Competitive Intelligence System Powered by LLM and RAG

Manual competitor monitoring takes up to 20 hours per week, and missing a new product launch or price change can cost market share. Our AI competitive intelligence system specializes in LLM competitor analysis and competitive intelligence automation, utilizing neural network market monitoring to track competitor changes automatically. It collects data from dozens of open sources, analyzes it with LLMs (GPT-4, Claude 3.5), and delivers actionable insights straight to a competitor dashboard. Unlike manual collection, the system runs 24/7 and is 20 times faster than manual monitoring—detecting changes from new job postings to pricing shifts in minutes. Implementation experience shows an average ROI of 500% by cutting analytics costs, saving clients $50,000 annually. We guarantee event classification accuracy at 95%. Our LLM monitoring capabilities ensure events are never missed.

The system checks competitor websites, job databases, reviews, social networks, SEO metrics, and financial reports daily. All data is aggregated in a single dashboard with color-coded threat indicators: red for urgent, yellow for attention, green for informational.

How the System Tracked a Competitor Launch

In a real case for a SaaS product, the system detected new ML-engineer job postings at a top-3 competitor. Two weeks later—an updated page mentioning an AI feature. The LLM classified the event as a "product launch" with a significance of 4 out of 5, and the dashboard flagged it red. The team received a Telegram alert, prepared a response feature 3 weeks before the competitor's release, and preserved 15% market share.

Data Sources: What and How We Collect

Each source is implemented as an async Python worker (asyncio), enabling parallel processing of up to 50 competitors—5x more efficient than sequential scraping.

How the System Determines Event Significance

Significance is calculated from three parameters: market impact (LLM evaluates context—top management mentions, investment scale), mention frequency (if an event appears in 3+ sources, weight doubles), and competitor resources (market share, revenue). The final score from 1 to 5 is displayed on the dashboard as a heat map. We employ RAG analysis using ChromaDB for efficient event classification.

For example, if a top-3 competitor launches a free tier—red flag (significance 5) and instant Telegram alert. If a small company updates its privacy policy—green (significance 1). Thresholds are configurable: for instance, any price change from a competitor with >10% market share triggers an immediate push.

Job Postings as a Leading Indicator

Hiring dynamics provide a 6–12 month lead. A competitor hiring ML engineers aggressively → expect AI features in six months. Hiring sales in a new region → market entry. Mass layoffs → financial trouble. The LLM automatically highlights new roles, tech stack changes (e.g., switching from PyTorch to JAX), and expansion regions. Our AI job analysis capabilities also track skill demand shifts.

Results feed into the dashboard and affect the overall strategic activity score. This cuts manual analysis effort by 80%.

Comparison with Traditional Monitoring

Dashboard and Alerts

The competitor dashboard features a competitor heat map: competitor × dimension (product/price/hiring/reviews) with color indicators. Timeline—all events chronologically. It sends competitor alerts via Telegram, email, webhook to Slack. Thresholds are tailored to your business. Certified engineers guarantee 99.9% uptime.

Process of Work

1. Analysis—interview your team, identify key competitors and critical sources. Define KPIs, e.g., "notifications of price changes within 1 hour."
2. Design—pipeline architecture, LLM selection (GPT-4, Claude), setup of vector database ChromaDB for RAG analysis.
3. Implementation—develop workers, integrate sources, configure LLM classifiers with few-shot examples.
4. Testing—A/B comparison with manual monitoring on 3 months of historical data, calibrate significance thresholds.
5. Deployment—deploy on your server or cloud (AWS, Azure), set up Grafana dashboard, train the team (2 sessions).

What's Included

• Architecture documentation (flow diagram, ERD, LLM prompts)
• Pipeline source code (Python, asyncio) in your GitLab
• Configured Grafana dashboard with heat map and timeline
• Weekly digests to Telegram/Slack
• Team training (2 sessions of 2 hours each)
• 3 months of support (bug fixes, source adjustments)

With over 5 years of experience in building competitive intelligence systems and successful implementations for 50+ clients, we deliver a robust solution. Get a consultation—we will analyze your niche and prepare a preliminary architecture within 1 day. Contact us to estimate potential savings for your business.

Source: internal research on implementation effectiveness

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.