Analyse video, images, audio, transcripts, on-screen text, descriptions, and related records. Mimasa AI helps media teams apply configurable policies, prioritise exceptions, and move every sensitive decision through a traceable content approval workflow.
Go beyond isolated content moderation tools. Build AI agents that prepare reviews, assign cases, monitor queues, generate compliance intelligence, and execute only the actions permitted by your policies and approvals.
Cloud, private-cloud, or on-premise deployment for broadcasters, OTT platforms, studios, publishers, agencies, and enterprise content teams.
Media teams review content across files, inboxes, content systems, and external partners. Policies vary by platform, market, audience, and content type. Evidence and decisions are often difficult to trace afterwards.
AI content moderation can screen large volumes, but a label does not complete the work. Teams must examine the evidence, apply the correct policy, document a decision, and coordinate the approved next step.
Mimasa connects analysis, policy checks, people, agents, and enterprise systems in one content moderation solution. AI moderation handles repeatable preparation and routing. Authorised reviewers retain control where context, rights, or compliance obligations matter.
AI content moderation identifies content that may require classification, restriction, review, warning, editing, or escalation. It examines visuals, speech, written language, on-screen text, metadata, and their relationships.
Mimasa adds an operational layer around the analysis. It structures potential findings, evaluates configured rules, creates reviewer-ready cases, orchestrates a content moderation workflow, and preserves the outcome for reporting and audit.
The system is not the final authority on legality, editorial acceptability, age rating, or platform suitability. Outputs can be incomplete or wrong. Mimasa therefore supports confidence thresholds, human review, escalation, and approval controls.
Signals rarely appear in isolation. Mimasa coordinates them through configurable Data Extraction so reviewers see the full context of a case.
Review scenes, visual objects, actions, dialogue, transcripts, on-screen text, and associated metadata within one process. Return reviewers to the relevant timestamp so they can evaluate the surrounding context before deciding.
Detect configured visual classes, logos, products, objects, people, or scene conditions that may require attention. Apply organisation-specific thresholds rather than assuming one universal definition of acceptable content.
Analyse available speech, transcripts, speakers, terms, topics, and audio-derived signals. Coordinate language-specific or specialist review when meaning, tone, or context cannot be resolved reliably through automation.
Screen transcripts, titles, captions, descriptions, subtitles, comments supplied with an asset, and supporting documents for defined categories, terms, entities, or policy conditions.
Extract and evaluate visible words in captions, graphics, signage, credits, product packaging, and other relevant frames. Connect the finding to its source image or video timestamp.
Evaluate how visual, spoken, and written signals relate. A term, object, or action may require different treatment depending on its surrounding scene, editorial purpose, audience, and policy.
Need metadata, summaries, and classification instead of policy screening? See Media Content Intelligence.
Mimasa adapts to the organisation's review process rather than imposing one universal moderation policy. Orchestration is configured in Workflow Builder.
Receive assets and related records through approved storage, content systems, APIs, databases, upload channels, or secure data pipelines. Preserve identifiers, versions, sources, and available relationships.
Run the configured visual, audio, speech, OCR, transcript, metadata, and multimodal analysis steps. Convert potential findings into structured categories, timestamps, evidence, and confidence information.
Evaluate the results against customer-defined categories, required fields, confidence thresholds, risk levels, markets, audiences, platforms, or other business conditions.
Assign the item to an authorised reviewer or specialist queue. Show the relevant content, policy, evidence, source, priority, deadline, and available actions.
Capture the reviewer's decision, changes, comments, identity, and timestamp. Require additional approval for sensitive categories or consequential actions where configured.
Notify a team, request an edit, add an approved classification, update a connected record, generate a report, or initiate another authorised workflow.
Track volumes, categories, queues, exceptions, turnaround, escalations, overrides, and policy patterns. Use approved data to refine operational rules and training—not to remove necessary human judgement.
Intake, analysis, policy checks, reviewer queues, approved action, and audit run as one measured process rather than seven disconnected steps.
A content moderation platform should do more than return a category. Mimasa combines analysis with AI agents, workflow orchestration, analytics, and governance.
Build agents that monitor approved sources, run required checks, validate evidence, create cases, assign owners, send reminders, prepare summaries, and initiate approved actions.
Agent BuilderConfigure categories, conditions, confidence thresholds, queues, approvers, escalation paths, and actions around each content type and operating context.
Organise AI content review by priority, policy, market, language, source, content type, risk, or deadline. Restrict sensitive cases to authorised roles.
Present available timestamps, transcript passages, visual findings, text, metadata, rules, and confidence information that contributed to the case.
Data ExtractionAsk questions in natural language. Build dashboards and reports for case volume, categories, status, turnaround, escalation, reviewer workload, model-human disagreement, and recurring policy patterns.
Data Visualisation and DashboardsMaintain a traceable record of source data, analysis, reviewer decisions, changes, approvals, and agent actions. Generate compliance summaries, exception reports, spreadsheets, and presentations from authorised information.
Monitor recorded or incoming broadcast content against configured editorial, advertising, language, disclaimer, or scheduling policies. Create timestamped cases and route potential exceptions to broadcast, editorial, legal, or compliance reviewers. Mimasa supports broadcast compliance monitoring as an analysis and workflow layer. It does not make legally binding compliance determinations.
Apply different review rules by catalogue, market, language, platform, audience, or content type. Coordinate classifications, warnings, approval states, and exception handling before authorised distribution steps.
Review cuts, trailers, promos, images, transcripts, and supporting records. Assign editorial, legal, brand, or commercial reviewers and retain decisions across versions.
Evaluate configured products, logos, terms, themes, and visual contexts. Route potential brand, sponsorship, or placement concerns for human judgement before an approved action.
Screen video, images, audio, titles, captions, and supporting text. Coordinate editorial review and maintain a consistent decision record across teams.
Analyse selected legacy collections against a current policy or metadata requirement. Prioritise likely exceptions for review without claiming that automation can certify the entire archive.
Organisations searching for content moderation services may be comparing outsourced teams, software platforms, or a blended model.
Mimasa AI is not an outsourced moderation agency. It provides analysis, agentic automation, analytics, and governance for internal teams or authorised service partners. The same applies to video moderation software decisions: Mimasa supplies the workflow layer, not the review labour.
Consider a programme with a scene that may require an audience warning. Mimasa can locate it, extract available dialogue and on-screen text, evaluate policy conditions, and create a reviewer-ready case.
An agent can assign the case, monitor its deadline, and notify the owner. The reviewer can examine the context, edit the proposed classification, comment, approve, or escalate.
After approval, another authorised step can update metadata, request an edit, generate an exception record, notify distribution teams, or prepare a compliance report. Every action remains subject to roles, permissions, and configured approval rules.
This is the difference between detecting a possible issue and operating a complete content approval workflow.
“Which video assets contain unresolved high-priority review cases?”
“Show potential policy exceptions identified in this week's broadcast content.”
“Which items are waiting for legal or editorial approval?”
“What categories generate the most human overrides?”
“Which cases are approaching their review deadline?”
“Show audio-related findings awaiting a language specialist.”
“Prepare a weekly exception report with source links and approval status.”
“Assign unowned medium-priority cases to the authorised review queue.”
The final examples demonstrate that Mimasa moves from analysis to reporting and controlled action. Evidence retrieval across older material is covered by AI Video Search for Media Archives.
Mimasa is an intelligence and automation layer. It does not need to replace content-management, media-asset-management, archive, editing, publishing, or case-management systems.
Connect approved sources and destinations through available APIs, databases, object storage, repositories, file transfers, or secure data pipelines. Depending on access and system capabilities, Mimasa can read content and metadata, store derived findings, initiate review tasks, return source references, or write approved results back.
Content moderation can affect expression, reputation, contractual obligations, audience safety, distribution, and revenue. A content moderation software platform must therefore support accountable decisions—not simply maximise automation.
Specific legal, regulatory, privacy, content-rights, retention, residency, and security requirements must be evaluated during solution design. Software use alone does not establish compliance. Read more about Data Governance.
Use automated content moderation to prepare and prioritise cases so reviewers can concentrate on content requiring context and judgement.
Apply shared policies, evidence requirements, queues, roles, and approval stages across teams, markets, and content types.
Retain the source, finding, policy, reviewer, comments, changes, decision, and approved action in one workflow record.
Move from analysis to tasks, notifications, metadata updates, remediation requests, reporting, and other authorised processes.
Analyse volumes, categories, turnaround, exceptions, overrides, workload, and emerging policy trends through natural-language questions and dashboards.
Select cloud, private-cloud, or on-premise architecture based on content, infrastructure, integration, and governance requirements.
Evaluate visual, audio, speech, transcript, OCR, metadata, and document signals within one coordinated workflow.
Define when AI can prepare or route work and when an authorised person must review, edit, approve, reject, or escalate.
Use agents to monitor sources, validate information, prepare cases, manage queues, generate reports, and execute approved next steps.
Move beyond individual cases to understand moderation patterns, workload, exceptions, turnaround, and decision consistency.
Use roles, permissions, audit trails, lineage, approvals, and flexible deployment patterns for sensitive media operations.
Common questions about AI content moderation, approval workflows, and governed review operations.
Start with one content type, one policy set, and one approval process. Define the categories, evidence, thresholds, reviewer roles, escalation rules, integrations, and success measures before expanding across the organisation.
Monitoring live cameras and operational sites instead? See AI Video Analytics.
