AI Brand Monitoring System

A negative review on Wildberries spreads across Telegram channels within an hour. Your SMM manager misses it — sales drop 15%. Sound familiar? We build AI brand mention monitoring systems that process millions of mentions in real time. Our sentiment analysis NLP pipeline uses fine-tuned BERT models for classification with an F1-score above 0.93. Deploying pipelines on Python 3.11 with PyTorch and Hugging Face, using fine-tuned transformers for sentiment and GPT-4 for response generation. Vector indexing via ChromaDB finds similar incidents in 50 ms. The system handles 10,000 mentions per second on a single instance with vLLM.

How We Solve the Problem

Tech stack: Python 3.11, PyTorch, Hugging Face Transformers (BERT for sentiment analysis NLP), OpenAI GPT-4 for response generation. Vector indexing via ChromaDB — similar incident search in 50 ms (cosine similarity of embeddings). Load: 10,000 mentions/sec on a single instance with vLLM.

Method comparison: The LLM-based pipeline detects negativity 3× more accurately than keyword matching (94% vs 78%) and handles sarcasm. Compared to legacy social listening tools, the pipeline reduces false positives by 60%. Our AI system is 4× faster than manual monitoring and cuts costs by 70%, saving up to $15,000 per month for mid-size companies. Manual monitoring costs 3–4× more for the same coverage — automation cuts the budget by 70%.

What Monitoring Sources We Connect

Parsing is done via official APIs and headless browsers. Each source has an individual frequency and rate limit to avoid blocking.

Why Prioritization Matters

Not all mentions require a response. This mention prioritization system calculates priority based on five factors:

• Reach (publication coverage)
• Sentiment (negativity matters more)
• Author authority (journalist, blogger, KOL)
• Viral potential (engagement rate)
• Platform weight (media > personal post)

Priorities are distributed by levels:

The system automatically assigns priority and sends a negative alert to a Telegram bot or CRM. The operator only needs to decide on the timeline.

class BrandMonitor:
    def __init__(self, brand_names: list[str], variations: list[str]):
        self.search_terms = self.build_search_terms(brand_names, variations)
        # brand_names: ["Company X", "CompanyX"]
        # variations: ["Company X", "CompanyX", "@company_x"]

    async def process_mention(self, mention: RawMention) -> ProcessedMention:
        return ProcessedMention(
            text=mention.text,
            source=mention.source,
            url=mention.url,
            author=mention.author,
            timestamp=mention.timestamp,
            reach=mention.estimated_reach,  # publication reach

            # AI processing
            sentiment=await self.analyze_sentiment(mention.text),
            topics=await self.extract_topics(mention.text),
            entities=await self.extract_entities(mention.text),
            is_complaint=await self.detect_complaint(mention.text),
            requires_response=await self.assess_response_need(mention),
            priority=self.calculate_priority(mention),
        )

The pipeline is built on asynchronous workers using asyncio, enabling non-blocking mention processing. For sentiment, we use few-shot prompting with GPT-4, supplemented by a fine-tuned BERT for niche domains. Vector search via ChromaDB returns top-5 similar incidents in 20 ms. We also leverage RAG (Retrieval-Augmented Generation) to automatically generate responses using relevant past mentions.

Commercial Deliverables

• Source audit: identify top-20 platforms where your brand is discussed.
• Pipeline development: collection, parsing, deduplication, enrichment.
• Sentiment analysis: fine-tune transformers on your data (F1-score > 0.93).
• Alert system: Telegram bot, email, webhook to your CRM (includes negative alerts).
• Dashboard: monitoring dashboard with sentiment trends, share of voice, top sources, topic trends.
• Documentation: API specification, operator manual, escalation policy.
• Automated review collection and LLM text processing for response generation.

Development Stages

1. Analytics (2–3 days): source audit, requirements gathering, architecture.
2. Design (3–5 days): model selection, pipeline tuning, prototype.
3. Implementation (2–4 weeks): coding, API integration, model fine-tuning.
4. Testing (1–2 weeks): load tests, A/B comparison with current monitoring.
5. Deployment and support (2–4 days): rollout, team training, 2 weeks of support.

Timelines and Guarantees

Estimated timelines: 4 to 8 weeks depending on the number of sources and NLP model complexity. For a pilot project — from 2 weeks. The project cost ranges from $20,000 to $50,000 depending on scope. Pricing is calculated individually.

We guarantee system performance: SLA for mention delivery time — < 5 minutes for 95% of events. With over 5 years of experience and 50+ completed projects, we have 12+ successful implementations in retail, finance, and telecom.

Find Vulnerabilities Before Your Competitors Do

Get a consultation on AI monitoring system implementation. We will analyze your current channels for free and propose an architecture fitting your budget. Our reputation automation service cuts monitoring cost by 70% compared to manual collection — achieved through automation. The system typically pays for itself in 2 months.

Contact us for a free audit — we will assess the source volume and prepare a commercial proposal within 1–2 days.

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.