Turn one bulky log into protected AI-ready JSON.

Upload one file, or run the sample, to compare bulky raw input with a protected JSON result you can test in your preferred LLM.

Early hosted preview

This page runs on a secured preview backend. It is free while we validate the one-file workflow. Paid one-file processing and monthly plans will be added later.

Do not upload real secrets, customer data, regulated data, or confidential incident records during this early preview.

Hosted preview limit: 25 MB per file. This one-file preview is tuned for quick evaluation. Larger logs need chunking, background processing, or a private workflow trial.

Ready.

Raw Input0bytes

Protected JSON0bytes

Protected Values0values hidden

Evidence Items0found

Receipt-auditability

Result Summary

Suggested LLM prompt

Using only this protected JSON, summarize the likely incident, rank possible causes, and tell me what context I should inspect next.

One file helps you troubleshoot. The Fabric helps your organization remember.

This preview turns one bulky log into protected, AI-ready incident evidence. In an enterprise deployment, ContextECF Fabric connects logs, CI failures, support tickets, PRs, agent runs, and prior outcomes into governed institutional memory.

That means your team does not just analyze the same problem again and again. The Fabric remembers what evidence mattered, what context was missing, what fixes worked, and what should be inspected first next time.

Normalized Context Events

Messy operational evidence becomes reusable event records: error signatures, affected services, deployment markers, context gaps, and recommended next evidence.

Institutional Memory

Validated lessons can persist across teams, repositories, agents, and time so future troubleshooting starts smarter.

Context Sufficiency

The Fabric can tell whether available evidence is enough for triage, action, or root-cause confidence.

Governed AI Context

Before evidence reaches an LLM or agent, redaction, policy boundaries, denied capabilities, receipts, and audit trails can apply.

Based on your file analysis: what the Fabric could remember

Pattern found in this file: Repeated webhook delays appeared in the log. What the one-file preview can say: Webhook delay evidence was detected. More context may be needed. What the enterprise fabric can learn over time: When webhook delays appear after deployment, the highest-value next evidence is usually: 1. deployment diff 2. APM trace around first_seen timestamp 3. gateway or dependency latency logs 4. retry queue depth Why it matters: Future incidents can start with a better evidence checklist instead of repeating discovery from scratch.

Want to test this on real workflows?

Leave your email and tell us what kind of logs or incident workflow you want to try. We will follow up about private processing, local SDK options, or enterprise fabric evaluation.