Contract AI Barriers: Economics, Reasoning + Prompt Engineering

By Pedram Abrari, CTO, Pramata.

In the first two articles of this series, we covered the first six major technical challenges to achieving value from Contract AI. However, three additional technical challenges stand between experimental AI usage and true enterprise-grade Contract AI deployment.

This final article explores the last three challenges organizations must overcome for reliable, cost-effective Contract AI at scale: economic viability, structured reasoning requirements, and prompt engineering complexities.

Contract AI challenge 7: Costs and economic challenges

Running sophisticated AI models isn’t free, and when you’re processing multiple thousands of contracts, the cost can quickly become prohibitive.

The cost challenge manifests in multiple ways: Per-token costs escalate at scale, and reprocessing due to context overflow or hallucinations adds expense. Dumping contracts into popular and widely available LLMs without optimization is cost-prohibitive beyond limited experiments and the cost will soon exceed any business value.

Pramata’s solution: Economic optimization through intelligent processing

Pramata addresses cost and economic challenges through multiple patent-pending innovations that dramatically reduce processing requirements while maintaining accuracy:

• Extract & Enrich: Pre-tags key contract clauses so the system doesn’t need to rediscover clause types repeatedly when analyzing thousands of contracts. This significantly reduces redundant processing costs.
• Multi-LLM Support: Allows the platform to leverage various LLMs based on specific task requirements, utilizing fast-performing models for simple queries and deep reasoning models only when complex analysis is required. This flexibility saves costs compared to forcing one LLM to perform all contract management functions, even when it’s not the most efficient or effective one.
• Pramata Prompt Language: Uses expert-designed prompts that target the precise, most relevant parts of contracts instead of requiring the AI to process every word of every contract. This dramatically reduces token consumption and associated costs.
• AI Agents (and Control-Flow): Pramata’s targeted approach means the AI can focus its processing power on analysis rather than discovery, eliminating wasteful processing cycles.
• Scalable Agent Processing & Reporting: Transforms individual AI analyses into standardized outputs that can be reused across the enterprise, preventing duplicate processing costs.

Through this new and exclusive technology, Pramata makes Contract AI economically viable at enterprise scale—turning what would be a cost center into a genuine business value generator.

Contract AI challenge 8: The need for structured reasoning

Contract analysis often requires complex, multi-step reasoning. For example, analyzing a price escalation clause requires identifying base price terms, finding escalation triggers, determining calculation methods, checking for caps or exceptions, and verifying amendments.

This multi-step process challenges GenAI, which struggles to provide clear logical progression. Yet structured reasoning is imperative for accuracy and reliability. Without it, AI cannot track interdependent terms, document precedence, or conditional logic. It takes shortcuts, conflates steps, or jumps to conclusions without proper analysis. You wouldn’t accept this from your legal team, so why accept it from your legal tech?

Pramata’s solution: Built-in structured reasoning frameworks

Pramata addresses the structured reasoning challenge through coordinated technologies that guide AI through proper analytical progression:

TrueDoc OCR: Precisely captures complex contract elements including tables, multi-page clauses, and intricate layouts, ensuring the AI has complete information for multi-step reasoning.

Extract & Enrich: Pre-tags key contract clauses, providing the structured foundation necessary for logical analysis.

Templates & Playbooks: Provide consistent frameworks that guide AI through proper analytical sequences aligned with organizational policies and risk thresholds.

Contract Families: Maintains contract relationship precedence, enabling AI to reason through how multiple related contracts interact and which terms take precedence.

Contract Status & Renewals: Automatically tracks and derives current state and future renewal dates based on complex contract term parameters, demonstrating sophisticated reasoning about temporal relationships.

Multi-LLM Support: Leverages deep reasoning models specifically for tasks requiring complex logical progression.

Pramata Prompt Language: Uses expert-designed prompts that break down complex analytical tasks into logical steps.

AI Agents (and Control-Flow): Implements specialized orchestrator agents that coordinate analysis progression, ensuring proper sequencing of analytical steps.

Few-Shot Prompting Support: Provides the AI with clear examples of expected reasoning patterns and outputs.

Thought Process Support: Requires Contract AI to document its reasoning step-by-step during analysis, making the logical progression transparent and verifiable.

These coordinated innovations ensure that Pramata’s Contract AI doesn’t just arrive at answers arbitrarily or opaquely. It arrives at them through proper, verifiable analytical reasoning.

Contract AI challenge 9: Prompt engineering

One of the most significant yet underestimated technical challenges is effective prompt engineering. The newly created “prompt engineer” role indicates this isn’t simply about asking clear questions—it’s a complex technical discipline requiring specialized expertise, especially for Contract AI. Without it, even advanced AI models produce inconsistent, inaccurate, or unusable contract analysis results.

Several specific technical hurdles make prompt engineering for Contract AI particularly challenging:

• Specialized legal language
• Format consistency
• Development overhead
• Model-specific optimization
• Balancing detailed instructions with context window capacity

These challenges create a significant technical burden for organizations trying to get value from Contract AI. They must either develop deep prompt engineering expertise in-house, outsource the job to expensive resources, or risk inconsistent and unreliable contract analysis.

Pramata’s solution: Twenty years of contract management expertise

Pramata eliminates the prompt engineering burden through innovations that embed two decades of contract expertise directly into the platform:

TrueDoc OCR: Ensures prompts have access to properly formatted, complete contract data.

Extract & Enrich: Pre-processes contracts so prompts can reference structured clause types rather than raw text.

Templates & Playbooks: Provide pre-engineered prompt frameworks that embody best practices for contract risk assessment and analysis.

Contract Families: Enable prompts to reference relationships and precedence without requiring users to specify complex hierarchies.

Contract Status & Renewals: Provide pre-engineered prompts for common status and renewal queries.

Multi-LLM Support: Includes model-specific prompt optimization, ensuring consistent results regardless of underlying AI model.

Pramata Prompt Language: Our patent-pending Pramata Prompt Language uses expert designed prompts that target the precise, most relevant parts of your contracts instead of requiring the AI to cull through every word of every contract.

AI Agents (and Control-Flow): Implements sophisticated prompt orchestration that coordinates complex analytical sequences without requiring users to design multi-step prompts.

Few-Shot Prompting Support: Utilizes pre-engineered examples that demonstrate expected inputs and outputs, improving AI performance without user intervention.

Thought Process Support: Includes prompt engineering that requires AI to document reasoning, improving transparency without additional user effort.

Through these innovations, Pramata makes sophisticated Contract AI accessible to legal, sales, finance, and procurement teams, without requiring anyone to become a prompt engineering expert.

The complete picture: Why purpose-built Contract AI matters

Over the course of this three-part series, we’ve explored nine fundamental technical challenges that prevent generic AI approaches from delivering reliable contract intelligence

Each of these challenges, individually, can undermine Contract AI effectiveness. Together, they create a perfect storm of technical obstacles that explain why simply loading contracts into ChatGPT or other generic LLMs produces disappointing results for enterprise use.

Rather than giving up on the promise of Contract AI, achieve the results you need with purpose-built technology that addresses these challenges through coordinated innovation. Pramata’s integrated approach combining Context Engineering, Prompt Engineering, and Quality Engineering provides the foundation legal teams need to achieve true 99%+ accuracy at enterprise scale.

To learn more about Pramata and to see Enterprise-Grade Contract AI that actually works, in action, schedule a demo today.

—

[ This is a sponsored thought leadership article for Artificial Lawyer by Pramata. ]