Executive Summary
OntoVision is an AI-native SaaS platform that automates the most time-consuming, repetitive parts of professional video post-production: structuring large footage libraries and assembling narrative-ready rough cuts for film, documentary, series, and recurring formats.
By combining multimodal Large Language Models (LLMs) with visual understanding, speech, and audio analysis, OntoVision transforms unstructured video archives into semantically searchable assets and generates brand-consistent, editable rough cuts in minutes instead of hours.
Designed for European broadcasters, production companies, in-house media teams, and sports organizations, OntoVision delivers up to 70% reduction in production time and up to 90% reduction in repetitive editing workload, while preserving full creative control.
The platform is built and hosted in the EU, with GDPR-aligned data management and infrastructure on European cloud providers.
1. Market Context and Motivation
The demand for high-quality video content has never been greater. Broadcasters, streamers, brands, and public institutions publish across linear TV, streaming platforms, mediatheques, social media, and internal communication channels — often with shrinking budgets and unchanged post-production capacity.
At the same time, each production day easily generates one terabyte or more of raw footage, of which only a small fraction is ever used due to manual bottlenecks in review, tagging, and assembly. This imbalance between exploding footage volume and limited editorial time has turned post-production into a structural bottleneck for the European media and film industry.
2. The Post-Production Bottleneck
Professional post-production workflows are still largely linear and manual:
- Editors and assistants spend 30–50% of post-production time on footage review and organization, before any creative cutting begins.
- Rough-cut assembly for feature films and documentaries can take weeks, especially when working with multi-camera shoots and fragmented archives.
- Multi-platform distribution (broadcast, streaming, YouTube, TikTok, Instagram, internal channels) requires repeated re-editing of similar material, often from scratch.
These dynamics lead to:
- Under-used archives: Large catalogues remain practically "dark" because search is limited to filenames, basic tags, and human memory.
- High cost per finished minute: A significant share of post-production budget is spent on repetitive labour rather than creative value.
- Burnout and creative frustration: Editors spend too much time "shovelling footage" and too little time on storytelling and refinement.
The result: Europe's creative sector struggles to meet multi-platform expectations and unlock the full value of its audiovisual heritage, while global competitors invest heavily in automation and AI-enabled tooling.
3. Solution Overview: OntoVision
OntoVision addresses this bottleneck by introducing an AI-native post-production engine designed for long-form, narrative content and professional workflows.
3.1 Core Value Proposition
OntoVision:
- Semantically analyzes raw and archival footage (image, audio, speech) to understand who appears, where, doing what, in which mood and narrative context.
- Transforms video archives into searchable, recombinable assets — enabling editors to find scenes by character, location, action, motif, or message in seconds.
- Generates narrative-driven rough cuts based on story briefs, templates, or editorial rules, delivering multiple edit suggestions ready for refinement in the NLE.
In short: OntoVision is the AI co-editor that automates the tedious 70% so editors can focus on the decisive 30% — narrative, rhythm, emotion, and polish.
3.2 Target Users
OntoVision is built for B2B teams that work with significant volumes of video:
- Public service and private broadcasters
- Documentary and feature-film producers
- Post-production houses and trailer agencies
- In-house media and marketing teams
- Sports clubs and sports media units (e.g. matchday recaps, highlights)
4. Technology and Architecture
OntoVision combines several state-of-the-art components in a secure, cloud-based pipeline.
4.1 Multimodal Understanding Engine
At its core, OntoVision fuses:
- Computer vision models to detect and track faces, actions, objects, and visual motifs.
- Automatic speech recognition (ASR) and natural language processing (NLP) to transcribe, translate, and interpret dialogue and voice-over.
- Audio analysis to identify sound events and atmosphere that matter editorially (crowd reaction, music cues, ambience).
- Multimodal LLMs to connect these signals into a coherent semantic representation of scenes and story units.
Instead of shallow keyword tagging, OntoVision builds a structural understanding of who appears, where, doing what, in service of which story.
4.2 Story Engine and Rough-Cut Generation
On top of this representation sits a story engine that links project goals with material:
- The user defines a brief — for example, "90-second matchday recap for social media," "45-minute documentary episode," or "3-minute campaign film for web."
- OntoVision selects relevant scenes and shots based on narrative importance, coverage, pacing, and brand/editorial constraints.
- The system assembles one or more rough cuts respecting length, structure, and platform guidelines (e.g. aspect ratios, safe zones, intros/outros).
Editors receive these rough cuts as fully editable timelines inside their existing NLE, not as locked renders.
4.3 Workflow Integration
OntoVision is built as a cloud-native platform with deep integrations:
- Direct export to Adobe Premiere Pro, with planned support for DaVinci Resolve and Avid via plugins and interchange formats.
- Optional on-premises or hybrid setups for sensitive archives, where analysis can run close to storage while orchestration and UI remain in the cloud.
- APIs for connecting to MAM systems, archive repositories, and collaboration tools.
This "integrate, don't replace" philosophy ensures that media teams keep their proven toolchains while OntoVision removes the bottlenecks in ingest, logging, and first assembly.
5. Key Use Cases
5.1 Feature Films and Documentaries
Long-form productions generate massive volumes of rushes, often from multi-camera setups and extended shooting schedules.
OntoVision enables:
- Automatic segmentation of raw footage into scenes and story beats.
- Character-centric and motif-centric indexing across hours of material.
- AI-generated rough assemblies aligned with the director's treatment or script.
This reduces the time from shoot to first rough cut from weeks to days, allowing directors and editors to iterate more creatively within the same schedule.
5.2 Broadcast and News
Newsrooms and magazine formats operate under extreme time pressure and multi-platform constraints.
With OntoVision, they can:
- Reuse archival footage for context packages, explainers, and retrospectives.
- Rapidly assemble multiple narrative angles around breaking events.
- Output platform-specific versions (broadcast, web, vertical social) with minimal duplicate work.
5.3 Sports and Live Events
Sports clubs and broadcasters need recurring content: matchday recaps, player highlights, behind-the-scenes stories.
OntoVision supports:
- Multi-angle ingest of game footage and automatic identification of key moments (goals, fouls, reactions).
- Rapid creation of highlight packages for different audiences and channels.
- Efficient reuse of season-long archives for thematic compilations and sponsor activations.
5.4 Marketing and Corporate Media
In-house media teams must deliver more video content for campaigns, recruiting, internal comms, and thought leadership — often with small teams.
OntoVision helps them:
- Turn long-form shoots (events, interviews, webinars) into a steady stream of short clips.
- Maintain brand consistency across multiple markets and language versions.
- Keep sensitive footage on EU infrastructure in line with internal compliance and external regulations.
6. Benefits and ROI
6.1 Time and Cost Savings
Across pilots and controlled tests, OntoVision has demonstrated:
- Up to ~70% reduction in overall post-production time for repetitive tasks.
- Up to ~90% reduction in manual editing workload in the rough-cut phase.
These gains translate directly into:
- Fewer night shifts and fire-drills before deadlines.
- Lower variable costs for freelance editors on routine work.
- More capacity to take on additional productions with the same staff.
6.2 Increased Content Output
By automating ingest, tagging, and first assembly, OntoVision allows teams to:
- Produce more format variants (trailers, teasers, recaps, explainers) from the same base material.
- Systematically unlock archive value — turning dormant footage into reusable production assets.
The focus is not over-production for its own sake, but effective production: more relevant content per hour invested.
6.3 Creative Quality and Human Control
OntoVision is designed as a human-in-the-loop assistant, not an autonomous editor:
- Editors can review, adjust, or override every AI suggestion.
- Creative decisions on pacing, tone, and final selection remain entirely with the human team.
- AI supports transparency and explainability through visual interfaces and logs, making its choices understandable.
This frees creative professionals to spend more of their time where their judgment is irreplaceable: storytelling, nuance, and emotion.
7. Data Protection, Compliance and European Sovereignty
For European broadcasters, public institutions, and content owners, data protection is not optional.
OntoVision is built accordingly:
- EU-based hosting on providers like Hetzner (Germany), with additional European options as the platform scales.
- GDPR-aligned data processing, including clear data boundaries, role-based access control, and encryption in transit and at rest.
- Training and evaluation datasets sourced from European partners under explicit agreements and legal review.
By keeping infrastructure, governance, and optimization within the European regulatory framework, OntoVision contributes to technological sovereignty for the continent's creative industries.
8. Business Model and Go-to-Market
OntoVision is offered as a subscription-based SaaS product:
- Essentials: entry-level tier for small teams and agencies starting around €199/month.
- Professional: for production companies and broadcasters' internal teams around €499/month.
- Enterprise: custom contracts starting around €1,000/month with tailored support, integrations, and SLAs.
Pricing scales with team size, number of projects, and AI usage, aligning OntoVision's economics with the customer's production volume.
The go-to-market strategy focuses on:
- High-touch pilots with broadcasters, production companies, and sports clubs.
- Partnerships with NLE and MAM vendors to integrate OntoVision into established toolchains.
- Participation in European innovation programs and industry networks to align with sector needs.
9. Roadmap and Vision
OntoVision is evolving along three main axes:
- Scale — from short-form image films to full-length features and multi-episode series, including complex story arcs and character networks.
- Language and localization — expanding multilingual support for subtitling, dubbing, and voice-over generation across European languages.
- Ecosystem and interoperability — deeper integrations with archives, rights management systems, sustainability dashboards, and analytics tools.
The long-term vision is clear: OntoVision aims to become the leading European AI platform for narrative video production, enabling small and mid-sized teams to operate with the efficiency and insight of large studios — without sacrificing creative autonomy or regulatory compliance.
10. Getting Started
OntoVision typically onboards new customers via a staged process:
- Discovery — joint workshop to map current workflows, pain points, and archives.
- Pilot project — applying OntoVision to a concrete production (e.g. a documentary, recurring show, or sports format) to measure time savings and quality.
- Roll-out — integrating OntoVision into standard operating procedures, connecting to NLE/MAM tooling, and training teams.
Teams interested in exploring OntoVision can begin with a focused pilot on a single show, season, or campaign and expand once the value is demonstrated in practice.