Valori reconstructs relevant, connected, point-in-time state. Jev turns that state into typed decisions with probabilities. Your code remains the authority.
AI systems rebuild their memory inside the prompt. As they grow, so does the prompt.
A larger context window does not solve the memory problem.
The system does not need everything it has ever seen. It needs the right state for the current decision.
Semantic similarity narrows the search space. But similarity alone is not enough.
"What changed before checkout started failing?" deployment_842 checkout_latency_129 config_change_418 incident_2026_04
The most important information is not always the nearest vector. Sometimes it is the record connected to it.
deployment_842
│
├── changed → service_checkout
├── deployed → config_418
├── depends_on → payments_api
└── preceded → latency_spike_129Not "what is true now," but what was true when the decision happened.
healthy → deploy → incident → healthy
service_checkout version: 8.4.3 healthy: true
service_checkout version: 8.4.2 config: config_418 latency: 4.8s healthy: false
Deterministic state, provenance, state hashes, and an audit trail — reproducible history, checked with BLAKE3-chained verification evidence, not just trusted.
The world contains uncertainty. The state supplied to a decision does not also need to be ambiguous.
deployment: 842 service: checkout latency_before: 180ms latency_after: 4.8s config_changed: true similar_incident: rollback_resolved
incident_type: deployment | dependency | traffic | unknown rollback_safe: true | false severity: low | medium | high | critical human_required: true | false
Jev did not need to generate a paragraph first.
Remember → retrieve → connect → reconstruct → decide → act → record → verify.
This is a loop, not a pipeline. What happens becomes memory for what happens next.
Checkout latency suddenly jumps from 180ms to 4.8s. An automated operations agent needs to determine what to do.
Logs
Deployments
Runbooks
Alerts
Incident history
Tool history
Service state
↓
Huge prompt
↓
LLM
↓
"Based on the information provided..."
↓
parse generated JSONValori ↓ find relevant records ↓ follow relationships ↓ reconstruct 14:32 state ↓ verify evidence ↓ compact state ↓ Jev ↓ typed decision
if (
rollbackSafe > 0.98 &&
deploymentAge < 10 * MINUTE &&
policy.allowAutomaticRollback
) {
rollback();
} else {
pageEngineer();
}The model recommends. Your software remains the authority.
Why did it do that?
rollback_921
│
├── decided_by → jev_decision_551
├── observed → latency_event_129
├── followed → deployment_842
├── involved → config_418
├── matched → incident_2026_04
├── governed_by → rollback_policy_v12
└── state → snapshot_8af92Instead of storing only the action, preserve the evidence that produced it.
The LLM becomes a specialist instead of the operating system.
The model doesn't need your history. It needs the state required for this decision.
Construct the state once. Evaluate the questions your workflow actually needs. Do not repeatedly ask a generative model to rediscover the same facts.
Knowledge lives outside the model.
Find semantically relevant records.
Follow relationships between memories.
Reconstruct what the system knew earlier.
Make the memory substrate reproducible.
Know where retrieved evidence came from.
Connect important state and history to cryptographic evidence.
Receive machine-usable decision outputs.
Represent uncertainty explicitly.
Your code controls what a model is allowed to execute.
Jev is developed by TypeSafe AI. Valori × Jev describes an integration architecture and does not imply an official partnership or endorsement.
Build AI systems where memory persists, relationships survive beyond a context window, decisions expose uncertainty, and important actions can be traced back to the state that produced them.
Valori provides the memory.Jev provides the decision.Your code provides the authority.