Turn video, audio, images, transcripts, and supporting documents into structured, searchable intelligence. Mimasa AI provides a governed content intelligence platform that identifies scenes, speakers, topics, entities, objects, on-screen text, and other metadata at scale.
Connect the results to review, cataloguing, reporting, and downstream workflows. Keep people in control with confidence thresholds, role-based access, human approvals, and audit trails.
Built for broadcasters, OTT platforms, studios, publishers, sports-media teams, production houses, and enterprise media archives.
Media libraries contain far more information than their filenames and manually entered descriptions reveal. Important people, brands, topics, locations, spoken references, objects, scenes, and on-screen text can remain hidden inside hours of content.
Manual viewing and tagging are difficult to scale. Metadata can also vary across teams, suppliers, titles, and systems. This makes content harder to organise, review, reuse, analyse, and activate in operational workflows.
Mimasa AI combines multimodal AI, configurable rules, and human review to perform media content analysis across video, audio, images, transcripts, and documents. It turns extracted information into a consistent content record that teams and approved AI agents can use.
Video content analysis uses AI to interpret the visual, audio, speech, and text signals within a video. Instead of treating a programme or clip as one opaque file, the system can create time-based information about what appears, what is said, who is speaking, and how the content changes from scene to scene.
Mimasa extends AI video content analysis across related assets. Unlike isolated AI video analysis, a video can be evaluated together with its transcript, synopsis, script, production notes, image assets, content identifiers, and business metadata. This wider context helps teams create more useful records than a single model or isolated tagging tool can provide.
The result is not merely a list of labels. It is governed intelligence that can support cataloguing, editorial review, content operations, analysis, archive enrichment, and agentic automation. Video understanding AI becomes more valuable when its evidence and outputs can move through a controlled business process.
Identify scene boundaries, visual changes, recurring sequences, and meaningful segments. Create timestamped descriptions that help teams navigate long-form programmes, clips, interviews, news footage, sports content, and other video libraries.
Convert speech to time-aligned text, distinguish speakers where supported, and extract topics, phrases, and named entities from transcripts. Use the transcript as another governed data source for analysis and workflows.
Detect relevant visual elements based on the configured use case. Apply thresholds and review rules when a result is uncertain or when the identification of a person, brand, or location requires human validation.
Extract titles, captions, labels, credits, graphics, and other visible text through OCR. Connect this information with scripts, briefs, cue sheets, schedules, or other documents associated with the asset.
Generate concise summaries and structured descriptions for programmes, episodes, clips, and segments. Use organisation-specific prompts and fields so outputs match the needs of editorial, archive, production, or commercial teams.
Map content to approved genres, subjects, collections, taxonomies, and custom attributes. Configure required fields, confidence thresholds, validation rules, and reviewer queues rather than accepting every model output automatically.
Connect assets, analyse every modality, generate metadata, validate results, and activate workflows.
Ingest files or connect approved storage, media asset management, content management, archive, API, database, and document sources. Preserve source identifiers and relevant relationships between assets.
Apply visual analysis, speech processing, OCR, transcript analysis, language models, and configurable extraction steps. Select the models and pipeline appropriate to the content, deployment, and governance requirements.
Convert raw outputs into defined fields, timestamped events, summaries, entities, topics, and classifications. Normalise values against approved vocabularies and business rules.
Check required fields, confidence levels, duplicates, and policy conditions. Route exceptions or sensitive findings to the appropriate reviewer and retain the decision history.
Write approved metadata to connected systems, make it available to authorised search and analytics experiences, generate reports, or trigger a downstream agentic workflow.
Mimasa AI combines content analysis with workflow orchestration. Teams can configure reusable pipelines instead of transferring outputs manually between isolated AI tools.
Analyse video, audio, images, transcripts, subtitles, scripts, spreadsheets, and documents within one coordinated workflow.
Data ExtractionDefine the metadata your organisation needs, including required fields, labels, formats, vocabularies, and relationships. Avoid forcing every content type into one generic template.
Ask approved questions about processed assets and metadata using natural language. Follow the answer back to the supporting content record, timestamp, or source where available.
Build agents that monitor incoming assets, run the relevant analysis steps, validate outputs, create review tasks, notify teams, and update connected systems within configured permissions.
Agentic Workflow AutomationSend low-confidence, sensitive, or policy-defined results to reviewers. Capture edits, approval status, comments, identity, and timestamps for accountability.
Generate content inventories, exception reports, analysis summaries, dashboards, spreadsheets, or presentations from approved information.
Data Visualisation And DashboardsProcess programmes, episodes, promos, and clips to propose titles, descriptions, topics, entities, scenes, and structured attributes. Validate the results before synchronising them with downstream catalogues or content systems.
Create baseline metadata for legacy footage with incomplete or inconsistent descriptions. Prioritise assets and fields for review instead of asking teams to watch every item from beginning to end.
Create the timestamped metadata and embeddings required to make media easier to retrieve later. This page handles the understanding and indexing layer; the dedicated discovery workflow handles natural-language retrieval.
Explore Media Archive SearchExtract references to people, organisations, places, events, and topics. Help authorised teams review relevant segments and supporting transcripts without relying only on memory or file naming.
Identify configured segments, participants, logos, speech, and visual cues in recorded footage. Apply sport- or event-specific taxonomies and review rules rather than claiming universal automatic highlight accuracy.
Combine analysis results, source data, reviewer decisions, and version history in a traceable record. Make approved fields available to operations, analytics, and AI agents according to access policy.
A standalone video analysis AI tool may return tags or a transcript. Mimasa AI can use those results as inputs to a governed business workflow.
For example, an incoming programme can be analysed for scenes, speech, entities, and on-screen text. The platform can map the results to a defined taxonomy, flag missing or low-confidence fields, assign a review task, record the approved corrections, update a connected catalogue, and include the asset in an operational report.
Each action follows configured permissions. Editorial, legal, compliance, or commercial decisions can remain behind explicit approval checkpoints.
“Which assets in this collection are missing an approved synopsis or genre?”
“Where is a specified person, organisation, product, or location mentioned?”
“Which clips contain both a spoken reference and matching on-screen text?”
“Which metadata proposals are below the required confidence threshold?”
“What content arrived this week, and which items are still awaiting review?”
“Which assets could not be mapped to the approved taxonomy?”
Mimasa AI is an intelligence and automation layer. It does not need to replace the systems that already store, manage, publish, or monetise content.
Connect approved sources through available APIs, databases, file stores, object storage, document repositories, or secure data pipelines. Depending on the target system and permissions, Mimasa can read assets and metadata, prepare structured outputs, initiate reviews, or write approved results back.
Media assets can include unreleased productions, licensed content, personal data, commercial agreements, and proprietary archives. Content intelligence therefore requires more than model accuracy.
Mimasa AI supports enterprise controls around who can access content, which models and workflows can process it, what an agent may do, and when a person must approve the result.
Specific retention, residency, privacy, and security controls are confirmed during solution design. Read more about Data Governance.
Let AI prepare structured metadata and route the work that needs judgement to people.
Apply shared schemas, taxonomies, validation rules, and approval processes across teams and content sources.
Reveal information within assets that filenames and shallow descriptions cannot capture.
Connect analysis, validation, review, reporting, and system updates in one coordinated workflow.
Use confidence thresholds, reviewer queues, permissions, and audit trails for sensitive or consequential decisions.
Configure repeatable pipelines and agents for recurring content types, collections, and operational requirements.
Mimasa combines multimodal content understanding with data intelligence, workflow automation, and human approvals. Analysis can lead to an accountable next step instead of ending as an isolated output.
Select an architecture based on the use case, media sensitivity, infrastructure, and governance needs. Deploy in the cloud, private cloud, or on-premise where supported.
Define the fields, vocabulary, validation, routing, and approvals that fit the organisation. Avoid rebuilding operations around a rigid off-the-shelf workflow.
Use approved content metadata alongside audience, campaign, operational, rights, and revenue data in other Mimasa workflows.
Common questions about video content analysis, automated content classification, and governed media workflows.
Start with one representative content set and one high-value workflow. Define the metadata, taxonomy, review rules, integrations, and success measures before scaling across the library.
Looking for live-camera monitoring instead? See AI Video Analytics.
