Test better evidence.
Ingest evidence, generate a proof-bound Context Pack, then compare model input and answers on your workload.
Give your AI the right evidence.
Jylus turns changing records into compact, source-backed Context Packs.
Single-use trial
Drop in data. Get a proof-bound result.
No account is required. Jylus creates an isolated one-time tenant, compiles the result, blocks further access immediately, and sends the tenant through the deletion lifecycle.
Start with a synthetic example:
RESULTYour compiled evidence will appear here.
The response contains source-backed facts, proof IDs, conflicts, missing evidence and the deletion state.
Measured on published test workloads
These benchmark scores are separate from the data you submit in the trial.
Measured native stack50.695% QASPER NDCG@1075.130% QASPER Recall@1036.443% FinQA execution40.048% live TEMPO NDCG@10
Free storage1 GB
Ingest capacity1,000 events/sec
Data regionSydney, Australia
AccessHTTPS API
Push the data engine.
Send events, search retained history and test mixed structured, semantic and relationship queries.
Using an AI coding agent?Give it the Jylus quickstart and let it wire the first event, query and Context Pack into your application.
No credit cardNo sales callYour data stays yoursNever used to train general-purpose AI
Test AI evidence
See what your model gets when the evidence improves.
Use your own current state, history and documents. Jylus returns a bounded Context Pack with facts, timeline evidence, contradictions, freshness and proof IDs.
- 01Send dataUse real records, including irrelevant history and conflicting values.
- 02Ask Jylus for evidenceCall
/api/v1/analyzewith the question your model needs answered. - 03Inspect the Context PackCheck retained facts, proof IDs, token estimate, completeness and execution time.
- 04Compare against your modelRun the same question with your current context path and the Jylus evidence.
QASPER · 1,335 TEST QUERIES50.695% NDCG@10
75.130% Recall@10 · 45.663% MRR@10
FINQA · 1,147 TEST CASES36.443% execution accuracy
31.473% program accuracy · zero model calls
TEMPO · FULL LIVE API COHORT40.048% NDCG@10Measured on the native Jylus stack.QASPER uses the untouched mteb/QASPER test task. FinQA uses the official program executor. TEMPO is the full 1,730-question public-v1 API result. Metrics measure different tasks and are reported separately.
The complete test loop
Ask for evidence. Inspect exactly what your model receives.
The response keeps facts, temporal evidence, contradictions, missing evidence and proof IDs explicit. Efficiency values are measured from your own request.
CONTEXT PACK REQUESTPOST /api/v1/analyze
curl https://api.jylus.ai/api/v1/analyze \
--request POST \
--header "Authorization: Bearer $JYLUS_READ_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"source": "current",
"text": "What is the latest latency reported by edge-17?",
"vector": {
"text": "latest edge device response time"
},
"context": {
"mode": "decision",
"token_budget": 2000,
"max_facts": 18,
"max_timeline": 12,
"include_documents": false
},
"scan_limit": 1000,
"limit": 24,
"include_results": false
}'ABRIDGED EXAMPLE RESPONSEapplication/json
{
"success": true,
"complete": true,
"execution_mode": "native_current",
"context": {
"decision_context_id": "ctx_...",
"decision_readiness": {
"status": "ready",
"can_answer": true
},
"facts": [
{
"statement": "edge-17 reported 12.4 ms latency",
"proof_ids": ["evt_001"]
}
],
"timeline": [
{ "observed_at": "2026-08-21T08:00:00Z", "proof_ids": ["evt_001"] }
],
"contradictions": [],
"missing_evidence": [],
"context_efficiency": {
"source_tokens_estimated": 1842,
"retained_evidence_tokens_estimated": 436,
"estimated_token_reduction_percent": 76.33
},
"proof": { "event_ids": ["evt_..."], "complete": true }
}
}Test the data engine
From account to a real event in minutes.
- 01Create a free workspaceEmail, GitHub or Google. No payment step.
- 02Copy the one-time API keyThe key is scoped and shown once.
- 03Send your real payloadThe Developer hub includes HTTPS, NATS JetStream, Docker and OpenTelemetry setup.
FIRST EVENTcurl
curl https://api.jylus.ai/api/v1/events \
-H "Authorization: Bearer $JYLUS_API_KEY" \
-H "Idempotency-Key: try_evt_001" \
-H "Content-Type: application/json" \
-d '{
"stream": "my-workload",
"events": [{
"id": "evt_001",
"type": "telemetry.sample",
"occurred_at": "2026-08-21T08:00:00Z",
"data": { "device": "edge-17", "latency_ms": 12.4 }
}]
}'