Developer workload access

Test Jylus with your workload.

Send logs, OpenTelemetry, Docker events, telemetry or awkward JSON. Measure ingest, retrieval and proof-bound Context Packs with your own data.

Get a free API key →Read developer docs

Using an AI coding agent?Give it the Jylus quickstart and let it wire the first event, query and Context Pack into your application.

Open agent quickstart

No credit cardNo sales callYour data stays yoursNever used to train general-purpose AI

AI / AGENTS

Test better evidence.

Ingest evidence, generate a proof-bound Context Pack, then compare model input and answers on your workload.

Test AI evidence AI quickstart

DATA / REAL-TIME SYSTEMS

Push the data engine.

Send events, search retained history and test mixed structured, semantic and relationship queries.

Test the data engine API quickstart

Production proven1.8B events1M+/sec0 failures100% strict accuracy / 528 trials98.77% less model inputMethodology →

Free storage1 GB

Ingest capacity1,000 events/sec

Data regionSydney, Australia

AccessHTTPS API

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/analyze with 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.

Generate your first Context Pack →

FULL CONTEXT205,129 tokens

78.79% strict accuracy

CONTEXT PACK2,532 tokens

100% strict accuracy · 100% evidence recall

Same model. Same 528 questions. Better evidence.0% scored hallucination trials across the 528 frozen Jylus evaluations. Results apply to the defined benchmark.

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.

Create free workspace →

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 }
    }]
  }'