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AI in Legal Practice: How Neural Networks Analyze Contracts - A Step-by-Step Breakdown with a Real Example

Статья | 17 January 2026

How a Neural Network Analyzes a Contract: A Step-by-Step Breakdown

Let's consider a real example of analyzing a supply contract using AI.

Step 1: Primary Document Structuring
The neural network breaks the contract into logical blocks: preamble, subject matter, rights and obligations, payment terms, liability, term, additional conditions, details. Each block is analyzed separately, considering the context of the entire document.

Step 2: Key Parameter Extraction
The AI identifies and extracts specific parameters:

  • Contracting parties and their status

  • Subject matter with precise specifications

  • Contract amount and currency

  • Performance deadlines

  • Dispute resolution procedure

Step 3: Comparative Analysis with Templates and Norms
The neural network compares contract provisions with:

  • Standard clauses from its trained database

  • Legislative norms governing this contract type

  • Court practice regarding similar contractual structures

Step 4: Risk and Anomaly Identification
At this stage, the system identifies potentially problematic areas (detailed in the next section).

Step 5: Expert Report Generation
The AI generates a structured report classifying identified issues by severity, with recommendations for revision and alternative wording.

Typical Risks Identified by AI in Contract Analysis

  1. Ambiguity Risk — undefined terms, ambiguous wording, lack of specific performance criteria.

  2. Imbalance of Interests Risk — unilateral advantages, excessive penalties for one party, lack of reciprocal guarantees.

  3. Procedural Risk — incorrectly defined jurisdiction, unrealistic notice periods, cumbersome fact-confirmation procedures.

  4. Financial Risk — unclear payment terms, lack of indexation mechanism, disproportionate penalties.

  5. Non-Compliance Risk — provisions contradicting imperative norms, failure to account for legislative changes.

  6. Incompleteness Risk — missing essential terms, unregulated typical situations, regulatory gaps.

  7. Operational Risk — overly complex approval procedures, unrealistic deadlines, lack of phasing.

Integrating Neural Networks with the Botman.one Low-Code Platform: New Opportunities

The Botman.one platform, a visual environment for creating step-by-step scenarios without programming, opens unique possibilities for customizing and adapting neural network analysis to specific legal practice needs.

1. Built-in Prompts as the Foundation of an Expert System

The integration is based on a system of pre-configured prompts (queries to the neural network) that structure the analysis process:

  • Diagnostic Prompts — guide the AI to identify specific risk types

  • Comparative Prompts — request comparison with standard clauses

  • Normative Prompts — focus on checking legislative compliance

  • Recommendation Prompts — initiate generation of improvement suggestions

These prompts are created by experienced lawyers, codifying expert knowledge in a format understandable to the neural network.

2. Adaptive Prompt Variation Based on User Responses

A key advantage of integration with Botman.one is the dynamic adaptation of the analysis process based on the user's preliminary answers. Example scenario:

text

User indicates they are analyzing a lease agreement
↓
System asks to specify lease type: commercial/residential
↓
Depending on the answer, relevant prompts are activated:
- For commercial: check clauses on possible rent adjustment
- For residential: analyze compliance with the Housing Code
↓
System then clarifies the parties' status
↓
Depending on the answer (legal entity/individual), it activates:
- Prompts to verify the legal representative's authority
- OR prompts on the individual's legal capacity

Thus, the bot follows an individual path for each specific contract, asking precisely those questions and applying precisely those prompts relevant to the situation.

3. A Variable Prompt Builder as a RAG Approach Implementation

The ability to create a variable prompt builder, whose text changes based on user answers to the algorithm's questions, represents a practical implementation of the RAG (Retrieval-Augmented Generation) approach.

How it works in Botman.one:

  1. Retrieval — based on user answers, the system retrieves relevant prompt templates, regulatory requirements, and standard clauses from the knowledge base.

  2. Augmentation — retrieved elements are dynamically combined into prompts precisely matching the context of the analyzed contract.

  3. Generation — the neural network receives the constructed prompt and generates analysis based both on the model's general knowledge and the specific data provided by the system.

This approach helps overcome the limitations of standard neural network models, which may lack current information on recent legislative changes or specific company practices.

Practical Example: Automated Supply Contract Analysis System

Let's examine how an integrated solution works in practice.

Stage 1: Initial Questionnaire
The bot asks the user questions about the contract:

  • Contract type (supply, services, contract work, etc.)

  • Contract value

  • Presence of an international element

  • Client's special requirements

Stage 2: Dynamic Analysis Scenario Building
Based on the answers, the system:

  1. Activates the relevant analysis module (e.g., for international supply — prompts to check Incoterms, currency regulation)

  2. Adjusts the detail level (for large amounts — deeper analysis of warranty obligations)

  3. Connects specialized checks (if the client indicated special requirements — checks their reflection in the contract)

Stage 3: Analysis and Report Generation
The neural network analyzes the contract using the generated prompts and forms a report with a prioritized list of remarks.

Integration Benefits for Legal Practice

  1. Preservation and Structuring of Expert Knowledge — the system codifies experienced lawyers' approaches as prompts and analysis scenarios.

  2. Customization for Specific Company Needs — specialized checks reflecting internal policies and typical company risks can be created.

  3. Training Junior Specialists — the system can be used as a training tool, demonstrating what to look for in contract analysis.

  4. Scalability — once created, analysis scenarios can be used to process unlimited contracts without quality loss.

  5. Flexibility and Adaptability — when legislation or internal policies change, it's sufficient to update the relevant prompts in the system.

Conclusion

Integrating neural network contract analysis technologies with the Botman.one low-code platform creates a powerful synergy of artificial intelligence and expert legal knowledge. This approach not only automates routine checks but also creates adaptive, evolving expert systems that personalize analysis for each specific contract.

For law firms and corporate legal departments, this means significant time savings, improved quality and consistency of contract work, risk minimization, and preservation of expert knowledge in a format suitable for use and development.