Contextual
Paid
Individual developers managing local codebases
Jylus
FreemiumAPI
Your agent answered from data that had already changed — and nobody caught it until the incident review. Jylus exists to close that gap, sitting between your live data streams and any model as a dedicated evidence-preparation layer.
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Summary
Jylus ingests streaming data, resolves current and historical state, follows relationships between entities, flags contradictions, and packages the result into a compact Context Pack with proof IDs attached to every retained claim. The vendor reports 98.77% reduction in model input tokens and a 23.6× speed advantage over tuned RAG on defined benchmark workloads — both worth reading the methodology before quoting in a design doc. The tool is not an agent and does not reason; it prepares evidence so your model can. Teams whose workflows need the tool to act on what it finds will need to wire that logic themselves upstream.
Bottom line: Bet on Jylus when your model needs source-backed, freshness-aware context from a streaming operational environment — but if your architecture requires the evidence layer to make routing decisions or trigger actions autonomously, you are building that orchestration yourself on top of this API.
Pricing Plans
Usage-BasedFree / Start free
Free
Try with your data, no account required; usage caps and scoped keys apply
- Limited ingest and queries
- Basic Context Packs
Paid
Custom
Monthly spend cap A$650 with higher throughput and features
- Higher ingest rates
- Full evidence features
View full pricing on jylus.ai →
Pricing may have changed since last verified. Check the official site for current plans.
Find your fit
Before you choose Jylus
More options in RAG Frameworks. Compare pricing, platforms, and features side by side.
AI·rete·RAG
Freemium
Teams needing deterministic rules plus RAG explanations
Pros
- As-of state resolution tracks how data changes over time, so your model reasons from the state that actually existed at the moment of an event — not whatever happened to be in the index when the query ran.
- Proof IDs attached to every retained claim in the Context Pack, so tracing why the model said what it said leads back to a specific source record rather than a vector similarity score.
- Contradiction detection flags conflicts between records before they reach the model, which means the model is not left to silently pick one conflicting fact over another without surfacing the disagreement.
- Missing-evidence reporting makes gaps explicit in the Context Pack, so the model reasons with declared uncertainty rather than false completeness.
- Provider-agnostic evidence API, so swapping the downstream model — from OpenAI to a local deployment, for example — requires no changes to the evidence preparation layer.
Cons
- Jylus does not act on what it finds. Teams building incident-response workflows that need the evidence layer to trigger alerts, route tickets, or call external systems must implement that logic entirely outside Jylus — which means maintaining a separate orchestration layer alongside this API.
- No self-hosted deployment option exists. Teams operating under data-residency regulations or internal policies that prohibit sending operational telemetry to a third-party SaaS will hit this wall before the first integration is complete, and will need to evaluate on-premise alternatives instead.
- The benchmark figures — 98.77% input reduction, 23.6× speed improvement — are measured on the vendor's defined workloads. Teams whose data structures or query patterns differ substantially from those workloads should treat these numbers as directional until validated against their own pipelines.
About
- Platforms
- Cloud API (HTTPS), NATS JetStream
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- September 30, 2026
Best For
Who it's for
- AI models and agents that require source-backed, up-to-date evidence from changing data
- Teams running security, observability or incident-response workloads on streaming data
- Operational decision systems that need explicit state transitions, contradictions and proof IDs
What it does well
- Building compact Context Packs from live state, history and relationships for models and agents
- Ingesting and immediately investigating network, log and telemetry streams for security and observability
- Comparing current conditions with historical evidence for operational decisions while tracking scope and contradictions
Frequently Asked Questions
- Is Jylus free?
- Jylus offers a free option alongside paid upgrades. Check the current allowances and terms before choosing a plan.
- Does Jylus have an API?
- Yes. Jylus exposes a developer API. See the official documentation at https://jylus.ai for details.
- What platforms does Jylus support?
- Jylus is available on: Cloud API (HTTPS), NATS JetStream.
Curated lists that include this category
Your agent answered from data that had already changed — and nobody caught it until the incident review. Jylus sits between live data streams and any model as an evidence-preparation layer.
What Jylus does
Jylus ingests streaming data, resolves current and historical state, follows relationships between entities, flags contradictions, and packages the result into a compact Context Pack with proof IDs attached to every retained claim. The vendor states this produces a 98.77% reduction in model input tokens and a 23.6× speed advantage over tuned RAG on defined benchmark workloads. The tool is not an agent and does not reason; it prepares evidence so your model can. Pricing is usage-based and the service runs on Cloud API over HTTPS or NATS JetStream.
Who it is for / who should skip it
Teams whose models or agents need source-backed, up-to-date evidence from changing data, security and observability groups working with network and telemetry streams, and operational systems that must track state transitions and contradictions will find the proof IDs and as-of resolution useful. Teams that require the evidence layer to trigger alerts or call external systems must build that logic separately. Organizations that cannot send data to third-party SaaS because of residency rules will need to evaluate other options.
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