We help video dataset brokers, AI companies, and content libraries process, organize, and deliver video for AI training — faster, cheaper, and without moving your data to the cloud.
Video remains the hardest data modality to work with at scale. It is unstructured, expensive to process, and difficult to search with precision. We accelerate the process, making video searchable, structured, and AI-ready within existing environments — without recruiting full-time specialists, disrupting workflows, or sacrificing control, all in a cost-effective way.
AI companies increasingly demand datasets that are more complex, precise, diverse, and tailored to specific model objectives. We help dataset providers transform large video archives into structured, AI-ready assets that can be discovered, assembled, and delivered significantly faster.
By reducing client request (RFP) to delivery time from days to hours, providers can respond to opportunities faster, launch higher-value data products, and evolve toward a Clip-as-a-Service (CaaS) model for AI training and development.
Human expertise remains essential for frontier AI. Text and code workflows are mature. Video is not. We help AI data orchestrators extend their human-expert networks into video and multimodal domains by providing the production-grade infrastructure that accelerates dataset preparation, semantic retrieval, dataset construction, and quality assurance.
You bring the experts and operational scale. We provide the video data infrastructure frontier AI labs increasingly demand.
Modern vector databases and AI pipelines handle storage and retrieval well, but they do not solve the underlying complexity of multimodal dataset quality, semantic structure, and consistency. We add an intelligence layer on top of existing vector infrastructure that helps teams go beyond retrieval when working with video and multimodal data.
It improves dataset quality, reduces redundancy, and structures complex assets for better decision-making. All of this integrates into existing workflows — with no re-architecture and no migration of existing vectors.
Large video archives are often underutilized — not because of lack of value, but because AI-era requirements for search, structure, and licensing evolve faster than traditional archival systems and existing DAM solutions can adapt.
We enable organizations to activate these archives by adding an intelligence layer that complements existing DAMs and content systems, enabling semantic search, automatic organization, and dataset-ready structuring for AI training, licensing, and discovery.
This turns existing content systems into AI-ready data pipelines — without replacing infrastructure, changing DAMs, or moving content outside their environment.
AI demand is becoming more complex and dynamic — requiring semantically structured datasets that evolve with changing model requirements.
We enable providers to respond to complex RFPs in hours instead of days by solving multi-constraint, high-dimensional requests across video datasets, going beyond semantic search to structured, intent-aware retrieval of the right assets.
This shifts dataset businesses from one-off licensing to Clip-as-a-Service (CaaS) — turning archives into structured, continuously monetizable AI data products.
AI pipelines are evolving quickly, but underlying video archives remain inefficient — full of redundancy, duplication, and expensive manual processing.
We reduce this complexity by replacing redundant video workflows with lightweight representations, making large-scale datasets economically efficient.
On-premise processing eliminates data egress costs, while a hybrid architecture enables efficient search across massive asset libraries — reducing storage waste, manual review, and compute inefficiency without changing existing workflows.
Access to high-value video data is increasingly constrained by privacy, regulation, and infrastructure limitations.
We enable a federated model — where content owners process data on their own systems and share only abstract representations (not raw video or assets) — ensuring full control while enabling collaboration.
This removes structural barriers to collaboration and unlocks new supply networks that were previously impossible to activate.
In a fast-changing AI environment, we provide an intelligence layer that helps companies adapt without rebuilding their infrastructure.
AI systems increasingly depend on curated, structured, task-specific video datasets — not raw or bulk footage. This shift is already underway. We enable organizations to lead this transition while keeping full ownership of their stack, data, and workflows.
| Option | The Reality |
|---|---|
| Build from scratch | 12–18 months of senior engineering effort. Requires complex decisions around data quality, sampling, vectorization, hashing, and pipeline orchestration. High cost and high risk. Teams often underestimate the micro-decisions required for frame-level quality, deduplication strategy, and indexing architecture. |
| License a proprietary platform | Constrained by lock-in. Proprietary embeddings and indexing models are difficult to migrate. Per-call API pricing can become unpredictable at scale. Limited flexibility for domain-specific fine-tuning. In many cases, exiting requires full re-indexing of data. |
| Glymt.ai approach | You retain full ownership of your architecture, data, and vectors. We provide client-optimized, modular, production-grade components that extend your existing stack with an intelligence layer for video and multimodal data. Deploy incrementally, integrate into existing workflows, and scale without re-architecture or migration. No lock-in by design. |
Curator is a modular Python SDK that runs within your infrastructure, providing production-grade components to build, structure, and operationalize video and multimodal AI systems — without re-architecting your existing stack.
Five steps. One modular framework. No proprietary lock-in.
Filter duplicates and irrelevant content at ingestion using scalable hashing and deduplication across large video datasets.
Generate multimodal embeddings across video, image, audio, and text using open-source models deployed within your environment.
Assess dataset quality through clustering, balancing, and automated reporting to detect gaps, redundancy, and overrepresentation.
Enable semantic and hybrid retrieval across multimodal assets. Move beyond tags to meaning-aware and constraint-based search.
Assemble and generate custom datasets and samples on demand. Store structured representations and reconstruct clips at delivery time.
Curator is derived from real-world multimodal infrastructure operating at scale — tested under production constraints before being made available to clients.
This is infrastructure, not a SaaS platform. It deploys inside your environment, extending existing systems rather than replacing them.
Not a SaaS platform you log into.
Curator is a production-grade AI Data Intelligence framework deployed within your own infrastructure. Built from more than 10 years of experience operating large-scale video archives and multimodal search systems, it helps organizations understand, optimize, search, govern, and monetize large multimodal datasets.
Vector databases solve storage and retrieval.
Organizations still need to understand what their data contains, identify redundancy, balance datasets, discover hidden relationships, generate metadata, track attribution, and make content searchable across modalities.
Curator organizes AI data operations into three connected layers.
Turn large collections into measurable, searchable knowledge.
Identify semantic groups, outliers, hidden patterns, and distribution gaps.
Measure redundancy, over-representation, under-representation, diversity, and balance.
Generate scene-level descriptions, semantic tags, and temporal metadata within your infrastructure.
Explore datasets through clustering, similarity relationships, highlights, and dimensionality reduction.
Improve training quality, reduce costs, and increase operational efficiency.
Identify near-duplicate assets across archives containing millions of items.
Automatically identify representative moments within long-form content in seconds.
Receive recommendations for redundancy removal and additional content sourcing.
Measure diversity, distribution quality, and benchmark dataset composition.
Advanced discovery, validation, attribution, and collaboration.
Search by meaning rather than keywords.
Validate content presence and identify similar content using examples.
Search video with images. Search images with text. Search audio with video.
Track provenance and attribution across indexed content and AI training datasets.
Support secure indexing workflows across organizations and partners.
The foundation powering every Curator deployment.
Core Python framework providing vector mathematics, similarity scoring, clustering algorithms, indexing workflows, and multimodal processing.
Hybrid retrieval combining hash pre-filtering with vector similarity ranking for large-scale search.
Unified support for video, image, audio, text, and 3D content using standard open-source models.
Vectorize content locally while sharing only abstract vector representations.
Optional modules for specialized workflows.
Train domain-specific embedding models for proprietary or specialized content.
Measure originality, identify similarity to protected content, and support compliance workflows.
Identify the closest matching assets, styles, examples, or prompting references.
Reveal hidden relationships across indexed repositories and knowledge collections.
AI companies now demand precisely curated, diverse, short-form, well-tagged clips. The transition from 'library as warehouse' to 'library as intelligence platform' is already underway. We give you the infrastructure to lead it — enabling Clip-as-a-Service (CaaS) delivery at scale.
Talk to our team →Respond to complex RFPs 10× faster with AI-powered semantic search. Build curated off-the-shelf datasets listed for ongoing revenue. Enable self-service client library browsing with purchase workflow attached.
Store only timestamp markers instead of trimmed clip files. Generate clips on-demand at delivery, delete after shipping. Move all asset videos to cheap deep-storage tiers.
Offer large content partners the ability to vectorize on-premises. Only abstract vectors are sent to you — never raw video. Open the door to partnerships that were previously operationally impossible.
Analyze a client's existing dataset, find the gaps, and proactively pitch the missing content — turning one deal into a recurring relationship and Clip-as-a-Service revenue stream.
| Use Case | How It Works & Business Impact |
|---|---|
| Custom RFP Sample Assembly | An AI company sends a request for 500 diverse clips matching precise criteria. Instead of 3 days of manual review, semantic + multimodal search returns a candidate set in minutes. Time-to-sample drops from 3 days to 3 hours. |
| Dataset Deduplication & Cleaning | Hash-based redundancy detection runs across a 10M+ clip library, identifies duplicate clusters, generates a health report, and recommends removals. Storage costs drop. Dataset quality improves for all future RFPs. |
| Off-the-Shelf Dataset Products (CaaS) | Clustering and sampling tools generate a balanced, tagged dataset of 50,000 clips organized by geography, time of day, and aesthetic quality — listed as a SKU for instant purchase without per-deal curation effort. |
| Federated Partner Onboarding | A major broadcaster refuses to transfer video files due to legal restrictions. Curator's local vectorizer deploys on their infrastructure. Vectors transmitted — broker gains semantic search access to 5M+ additional clips without storing a single file. |
| Gap Analysis & Proactive Selling | Curator analyzes a client's existing training dataset, identifies under-represented categories, and generates a targeted pitch for the missing content — turning a one-time deal into a recurring data supply relationship. |
A leading video dataset broker needed to move from manual, catalogue-browsing delivery to a precision semantic search platform capable of serving complex AI company RFPs in hours instead of days. We leveraged the Curator framework to implement a solution on their infrastructure, processing their archive without a single byte leaving their environment.
AI data orchestrators run large-scale human-expert networks to generate training data for frontier AI labs. Their model excels at text, code, and STEM. Video and multimodal data is structurally different — and that is where we come in. We provide the infrastructure layer that accelerates your video pipeline, raising quality, consistency, and operational control across the most demanding data modality.
Talk to our team →Video and multimodal training data — particularly for computer vision, embodied AI, and multimodal reasoning models — demands vectorization, deduplication, semantic structuring, and rights-cleared pipeline management at scale.
These are not annotation tasks. They require a purpose-built infrastructure layer that orchestrators do not have internally — and were never designed to build.
Companies managing large networks of human experts for frontier AI lab data generation — expanding into multimodal and video domains where their core model has structural coverage gaps.
Annotation platforms moving into video-specific tasks — action recognition, scene understanding, temporal captioning — that require semantic structuring before human annotation can begin.
Organizations contracted by frontier labs to supply training data across modalities — where video pipeline infrastructure needs to match the quality standards applied to text and code datasets.
Large organizations building proprietary multimodal AI systems internally — needing a video data infrastructure partner rather than a costly internal build or a full platform replacement.
We handle the video infrastructure layer that must exist before your annotators can work efficiently — deduplication, semantic segmentation, clip generation, and quality filtering. Your human experts receive clean, structured, pre-processed video ready for task-specific annotation. Not raw footage.
When clients specify precise video training requirements, we supply the retrieval infrastructure to source and assemble matching content at speed — across partner archives, internal libraries, or federated content networks — without manual browsing or bulk transfers.
We enable you to extend content supply through privacy-safe federated partnerships — content owners vectorize on-premise, only abstract vectors are shared. Access content that was previously impossible to acquire, at scale, without legal or transfer risk.
Automated redundancy detection, cluster balance analysis, bias assessment, and dataset health reporting — giving you and your clients confidence that video training sets are genuinely fit for purpose before expensive model training runs begin.
Frontier lab clients often require that data never leaves a controlled environment. Our Curator framework deploys entirely within your or your client's infrastructure — zero egress, zero cloud dependency — matching the security standards that leading AI research environments demand.
Your vector database is excellent at storage and retrieval. It was never built for the curation logic, semantic filtering, and dataset intelligence that high-performing AI pipelines actually need. We add that intelligence layer — on top of your existing stack, zero migration required.
Integrate via BYOV. Pass your existing embeddings. No migration. No re-indexing.
Run redundancy detection, inlier/outlier analysis, and dataset health reports.
Enable semantic arithmetic: positive + negative prompt filtering and re-ranking.
Apply clustering to group vectors by semantic similarity. Spot dataset patterns.
Train custom vectorizers on domain-specific content for maximum precision.
| Developer Profile | Primary Use Cases |
|---|---|
| ML Developer / Vector DB User | Add video-native auto-trimming, auto-sampling, and clustering to your pipeline. Enhance search with relevance-aware multimodal ranking. Develop white-labeled 'Curator Layer' features for your own product. |
| AI Model Trainer / Research Lab | Reduce dataset noise and redundancy before training. Enable semantic filtering for precise training set composition. Cut manual review costs with automated relevance scoring. |
| Healthcare / Industrial AI | On-premises processing for HIPAA-sensitive medical video. Auto-trim surgical recordings for relevant segment extraction. PACS system integration with fine-tuned indexing models. |
| RAG Pipeline Engineer | Improve LLM context quality by feeding only non-redundant, high-relevance video segments. Reduce token costs by eliminating low-signal data from the retrieval pipeline. |
You are sitting on an archive of enormous potential value. The problem is that it is largely invisible — trapped in formats, folders, and incomplete metadata. We activate it without moving a single file to the cloud.
Reduce manual review by up to 60% at ingestion. Duplicate content identified and flagged automatically. Consistent metadata, cover frames, and tags generated.
"People dancing outdoors, golden hour, not in a studio" — returning exactly that, without manual tagging. Far beyond keyword-based browsing.
Package archive content for AI training data buyers — opening the B2B data market without a dedicated sales team.
Highlighting local vectorization pipeline execution layers. Your content never leaves your environment. Full data sovereignty.
| Segment | Primary outcome |
|---|---|
| Content studios & farms | 60% reduction in manual curation. New AI licensing revenue from existing content. |
| Stock footage platforms | Enterprise-grade semantic search. Dataset packaging for AI buyers without rebuilding the platform. |
| Media archives & broadcasters | Dark archive activated. Search by meaning, not keywords. No cloud migration required. |
| Independent creators | Direct access to AI dataset licensing market. Automated enrichment and IP security. |
Unlike cloud AI platforms where costs scale unpredictably with usage, Curator runs entirely on your own infrastructure — on-premises, private cloud, or local GPU. Your cost is fixed. Your data never leaves your environment.
All routes run on the same production-proven Curator infrastructure. The difference is the level of specialization and support your organization requires.
Best for: ML teams, vector DB users.
Best for: Organizations with real multimodal AI pain and no large internal ML team.
Building production-grade pipelines for real-world, inconsistent video datasets requires hundreds of precise micro-decisions. Large consulting firms can't focus on this. Large model companies are incentivised to increase your Token spend, not reduce it.
Dataset brokers needing faster RFP-to-sample delivery. Specialized media archives with complex, inconsistent content. Companies with rising video processing costs and no internal ML team. Organizations with growing video processing costs.
Reduce token costs, eliminate low-signal data ingestion, optimise prompt structures, cut cloud compute burn by up to 50%.
Deploy scalable retrieval architectures combining vector search and metadata indexing for high-precision discovery.
Improve RFP-to-sample speed and quality. Automate the most time-consuming steps. Validate outputs at scale.
Build reliable retrieval workflows with similarity scoring, clustering, and semantic search tuned to your specific archive and use cases.
Test, validate, and integrate the right open-source models for your domain. Build small bespoke annotation models.
Recurring processing refinement, indexing/validation operations, technical support, and lightweight infrastructure optimisation.
Glymt.ai was born from a practical problem. Running a real video data business — Glymt, a marketplace for short-form video clips — meant confronting daily the operational chaos of managing massive, inconsistent, multimodal content libraries at scale.
We built tools to solve our own problems. Tools for eliminating duplicate content efficiently. Tools for finding the most representative frame from a long video in seconds. Tools for balancing and organising datasets to serve precise client requests. Tools for enabling federated content access without moving sensitive data.
Those tools became the Curator framework. We packaged them, tested them in production, and made them available to organisations facing the same challenges we had already solved.
Today, Glymt.ai operates at the intersection of two things we know deeply: video content operations and applied AI for data management. We are not a research lab. We are practitioners who built production infrastructure on top of hard-won operational experience.
Not from a research paper — from running a real production video business at scale.
Continuous model testing and validation against a real, diverse production dataset.
Not just pilot environments. The same technology running in our own business every day.
We test and validate the latest open-source models against our own production dataset before recommending them to clients.
Curator powers Glymt — our own marketplace. What we license, we live with every day.
hello@glymt.com
Tell us about your video data challenge — archive, pipeline, or dataset delivery. We'll show you exactly how Curator addresses it, based on 10+ years of production experience.