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
| Criterion | Manual Entry | Regular Expressions | AI Extraction |
|---|---|---|---|
| Time per contract | 15–30 min | 2–5 min | 20–40 sec |
| Accuracy (standard fields) | ~85% (fatigue errors) | ~60% (template-dependent) | 95%+ |
| Scalability | Linear | Per template | One prompt |
| Implementation cost | Zero | Medium | Custom, pays off in 3–6 months |
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
| Field Type | AI Accuracy | Manual Entry Accuracy |
|---|---|---|
| INN/OGRN | 98% | 90% |
| Amounts | 96% | 85% |
| Dates | 97% | 88% |
| Contract Subject | 90% | 80% |
| Rare Conditions | 80% | 70% |
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
Work Process (Click to expand)
- Analysis — We study your contracts (standard and exceptions), determine fields to extract.
- Design — We choose the stack (LLM, parsing methods, validation).
- Implementation — We write the pipeline, test on a sample of 50+ documents.
- Integration — We connect to your CRM/ERP via REST API or Webhook.
- Deployment — We run it in your environment (on-premise or cloud).
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.