AI engineering for media, broadcast, and telecom

Media and telecom workloads share a shape few other industries hit at the same scale: every cent on the per-stream, per-frame, per-packet cost line matters; the reliability bar is set by the customer's perception of "did it just glitch?"; and the failure modes are operational, not catastrophic: a slow regression that bleeds margin or a noisy alert pipeline that exhausts the on-call.

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Where the Engineering Bottleneck Lives

A video, encoding, or media-AI pipeline runs too expensive per stream, per minute, or per frame at the volumes the business now runs, and a new device or codec target makes the budget worse.

Or a content-quality, routing, or classification system needs an eval harness the operations team can actually run, not a research-only metric, and an operational-anomaly system (network telemetry, infrastructure metrics, stream-health) is producing noisy alerts that need structured evaluation against historical incidents.

Video & Media-Pipeline Cost

Streaming, encoding, and media-AI workloads compound: a small per-frame win is serious margin recovery at production volume, and a small loss is the line that breaks unit economics on a new device or codec target. We profile the pipeline first (codec choice, GPU kernel paths, batching, scheduling, target-specific runtimes) and move the per-stream cost line on the workload you actually run.

Lands in the Inference Cost-Cut Pack: 4–8 weeks, milestone or fixed-price.

Content-Classification System Evals

Content-classification and content-quality systems (the surface behind moderation, routing, and policy-driven handling) have the same production-AI failure modes as any classifier: silent drift, slice-level regression, ungated model swaps, and harnesses that grade the model on the wrong distribution. We build the eval harness, slice-level monitoring, drift gates, and release runbook your operations team can rerun.

Lands in the Production AI Monitoring Harness: 4–10 weeks, milestone or fixed-price.

Operational-Anomaly Detection

Network telemetry, infrastructure metrics, stream-health signals, and operations-side anomaly pipelines fail in a specific way: the detector is fine in isolation, but the alert volume and false-positive economics make it unworkable on the team's actual workflow. Detector quality is a calibration and evaluation problem, not just model accuracy. We build the structured evaluation against historical incidents, the slice-level breakdown by signal type, and the gating that ties detector behaviour to alert-pipeline cost, on your own infrastructure, not surveillance of people.

Lands in the Production AI Monitoring Harness: 4–10 weeks, milestone or fixed-price.

How We Work With Media & Telecom Teams

Each pack has a fixed scope and a price tied to the outcome, and ends in something your team keeps and can re-run: a benchmark replay, an eval re-run script, a deployment runbook. If your question does not match a pack, we say so.

Heading into a pipeline cost review, a content-system eval, or an operational-anomaly rollout? The named pack page is the entry point, or contact us and we will route you to the right one.

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