AI-Powered Contract Analysis System for Legal Teams

A lawyer spends hours reviewing standard contracts. A missed penalty clause can cost up to 5% of annual revenue—especially at quarter-end when workload peaks. Studies show 80% of contracts contain at least one unfavorable condition, and manual review misses up to 30% of hidden risks due to fatigue.

We develop AI systems that analyze a contract in 2–5 minutes, extract key terms, assess risks, and compare against your templates. Our team of AI/ML engineers has 5+ years in NLP and Computer Vision, delivering 50+ projects for legal departments. Data extraction accuracy >95%—validated on thousands of documents.

Our AI contract analysis system delivers automatic contract review, data extraction from contracts, and contract risk assessment using NLP for legal documents. This legal AI powers efficient contract processing and machine learning in law applications. Our contract clause classification is industry-leading, and we support multilingual contract analysis.

What We Solve: Hidden Risks and Inefficiencies

• Inconsistent quality – Human reviewers vary in speed and accuracy. Fatigue in peak periods leads to missed clauses.
• Scalability limits – Hiring more lawyers doesn't scale linearly; AI handles thousands of contracts in parallel.
• Risk blind spots – Subjective assessment overlooks patterns. Our system flags every deviation from your templates.

How We Do It: AI-Powered Contract Analysis

We fine-tune modern LLMs (GPT-4o, Claude 3.5) on legal corpora. The pipeline:

• Document parsing – Extract text from PDF, DOCX, scans (OCR accuracy >99%).
• Structure extraction – Identify sections, clauses, tables.
• Entity extraction – Parties, amounts, dates, obligations (precision >97%).
• Clause classification – Each provision categorized: obligation, right, limitation, condition, exclusion of liability.
• Risk scoring – Each clause gets a score based on rules and anomalies relative to your templates.
• Summary generation – Plain-language overview for non-legal stakeholders.

The system processes up to 1,000 contracts per hour with latency p99 <2s. The model is fine-tuned on 50,000+ legal documents, achieving F1-score >0.95 for key fields.

Case Study: Reducing Review Time by 90%

For a manufacturing client processing 500+ contracts per month, we deployed a custom system. After a 4-week implementation, they reduced review time from 45 minutes to 3 minutes per document. Risk detection precision reached 96%, and human reviewers now only handle flagged clauses. The client reported a 70% reduction in legal review costs in the first quarter. This translated to $200,000 in annual savings.

Our Process

1. Audit – We study your contract types, templates, and risk profile.
2. Design – Architect the pipeline: extraction, classification, scoring.
3. Build – Develop in Python using PyTorch, Hugging Face Transformers, LangChain. Data stored in vector database (pgvector).
4. Test – Run on your contracts, measure precision/recall per field. We target >90% accuracy.
5. Deploy – On-premises or cloud (SageMaker, Vertex AI).
6. Monitor – Track latency p99, accuracy, data drift. We provide monitoring dashboards.

What’s Included

• Fully functional system with REST API.
• Complete documentation: architecture, operations manual.
• Training for up to 10 staff.
• Source code (full ownership transferred).
• Accuracy guarantee >90% on your test set.
• SLA with incident response time.

Accuracy Guarantees

For critical fields (parties, amounts, dates), we add rule-based post-processing to achieve 100% accuracy.

Additional Capabilities

Multilingual Analysis

Contracts can be in Russian, English, or both. GPT-4o/Claude work natively with these languages. For others (German, French), we use NLLB translation (200+ languages) followed by English analysis.

Risk Clause Library

Each contract category has mandatory and recommended clauses. Missing a mandatory clause triggers a warning. Unusual wording is flagged for human review. The library updates automatically with regulatory changes.

Code Example

class ContractAnalysis(BaseModel):
    summary: str                        # brief summary 3-5 sentences
    contract_type: str
    parties: list[Party]
    key_obligations: list[Obligation]   # what each party must do
    key_rights: list[str]
    financial_terms: FinancialTerms
    term: TermInfo
    termination: TerminationInfo
    liability_caps: str | None          # limitation of liability
    risk_clauses: list[RiskClause]      # high-risk clauses
    missing_standard_clauses: list[str] # missing typical clauses
    overall_risk_level: Literal["low", "medium", "high", "critical"]
    recommendations: list[str]

Get in Touch

Contact us for a free audit of your contracts—we'll assess automation potential within one day. Our team with 5+ years of experience and 50+ delivered projects guarantees quality and deadlines.

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.