Match the right model to every workflow and
Streaming services, publishers, and studios generate content at a scale where ungoverned AI spend becomes a margin problem before it becomes a compliance one. Igris matches the right model to each content workflow, tracks spend per project, holds moderation pipelines within cost bounds when content volume spikes, and keeps unreleased material inside your network.
AI Assisted Content Production
A media company running LLMs across scriptwriting, article drafting, and social content output faces a cost structure that is invisible without per-project tracking. Different content types have genuinely different model requirements: a quick social draft needs responsiveness, not the full reasoning capability of a premium model. Without model routing policy enforced at the gateway layer, every content type routes to whatever model the integration defaults to — typically the most capable and most expensive. Automated content bots producing social output pipelines accumulate cost per post without any single interaction being obviously excessive. The budget erodes steadily, without a triggering event, until the project allocation is gone before the campaign is.
Each content project gets its own Igris connection with model restriction policies that define which models are permitted for which content types. Social drafts route to inexpensive generation models by policy. Long-form editorial and script work routes to premium models. Neither can be accidentally overridden by a library update, a new integration environment, or an individual contributor using a personal API key. Igris Lens tracks cost per project connection in real time. Rate limiting on automated content bot connections prevents any single pipeline from consuming beyond its defined hourly allocation. For content that includes identifiable individuals, Guard PII redaction policies apply GDPR Article 5 and DPDP Act 2023 protections before the call leaves the network.
Premium models run only where they are needed, enforced by policy rather than remembered by convention. Every project's AI spend is visible against its budget in real time. Automated content bots cannot quietly exhaust their allocation between check-ins. When the end-of-project financial review asks what AI cost this campaign to produce, the answer is a Lens report per project.
Content Moderation Pipeline
A platform moderating user-generated content with LLMs operates at throughput that has no reliable ceiling. When content goes viral, moderation volume can multiply by an order of magnitude within hours. Without rate limiting, the pipeline consumes whatever provider capacity is available. Without cost anomaly detection, a growing moderation backlog is invisible until users begin reporting that harmful content is staying live. Without a budget cap, the viral event the Trust and Safety team is managing operationally is simultaneously generating a spend event that no one approved. User-generated content sent to an LLM for moderation may also contain the personal data of the users who posted it — carrying GDPR Article 5 obligations for EU users and DPDP Act 2023 protections for Indian users on every call.
Igris rate limiting holds the moderation pipeline within its defined capacity bounds regardless of how far above baseline content volume climbs. Igris Lens cost anomaly detection fires when moderation spend breaks its expected pattern, flagging both genuine volume spikes and the more dangerous scenario where a growing backlog is queueing content rather than processing it. Error rate monitoring in Lens confirms the pipeline is returning decisions and not silently failing. Budget caps define a ceiling on total moderation spend for any event window. Guard PII policies strip identifiable user information from moderation prompts where not required for the moderation decision, applying GDPR and DPDP Act data minimisation controls at the call level.
The content moderation pipeline handles viral events within its defined cost envelope because the rate limit and budget cap hold regardless of what happens above them. Growing backlogs surface as Lens anomaly alerts before they become Trust and Safety incidents. Personal data in moderated content is governed by GDPR and DPDP Act minimisation controls on every call.
AI Localisation and Translation Pipeline
Media companies using LLMs for localisation at scale process material that is frequently under active confidentiality obligations before its release date. An unreleased streaming original, a pre-launch game title, or an unannounced documentary subject appearing in a localisation prompt sent to an external LLM provider is not a data protection violation. It is a content leak with commercial and contractual consequences that no compliance framework recovers after the fact. Large-scale batch localisation jobs drive token volumes with no natural ceiling without governance at the connection level. Without per-market cost tracking, the production team has no visibility into what each language variant is costing.
Guard custom content patterns catch unreleased project codenames, pre-launch title references, and proprietary production identifiers before any localisation prompt leaves the network. Per-project connection isolation keeps each production's localisation prompts separated. Token limits prevent batch subtitle files from being passed as oversized prompt context. Igris Lens tracks cost per connection, per language market, and per project. Where localisation content includes identifiable individuals, Guard PII redaction addresses GDPR and DPDP Act obligations at the call level.
Unreleased content moves through the localisation pipeline at scale without proprietary project details reaching any external LLM provider. Every project is isolated at the connection layer. Per-market cost data is available without reconciling provider invoices across multiple language runs.
AI Audience Personalisation and Recommendation
Streaming platforms and publishers using LLMs to generate personalised recommendations process subscriber viewing histories, content preferences, and behavioural profiles. Where viewing patterns reveal religious, political, or health-related interests, they may qualify as sensitive categories under GDPR Article 9 and DPDP Act 2023 provisions for sensitive personal data. Without PII redaction at the gateway layer, every personalisation prompt carries a GDPR Article 5 data minimisation exposure and a DPDP Act consent obligation for Indian subscribers. At streaming scale — millions of recommendation calls per day — that is a continuous stream of individual data handling events, each ungoverned.
Guard PII redaction strips subscriber identifiers from personalisation prompts before they reach the LLM — subscriber IDs, viewing record keys, and behavioural profile identifiers are redacted automatically. Rate limiting on the personalisation connection keeps call volume within the defined allocation during peak usage. Lens tracks cost per recommendation connection and surfaces anomalies when spend breaks expected parameters. The audit trail records every personalisation call and every redaction event, providing the GDPR Article 30 records of processing activities documentation and DPDP Act compliance evidence.
Subscriber personalisation runs at platform scale with data minimisation enforced on every call. GDPR and DPDP Act obligations are addressed at the prompt layer. When a data protection authority asks how subscriber personal data is handled in the AI recommendation pipeline, the answer is a governance log.
Bring content AI spend under control
See how media teams enforce model routing by content type, cap moderation cost through viral events, and protect unreleased content.