EINBLIQ.IO for Streaming Services
Automated Detection, Root-Cause & Remediation
Find anomalies before viewers notice.
Understand what broke and steer fixes automatically (Slack, Jira, Teams).
Why typical “streaming analytics” fall short
Getting signals in is hard
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Analytics SDKs need bespoke integration across many platforms and device versions.
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Every extra SDK means maintenance, regression risk, and gaps on long-tail devices.
Our way: CMCD moves key player telemetry into standard request fields, drastically reducing custom SDK work across platforms. Player support exists today (e.g., dash.js) and keeps expanding.
Even with data, ops gets noisy
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Data is hard to interpret across encoders, packagers, CDN paths, ISPs, devices.
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Manual thresholds rot; they miss outliers and flood you at peak.
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External portals require constant watching → alert/dashboard fatigue.
Our automation flow: pattern detection → issue isolation → agentic investigation → root-cause hints → targeted tickets with clear fix steps (or auto-fix via API, e.g., trigger re-packaging).
And when delivery is the lever, we can steer traffic in real time (multi-CDN, e.g., via Content Steering).
How EINBLIQ.IO works
Observe (360°)
Standards-based CMCD from the player + client/server/CDN telemetry give end-to-end context without heavy custom SDKs. (CMCD is CTA-5004; adoption is growing across ecosystems including dash.js and DVB work.)
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Detect & diagnose (explainable)
ML highlights outliers at the granularity that matters (device model + OS + app + codec + CDN path + region/ASN).
For each cluster, a lightweight AI agent auto-investigates: it pulls the right slices, runs baseline and change-point checks, and cross-references recent releases.
Each incident then carries root-cause hints with confidence, scope/blast radius, and recommended actions, so teams can move straight to fixing instead of digging.
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Remediate (close the loop)
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Human-in-the-loop: auto-generated tickets with concrete steps (packager setting to change, CDN mapping to adjust).
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Hands-off (optional): trigger fixes via API — e.g., re-package broken assets — or apply QoE-aware steering using HLS Content Steering / multi-CDN logic.
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Case study example with ARTE
Illustrative demo: synthetic data, fictional vendor; real pattern, no PII; trademarks and logos belong to their owners – no endorsement implied.
What you get
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Faster MTTR, fewer firefights: Outliers surfaced before complaints pile up.
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Action over dashboards: Engineers spend time fixing, not digging.
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Lower OPEX: Less manual triage; fewer war rooms.
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Flexibility: Keep your existing players/CDNs; we integrate with your workflow tools.
Built on open standards (trust by design)
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CMCD (CTA-5004): portable player telemetry; reduces custom SDK burden.
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HLS Content Steering: standard mechanism to prioritize/shift pathways (multi-CDN).
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Data responsibility: aggregated signals, no PII, GDPR-aligned minimization.
Innovate without risk
Standards-first, quick start
Leverage CMCD with your existing players and CDNs. No heavy platform-by-platform SDK rollout.
Fully backwards-compatible: 1-line integration adaptors available for legacy platforms like HbbTV 1.x
Privacy you can show
Privacy-first and GDRP-compliant analytics only, no PII.
Controlled automation
Start in shadow mode, then canary with approvals. Every action is transparent and reversible.
Explainable, in your workflow
Each incident ships with root-cause hints and concrete fix steps. Tickets go to Slack, Jira, or Teams; APIs can re-package content or apply QoE-aware steering.