AI-Powered Copyright Management and Content Monitoring System

We've encountered a situation where a large media archive of 5 million content units had a team of 3 lawyers spending 40 hours per week just searching for infringements. Manually, they found no more than 10% of actual thefts. We developed an AI system that scans hundreds of platforms and automatically generates DMCA notices. The client reduced labor costs by 80% and increased successful blocks 15 times.

Problem: how to scale content protection as the library grows to 10 million objects?

Traditional methods don't work: a lawyer manually reviews at most 200 objects per day — for 10 million that's 50,000 person-days. But pirates copy instantly. We solve this with distributed search and perceptual hashing, processing up to 10 million images per day on a single GPU cluster (NVIDIA A100).

Why AI monitoring is more effective than traditional approaches?

AI monitoring processes up to 10 million images per day, while a team of 5 lawyers can handle at most 500. That's 20,000 times faster.

How does the AI system detect infringements?

class ContentRightsMonitor:
    def __init__(self):
        self.image_hasher = PerceptualHasher()    # pHash, dHash
        self.text_fingerprinter = TextFingerprinter()  # Rabin-Karp rolling hash
        self.audio_fingerprinter = AudioFingerprinter()  # acoustic fingerprints

    def check_for_infringement(
        self,
        protected_asset: ProtectedAsset,
        candidate: FoundContent
    ) -> InfringementCheck:

        if protected_asset.type == "image":
            similarity = self.image_hasher.similarity(
                protected_asset.hash, candidate.hash
            )
        elif protected_asset.type == "text":
            similarity = self.text_fingerprinter.similarity(
                protected_asset.fingerprint, candidate.fingerprint
            )
        elif protected_asset.type == "audio":
            similarity = self.audio_fingerprinter.match(
                protected_asset.fingerprint, candidate.audio_path
            )

        return InfringementCheck(
            asset_id=protected_asset.id,
            candidate_url=candidate.url,
            similarity_score=similarity,
            is_infringement=similarity > protected_asset.threshold,
            infringement_type=self._classify_type(similarity, protected_asset)
        )

The system uses a combination of perceptual hashing for images, rolling hash for text, and acoustic fingerprints for audio. The similarity threshold is configured per asset — for unique illustrations we set 90%, for mass photos 85%.

Comparison of fingerprinting methods

According to IEEE research, perceptual hashing provides robustness against compression and resizing with accuracy up to 98%.

Automating DMCA notices and rights management

Upon infringement detection, the system instantly sends a takedown notice via the platform's API. For YouTube Content ID — through their API, for Cloudflare — via Copyright API. If no API exists, it generates a legally substantiated email with links to the original and copy, along with an evidence package (screenshots, hashes, chain of custody).

The central rights registry stores: the IP object, rightsholder, territorial scope, term, license type (exclusive/non-exclusive), permitted uses. The AI checks usage compliance against allowed scenarios.

class LicenseChecker:
    def is_licensed_use(
        self,
        asset_id: str,
        user: str,
        usage_type: str,   # reproduction / distribution / modification / public_display
        territory: str,
        commercial: bool
    ) -> LicenseCheckResult:

        licenses = self.db.get_active_licenses(asset_id)

        for license in licenses:
            if (license.covers_user(user)
                and license.covers_territory(territory)
                and usage_type in license.permitted_uses
                and (not commercial or license.allows_commercial)):
                return LicenseCheckResult(is_licensed=True, license_id=license.id)

        # Check fair use / free licenses (CC, OFL)
        free_license = self.check_free_license(asset_id, usage_type)
        if free_license:
            return LicenseCheckResult(is_licensed=True, license_type="free", conditions=free_license.conditions)

        return LicenseCheckResult(is_licensed=False, available_licenses=self.db.get_available_licenses(asset_id))

Tracking royalties and managing content libraries

The system records every instance of music usage in a video, article in media, etc., and matches it against the licensing agreement. For example, for a track on a streaming service: each play is attributed to the contract (minimum guarantee or percentage). Integration with RAO, WIPO, RIAA allows automated reporting and payment reconciliation. Calculation margin of error — less than 0.5%.

For stock agencies and media libraries: AI automatically tags new content (ImageNet, Places365), checks for duplicates via reverse image search, extracts person mentions using NER (Spacy, BERT). When uploading photos with people, the system checks for model release; for architecture — property release. A library of 1 million objects is tagged in 2 hours (with GPU: 4 A100).

Process and deliverables

Stages:

• Analytics (1-2 weeks): audit current legal procedures, content types, platforms, reporting requirements. Form specification.
• Design (2-3 weeks): data schema, API, selection of fingerprinting algorithms, processing pipelines.
• Implementation (4-8 weeks): module development, integrations, test case creation.
• Testing (2 weeks): load testing (up to 10 million objects), usability tests with lawyers, A/B comparison with manual search.
• Deployment and launch (1 week): cloud or on-premises deployment, monitoring setup, team training.

Deliverables:

• Architecture document: module descriptions, system integrations, technology stack (Python, PyTorch, PostgreSQL + pgvector, Redis).
• Codebase: monitoring modules, LicenseChecker, DMCA automation, API for CMS interaction.
• Platform integration: up to 5 platforms (YouTube, Facebook, Twitter, Pinterest, your site).
• Documentation: complete admin guide, API description, incident response procedures.
• Training: 2 days for legal team and 1 day for DevOps on deployment.
• Support: 3 months warranty including critical bug fixes.

Results: Over 5 years of AI/ML experience, delivered 20+ computer vision and NLP projects. On one project with a catalog of 3 million images, we reduced undiscovered infringements from 80% to 3%, and average reaction time from 3 days to 4 hours.

Contact us to discuss your scenario — we'll select the optimal system configuration. Order AI-powered rights management system development: get an engineer consultation and preliminary timeline estimate. Implementation takes 4 to 12 weeks.

NLP Development: Text Classification, NER, Embeddings, and Information Extraction

We often receive a task: process 50,000 support tickets — currently all manual. Dataset — 3,000 labeled examples, 12 categories, imbalance: one category occupies 40% of the sample, three at 1-2% each. Baseline accuracy — 78%. Sounds decent until you look at recall for rare classes: 0.31, 0.44, 0.28. These classes — complaints and churn threats — are most important to the business.

This is a typical NLP development project. The problem is not the algorithm but that accuracy is the wrong metric. Our experience across 30+ projects shows: we start by analyzing business metrics and only then choose the model.

Why accuracy is not the right metric for rare classes?

Accuracy ignores imbalance. If the "churn" class appears in 2% of cases, the model can predict "all good" and get 98% accuracy — but the business loses clients. Solution: F1 macro (averaged over all classes) or weighted F1. For NER — strict entity F1 (exact matches only). We guarantee: after choosing the correct metric, model quality becomes measurable and predictable.

Text Classification: From BERT to Distillation

BERT-like models are the standard for classification. ruBERT-base or ruBERT-large from DeepPavlov for Russian. multilingual-e5-large — for multiple languages in one pipeline. XLM-RoBERTa-large — a strong multilingual backbone.

Fine-tuning for classification: add a classification head on top of the [CLS] token, train for 3-5 epochs with lr=2e-5, weight decay=0.01. For imbalance — weighted CrossEntropyLoss or focal loss with gamma=2.0. Contact us — we will show a code snippet.

Imbalance case study. Dataset — 3,000 examples, imbalance 1:20. Solution: class_weight via sklearn + CrossEntropyLoss. Additionally — augmentation of rare classes via backtranslation (ru→en→ru through MarianMT). Recall for rare classes rose from 0.31 to 0.67 with a slight drop in accuracy (76%→74%). Full NLP development end-to-end took 3 weeks.

Distillation for production. BERT-large gives F1 0.89, but inference on CPU — 180ms. Distillation into DistilBERT or ruBERT-tiny2 reduces latency to 25ms with F1 0.84. Export to ONNX Runtime provides an additional 1.5-2x speedup. DistilBERT achieves 7x lower latency than BERT-large with only a 5% drop in macro F1 – a typical production trade-off.

How to choose between BERT and LLM for your task?

For most classification and extraction tasks, BERT-sized models offer the best trade-off between cost and performance. Shift to LLMs only when the task demands generation, complex reasoning, or zero-shot generalization.

NER: Named Entity Recognition

NER — extracting persons, organizations, locations, dates, amounts, document numbers. For general categories (PER, ORG, LOC), pre-trained models work well. For specialized ones (medical terms, legal concepts) — fine-tuning is needed.

Data annotation. The main cost of an NER project. For a quality model — 500-2,000 labeled sentences per entity type. Tools: Label Studio (open source) or Prodigy (by spaCy creators). IOB2 format — standard.

Architecture. Token classification on top of BERT: each token gets a label (B-PER, I-PER, O). spaCy 3.x with transformer pipeline — a convenient production choice.

Nested entities. Standard IOB models cannot handle nested entities (organization inside an address). For such tasks — span-based NER: SpanBERT or SpERT. More complex but correct.

Post-processing is mandatory. The model predicts tokens — normalized entities are needed. Date — dateparser. Amounts — regex + validation. Names — deduplication via rapidfuzz. Included in our standard delivery.

Sentiment Analysis and Opinion Mining

Binary classification positive/negative works out of the box with BERT. Complexity — aspect-based sentiment analysis (ABSA): "the restaurant has good food but terrible service." For ABSA: aspect extraction (NER) + sentiment per aspect. Joint models BERT-for-ABSA — quality on Russian data is lower due to dataset scarcity. RuSentiment, SentiRuEval — main resources.

For production with simple positive/negative/neutral: distil models are enough. Three classes, balanced dataset, 2,000+ examples — F1 macro 0.82-0.87 in 1-2 days.

Text Summarization

Extractive summarization (select sentences) — TextRank or BM25 without training. Fast, no hallucinations. Good for long documents.

Abstractive (generates new text) — seq2seq: mT5, mBART, FRED-T5, ruT5-large. For production via LLM API (GPT-4, Claude) — often the best cost/quality/speed trade-off.

Embeddings: Vector Representations of Text

Embeddings are the foundation of semantic search, deduplication, clustering, RAG. Quality critically affects downstream tasks.

Models. E5-large-v2, BGE-M3, multilingual-e5-large — strong multilingual embedders. sentence-transformers/paraphrase-multilingual-mpnet-base-v2 — fast option. For Russian: ru-en-RoSBERTa (Skoltech) performs well on semantic textual similarity.

Embedding quality evaluation uses the MTEB benchmark as standard. But top results on MTEB don't guarantee success on a domain dataset — we build domain-specific eval.

Fine-tuning embeddings. If standard models don't give the required Recall@k — contrastive learning on domain pairs with MultipleNegativesRankingLoss. How to perform this for domain data:

1. Collect 500–2,000 semantically similar pairs from your domain.
2. Apply MultipleNegativesRankingLoss with a batch size of 32–64.
3. Train for 1–3 epochs using AdamW (lr=2e-5).
4. Evaluate Recall@k on a held-out domain test set.

This approach yields a 5–15% improvement in Recall@k in practice.

Dimensionality and storage. E5-large: 1024 dim, float32 — 4KB per vector. For 10M documents — 40GB. Quantization int8 reduces to 10GB. FAISS IVF_PQ — more compact but with losses. Included in our deployment recommendations.

Information Extraction

Structured extraction is a frequent task. Examples: key contract terms, technical characteristics, dates and amounts from invoices.

1. Regex + rule-based. For INN, OGRN, amounts, dates — more reliable than neural networks. No data required.
2. NER + post-processing. For variable formats.
3. LLM with structured output. GPT‑4 / Claude with JSON schema — for complex documents. Cost: minimal per document. For 10k+ documents/day — we calculate the economics.

We guarantee a hybrid: regex/NER for typical fields + LLM for edge cases. Our guarantee is backed by years of production experience and more than 30 projects.

Work Stages

Prototype on existing data — 1-3 weeks. Production system with CI/CD — 1.5–2.5 months. Cost is calculated individually — get a consultation for a project estimate.

What's Included

• Model and pipeline architecture documentation
• Access to the model via REST API (FastAPI + ONNX)
• Client team training (2-hour webinar + Q&A)
• Accuracy guarantee on the agreed test set
• Months of post-delivery support (bug fixes, adaptation to new data)

Our Experience

Years of NLP projects from classification to RAG systems. The team includes ML engineers experienced with Hugging Face, spaCy, LangChain, MLOps. We use vLLM, Kubeflow, Weights & Biases — a production stack, not toys. Contact us to evaluate your NLP project within two days — request a free consultation on your text processing pipeline.