OptiStreamAI is the AI/ML data quality and observability platform purpose-built for video streaming workloads. We catch silent pipeline failures, schema drift, and anomalies in minutes — not the three weeks it takes your CFO to notice the revenue leak.
Data bugs go undetected for days. Bad recommendations, broken funnels, missing ad impressions — directly impacting ARPU and churn before anyone notices.
Senior data engineers spend nearly half their time chasing data bugs instead of building product. Manual QA does not scale to 100M+ events per day.
Datadog, New Relic, Splunk detect infrastructure failures — not data-quality regressions. Rule-based tools like Great Expectations are brittle on streaming workloads.
Core components validated in production at Paramount Global / Pluto TV. Deploys as a Python library, or as Google BigQuery ML models running inside your data warehouse with zero infrastructure footprint.
Ensemble of Isolation Forest, ARIMA, and Prophet models tuned for time-series streaming data. Catches the regressions that rule-based systems miss.
Declarative quality rules engine. Schema, freshness, completeness, consistency — with self-healing assertions that adapt to upstream changes.
Auto-generates test suites from data schemas. Learns expected ranges, cardinalities, and distributions over a 7-day baseline. No tests to write.
Real-time dashboards, configurable alerts, KPI tracking, root-cause hints. Routes to Slack and PagerDuty. Integrates with Looker and Grafana.
Point OptiStreamAI at a Kafka topic, Kinesis stream, BigQuery table, Snowflake warehouse, or Databricks table. Setup takes <6 hours.
The ensemble model learns your normal patterns over a 7-day window. No manual threshold tuning. No false-positive avalanches.
Real-time anomaly scoring. Confidence intervals. Feature contributions explaining exactly why each anomaly fired.
Pipeline correlation map links the anomaly to the specific code change. Suggested runbook for fast rollback.
Core components — anomaly detection, data validation, AI-driven QA — were built and validated in production at Paramount Global / Pluto TV across BigQuery + GCP infrastructure serving millions of users.
Methodology published in The American Journal of Engineering and Technology: "Utilizing a Scalable AI/ML-Based Data Anomaly Detection Tool in Video Streaming Services."
Alexander Motylev — IEEE Senior Member, ACM Professional Member, former Director of Data Test Engineering at Paramount Global. Featured in AI Time Journal, August 2024.
Directly addresses priorities in Executive Order 14179 (Removing Barriers to American AI Leadership) and OMB M-25-21 (Federal AI use requires data quality assurance).
Every customer relationship begins with a free 14-day technical evaluation against one of your non-production data streams. We deliver a written technical report. Then — and only then — you decide if it's worth a paid pilot.
"At Paramount/Pluto TV I watched senior engineers spend half their week chasing data bugs that no observability tool could see. I built OptiStreamAI because the category of streaming-native data quality didn't exist — and the cost of not having it was measured in millions of dollars of leaked revenue. We're building the platform we wished existed when we needed it."
We'll deploy OptiStreamAI's Anomaly Detection module against one of your non-production data streams and deliver a written technical report. No cost. No PII access. No obligation to extend.
We respond within 24 hours. Or book a 20-min intro directly on Calendly.
We're seeking 1-2 letters from credible voices in U.S. AI/data infrastructure. 30-min briefing, 1-2 page assessment. Email hello@optistreamai.com with subject "industry review request."
Those tools detect infrastructure failures — CPU spikes, memory leaks, container crashes. OptiStreamAI detects data-quality regressions — when your event stream is technically flowing but the values are wrong, missing, or stale. They're complementary, not overlapping.
Those are rule-based DQ tools. You write expectations; they check them. They work well for batch data but struggle on streaming workloads where distributions shift continuously. OptiStreamAI's ML-based detection adapts to your data without manual threshold tuning, and we include a self-healing rules engine for the cases where declarative rules are the right answer.
One non-production data stream. We work against synthetic or staging data — no PII, no customer records, no production access. The technical report at the end documents what we found and how we'd integrate in production.
Yes. OptiStreamAI deploys as a Python library (pip install) integrated into your own pipelines, or as Google BigQuery ML models running inside your warehouse — zero infrastructure on our side. Cloud-agnostic: AWS, GCP, Azure, or hybrid.
SOC 2 Type I attestation is on the roadmap for months 6–12. Type II in months 18–24. During the evaluation period we work entirely on your infrastructure with non-production data, so the SOC 2 timing is decoupled from your pilot.
OptiStreamAI is the proprietary platform of Ronex Solutions LLC, a Florida limited liability company. The platform is independently developed and owned. Core methodology is documented in peer-reviewed research (TAJET 2024).