AI-Powered Contract Data Extraction for Lawyers

Lawyers spend hours manually parsing contracts: OCR confuses "ИНН" with "ИНК", dates are written out in words, and tables with amounts are scattered over 50 pages. We built an AI for lawyers solution for contract data extraction based on LLM (GPT-4o, Claude 3.5) that extracts structured contract data in seconds. Our team has 8 years of experience in legal tech and has completed 30+ projects, processing over 500 contracts per month for clients. This legal document automation solution uses NLP contracts techniques with a hybrid architecture: deterministic algorithms for requisites (parties, dates, numbers) and LLM for semantic analysis of the subject and non-standard conditions. This allows for PDF contract recognition even from scans with errors—we compensate through contextual hints and validation against external registries. Typical project cost ranges from $3,000 to $15,000 depending on complexity, with savings of $2,000–$5,000 per month for high-volume processing. For a firm processing 1,000 contracts per month, savings can exceed $8,000 per month.

To boost accuracy on rare templates, we apply fine-tuning LoRA on your data. This reduces hallucinations by 30% compared to a zero-shot approach. A 128K token context window covers up to 50 pages of text—enough for most contracts. Our LLM extraction pipeline achieves high accuracy.

Why Data Validation Matters

INN validation uses checksum verification; OGRN similarly. Amounts in digits and words must match. Start date cannot be after end date. Party names are checked against the Federal Tax Service registry by INN. Low-confidence fields are flagged for manual review. Manual entry achieves about 85% accuracy; our system delivers 95%+ on good scans, ensuring extraction accuracy. Additionally, we use RAG for contracts (Retrieval-Augmented Generation) to search for relevant conditions in long documents—this reduces the load on the LLM and improves extraction accuracy for the contract subject. This approach enables automated legal processing for contracts of any length.

How We Process Long Contracts

Contracts of 30–50 pages don't fit in the context window. Strategy:

• Deterministic extraction of requisites from the preamble (parties, number, date) — regex
• LLM for contract subject and non-standard conditions — only relevant sections
• Separate prompt for financial conditions (look for "Price and Payment Terms" section)
• Chunking with overlap for large volumes, followed by result aggregation

Comparison of Extraction Approaches

AI extraction is 30x faster than manual entry and 10% more accurate on standard fields. For a company processing 500 contracts per month, time savings reach 150 hours, reducing labor costs by 1–2 employees. Contract parsing with AI eliminates template dependencies.

Accuracy by Field Type

What Is RAG and Why Is It Needed?

RAG (Retrieval-Augmented Generation) allows referencing an external knowledge base during response generation. In our pipeline: first, relevant sections are extracted from the contract via vector search (embeddings 1536-dim), then the LLM generates output based solely on that. This reduces context, lowers p99 latency, and eliminates hallucinations on irrelevant data. For GPT-4 contracts processing, RAG ensures precision.

Work Process

What's Included

• Model — a tuned prompt for your contract type.
• Validators — INN/OGRN checksums, Federal Tax Service verification, logical checks.
• API — documentation, endpoints, sample requests.
• Training — 1-hour session for your lawyers.
• Warranty — 3 months of post-implementation support.

Timelines and How to Get Started

We'll assess your project in 1–2 days—just contact us. Basic implementation takes from 5 business days; complex customization takes up to 3 weeks. Pricing is calculated individually based on document volume and required fields. A typical project pays for itself in 3–4 months by reducing manual labor. Request a consultation to evaluate your documents and get a system demo on real examples.

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.