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Building an AI feature shouldn’t mean building the whole data layer.
Jylus ingests your data, connects records and prepares source-backed context for your model.
Here’s the workflow on screen.
Try your own records:
jylus.ai/try
AI can now write your integrations, build your pipelines and generate your applications.
But can your AI agent reliably determine what's true across constantly changing data?
That's the infrastructure problem Jylus is built to solve.
Real-time state. Connected evidence. Reliable AI context.
jylus.ai
How much of your agent bill is spent reading irrelevant data?
Our frozen 528-question test, same model.
Average input tokens/query:
Full context: 205,129
Jylus: 2,532
Strict accuracy: 78.79% → 100% on this test.
jylus.ai/benchmark-methodolo…
Can your AI tell “false” from “we don’t have the evidence”?
No approval record doesn’t mean rejected. No incident report doesn’t mean nothing happened.
Jylus flags missing evidence in the Context Pack so the model can qualify its answer or ask for what’s missing.
Try it with your own records. Ask a question the data cannot answer, then inspect the missing-evidence fields in the Context Pack: jylus.ai/try�
Why pay your AI to read 205,129 tokens when 2,532 scored higher?
Our frozen 528-question test: Full context: 78.79% strict accuracy Jylus: 100% observed
Same model. Same settings.
Your hardest question. Your data. Try it: jylus.ai/try�
Your AI agent can now connect directly to Jylus via MCP.
No custom API integration. Just connect and go.
Give Codex, Claude, Cursor and other MCP-compatible agents access to temporal state, verified evidence and compiled context.
Not another RAG wrapper. An evidence layer for AI.
jylus.ai
#AI #MCP #AIAgents
9:00 — Approved.
9:07 — Approval revoked.
9:10 — Your AI agent executes.
Which version of reality did it use?
Yesterday, OpenAI shared its work with Ironclad on training AI agents to handle complex contracting workflows, including agreements, approvals and business rules.
That raises a question every team deploying agents should ask:
What happens when the evidence changes halfway through the job?
Imagine an agent processing a supplier payment.
The invoice is genuine.
The original approval is genuine.
The agent can cite both.
But the supplier was placed on hold three minutes ago.
Every document it retrieved could be authentic. The action could still be wrong.
Before an agent acts, it needs to establish:
• What is valid at the time of the decision?
• Has anything superseded the original approval?
• Are there conflicting records?
• What evidence supports proceeding?
This is what Jylus does.
It resolves changing state and relationships, flags conflicts and missing evidence, and compiles source-backed context for the model to reason from.
As agents gain the ability to move money, change access and update systems, the age of their evidence becomes a business issue.
Here’s a useful test for your next agent demo:
Change an approval halfway through the task.
Does the agent catch it?
Try Jylus with your own data: jylus.ai/try
#AgenticAI #EnterpriseAI #Jylus
Your AI cites the source.
The source is real. The quote is accurate. The answer is still wrong.
The record was superseded yesterday.
A bigger context window does not fix that. It just fits both versions.
If you ship RAG: where does version get chosen — before retrieval, after retrieval, or inside the prompt?
We resolve the valid state before the model reads, and leave the old record as history with a proof id.
jylus.ai/try
205,129 input tokens → 2,532.
Accuracy: 78.79% → 100% observed.
Same model, same 528 questions in our internal benchmark.
Jylus changed the evidence the model received.
How much context does your AI actually need?
jylus.ai/benchmark-methodolo…
Built the AI feature. Now you're building the backend that decides what data it should see.
That's the work Jylus handles: turning your records into compact, source-backed evidence for your existing model.
Try your own data:
jylus.ai/try
What could your AI have known when it made a decision?
Same effective date. Different knowledge cutoff.
Jylus returns Normal before a correction was known, Warning afterwards—with source proofs.
Built an interactive demo. Feedback welcome:
news.ycombinator.com/item?id…
Your AI cites its source.
The source is real.
The quote is accurate.
The answer is still wrong.
The record was superseded yesterday.
If you're shipping RAG: where does your stack decide which version is valid—before retrieval, after retrieval, or inside the prompt?
This is what I'm testing with Jylus: resolving current and historical state before the model reasons, with corrections and source proofs preserved.
Bring a messy example. I'd like to see where it holds up and where it doesn't.
jylus.ai/try
Retrieval found plenty. What should your model actually read?
Jylus prepares compact, proof-bound Context Packs from live state, history and relationships.
Test a real question against your own data. Start free, no card.
jylus.ai/try?utm_source=x&ut…
Your logs captured the incident. Can your AI trace the evidence?
Jylus turns your data into Context Packs with facts, timelines and proof IDs.
Try your own logs or OpenTelemetry data. Start free, no card.
jylus.ai/try?utm_source=x&ut…
A confident answer still needs a source.
Jylus prepares Context Packs from live state, history and relationships, with proof IDs and missing evidence kept explicit.
Build your first integration with the developer quickstart. Free workspace, no card.
jylus.ai/docs?utm_source=x&u…
