloadable patterns for engineers who want the install shape before a paid engagement. Copy-paste Choice / Score / Noul templates for routing, gates, funnels, and compaction. This is our kit — not a TypeSafe SDK and not a hosted Jev account.
Starting at $490 USD. Sent after you request it. Included at no extra line item with a Decision Brain Install.
Or skim the free 10-step guide first.
How it works
Sort every frontier call: creates text, or picks / scores / gates? Only the second pile is Jev work.
Jev decides on exactly the state you hand it. Goals, evidence, constraints — not vibes.
Wire primitives into routers, tool gates, compaction, and branch pruning. Code executes the answer.
Log decisions, tighten thresholds, escalate low confidence. Keep generation for generation.
Where the brain goes
Inspired by Layton Gott’s “Jev Use Cases Clearly Explained” as circulated by Logan Gott, plus TypeSafe’s public System One description. These are patterns, not client case studies.
Gate a tool result: retry, repair, or escalate when the call drifted from the request — without asking a chat model to narrate the failure.
Decide which worker acts next, or whether this turn needs a frontier model at all. Paying GPT-class prices to pick a model is the leak.
Score candidates, keep the top few, Choice the winner. Code eligibility first; Jev only sees survivors.
Publish, delete, transfer, bash. High-confidence block or pass; low-confidence human queue. Permissions stay in code.
Score difficulty and risk before you spend. Cheap model when the state is simple; lock tools when confidence or policy fails.
Prioritize review, score an LLM judge’s claim, or rerank passages before the generator sees them. Semantic linting in CI is the same primitive.
ROI teaser
Illustrative only. Compares billed input on Jev’s public $0.042/M rate vs GPT-4o-class generation pricing for the same decision volume. An audit uses your traces.
Primary CTA
Email hello@jevineering.com. The form opens a draft to that inbox — we do not charge a card on this site.
Say which package you want, what the agent does, and where you think the leak is.