A Pramata Article
The Legal AI Reality Check
Why you should avoid the proprietary AI model trap, what makes AI actually work in production, and how to solve hallucinations through better systems and reliable data
By Tom Guhin, Pramata Chief Solution Architect
I’ve been having the same conversation with legal tech buyers for months now. They’re excited about AI, ready to invest, but stuck on a fundamental question: Should they go with a vendor that’s built their own “Legal AI LLM model,” or one that leverages the big public LLM models like Claude and GPT?
It feels like a smart question, but it has an assumed answer built in: that a proprietary “Legal AI model” is best. After all, wouldn’t a model built specifically for legal work be better than a general-purpose one?
Wouldn’t owning your AI infrastructure give you more control?
I get the appeal. But I’ve seen this movie before—and it doesn’t end well for the companies that choose “safety” over adaptability.
Déjà vu: A flashback to the private cloud era
Remember 2015? Every enterprise was debating public cloud vs. private cloud. The private cloud pitch was compelling: “Keep everything in-house for better security and control. Don’t depend on Amazon or Microsoft—build your own cloud infrastructure tailored to your specific needs.”
Sounds familiar, right?
Companies spent millions building private clouds, convinced they were making the smart, strategic choice. Meanwhile, their competitors were moving to AWS and Azure, gaining access to continuous innovation, global scale, and features that no internal IT team could possibly match.
Fast forward to today: How many of those private cloud investments are still paying dividends? Most companies that went the private cloud route have since migrated to public clouds or hybrid architectures. The innovation gap became impossible to ignore. The same theory applies to most “build vs. buy” technology cases and Contract AI is no exception.
The pace of AI innovation changes everything
Here’s what makes the current AI moment different from any previous technology shift: the pace of improvement is unprecedented. We’re not talking about annual software updates or periodic hardware refreshes. We’re seeing fundamental capabilities leap forward every few months, with incremental improvements as often as daily or weekly in some cases.
Look at the trajectory just in the past two years:
- GPT-3 seemed revolutionary
- GPT-4 made GPT-3 look like a toy
- Claude Sonnet raised the bar again
- GPT-4o brought multimodal capabilities
- Claude 4.0 just dropped with even better reasoning
- Google’s Veo 3 has just blown every prior iteration of image, video, and audio generative AI out of the water
Each generation isn’t just incrementally better—it’s dramatically more capable at tasks like contract analysis, legal reasoning, and document review. And this is happening across multiple competing research labs, each pushing the others forward.
Now imagine you’re a legal tech company trying to keep up with that pace while also running a business, serving customers, and maintaining existing products. Even if you had unlimited resources (and let’s be honest, no one in the LegalTech field does), you’re playing catch-up to teams of thousands of specialized AI researchers at OpenAI, Anthropic, and Google.
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The billion-dollar reality check
The leading AI labs are investing billions—not millions: billions—annually in R&D. OpenAI’s training runs cost hundreds of millions per model. Anthropic raised $4 billion just to stay competitive. Google’s DeepMind has virtually unlimited resources.
Any legal tech company building a proprietary model is competing against this level of investment with a fraction of the resources and expertise. It’s not just David vs. Goliath—it’s David vs. an army of Goliaths, all racing to outdo each other.
The math is simple: a proprietary legal model built this year, or last, will be outperformed by general-purpose models within months, not years. We’ve already seen Claude Sonnet and GPT-4 dramatically outperform specialized legal models on contract analysis benchmarks.
The "legal-specific" mirage
But wait,” I hear you saying, “shouldn’t a model trained specifically on legal data perform better on legal tasks?”
It’s intuitive, but it misses how modern AI actually works. Today’s leading models aren’t just trained on legal data—they’re trained on essentially all human knowledge, including vast amounts of legal content. They understand legal concepts not in isolation, but in the full context of business, finance, regulation, and human interaction.
Plus, the general models have something no specialized legal model can match: continuous improvement across all domains simultaneously. When GPT-4 gets better at reasoning, it gets better at legal reasoning. When Claude improves its language understanding, it improves its contract analysis.
A specialized legal model gets better at legal tasks only when its creators dedicate specific resources to legal improvements. And those resources are inevitably limited compared to the massive, continuous investment in frontier models.
The open ecosystem advantage
Smart legal tech companies are embracing what I call “LLM-agnostic architecture.” Instead of betting on one model—especially one they built themselves—they design their platforms to leverage the best available models for specific tasks.
Some models excel at document summarization. Others are better at logical reasoning or clause classification. The most sophisticated platforms can plug in whichever model performs best for each specific job, and swap them out as better options emerge.
This approach means when Claude 5 or GPT-5 launches with dramatically better capabilities, these platforms can integrate the improvements rapidly. Their customers get cutting-edge performance without much disruption or migration headaches.
Meanwhile, companies locked into proprietary models have to wait for their vendor to rebuild their entire AI system—if they even can
Learning from the API Economy
The most successful companies of the past decade didn’t try to build everything in-house. They embraced the API economy, integrating best-of-breed services instead of reinventing the wheel.
Stripe didn’t build their own banking infrastructure: They built great APIs on top of existing payment networks. Twilio didn’t lay their own telecom cables: They created excellent developer tools on top of carrier services. These companies won by focusing on their unique value-add, not by trying to own every layer of the stack.
The same principle applies to Contract AI. The companies that will dominate aren’t those trying to build their own AI models—they’re the ones building the best legal & contract solutions on top of the most advanced AI available.
What this means for your decision
If you’re evaluating Legal or Contract AI platforms, like Pramata right now, ask yourself: Do you want to bet on a vendor’s ability to keep pace with OpenAI and Anthropic, or do you want a platform that can continuously tap into the latest breakthroughs from the world’s best AI labs?
The choice seems obvious when you frame it that way. But I know the proprietary pitch is seductive. It feels safer, more controlled. You’re not dependent on external AI providers, you have a “legal-specific” solution.
But that safety is an illusion.
In a field evolving as rapidly as AI, the biggest risk isn’t depending on external providers—it’s betting on yesterday’s technology in tomorrow’s market.
Questions to ask
Once you’ve moved beyond the question of whether mainstream large language models will outperform proprietary legal models when it comes to your Contract AI needs (and they will), here are the questions that actually matter:
Can the platform integrate new AI models as they emerge?
If the answer is no, you're buying a depreciating asset.
Does the vendor have the resources to compete with billion-dollar AI labs?
If not, their proprietary model will fall behind quickly.
What happens when GPT-5 or Claude 5 launches with capabilities that make current models look primitive?
Can your platform take advantage, or are you stuck waiting for your vendor to rebuild?
Is the vendor focusing on their unique value-add, or trying to recreate existing AI capabilities?
The best legal tech companies solve legal problems, not AI problems
The Bottom Line
Building a proprietary AI model for legal tech in 2025 is like investing hundreds of millions in on-premise servers right before the cloud revolution. It might seem like the safe, controlled approach, but it may actually be the path to obsolescence.
The legal tech companies that will thrive are those embracing the AI ecosystem, not fighting against it. They’re building platforms that can ride the wave of AI advancement instead of getting crushed by it.
Choose adaptability over ownership. Choose access to cutting-edge capabilities over the illusion of control. Choose the future over the comfort of the familiar.
Your competitive advantage depends on it
Beyond the Model: Why AI Platform Architecture Matters More Than the Engine
Even if you choose the best AI model available, you can still end up with a system that fails spectacularly in production.
I’ve seen companies learn this the hard way over the past year. The demo pitch is always the same: “Watch our AI analyze this contract!” They upload a clean, simple NDA, ask it a straightforward question, and boom—perfect answer in seconds. Impressive stuff.
Then you try to use it on your actual contracts.
You know, the ones with three amendments, OCR scan artifacts, and cross-references to exhibits that may or may not exist in your system. Suddenly, that amazing AI starts giving you garbage outputs, missing critical information, or worse, confidently gives you wrong answers known as AI hallucinations.
The problem isn’t the AI model. It’s everything else.
The Ferrari engine in a broken car
Here’s an analogy that clicked for me recently: Imagine someone selling you a car by showing off the engine. They pop the hood, rev it up, talk about the horsepower and torque specs. Sounds incredible, right?
But what if that Ferrari engine is connected to a fuel system full of dirty gas, a transmission that slips, and brakes that only work half the time? You’re not getting to your destination safely, no matter how powerful that engine is.
That’s exactly what’s happening with most Legal AI implementations. Companies are obsessing over which language model to use while ignoring the critical systems that make AI actually work in the real world.
The dirty data problem
Let’s start with the foundation: your contract data. In demos, AI vendors use pristine contracts—clean text, consistent formatting, clear language.
But your actual contract portfolio looks nothing like that.
You’ve got contracts from the past decade in different formats. Some are native PDFs, some are scanned images with OCR artifacts. There are master agreements with multiple amendments, schedules, and exhibits. Some contracts reference other agreements that might be stored somewhere else entirely.
Feed this messy reality to even the most sophisticated AI model, and you’ll get messy results. The model can’t magically understand what it can’t properly see.
The best Contract AI platforms solve this at the foundation level. They implement what we call a “Contracts Object Model”, a systematic approach to cleansing, normalizing, and structuring contract data before any Gen AI ever sees it. They handle OCR cleanup, identify document relationships, and organize the complex hierarchies that exist in real contract portfolios. At Pramata, we do this for every customer. The importance can’t be overstated.
This isn’t glamorous work. It doesn’t make for exciting demos. But it’s the difference between AI that works in a demo and AI that works in your everyday life.
Context is everything
Even with clean data, most AI implementations fail because they give the model too much information at once. It’s like asking someone to find a needle in a haystack by handing them the entire haystack.
I’ve seen systems that feed entire 50-page contracts to AI models and ask them to extract key terms. The model gets overwhelmed, misses important details, or focuses on the wrong sections.
The results are inconsistent at best, dangerously wrong at worst.
Smart platforms implement what’s called Retrieval-Augmented Generation (RAG). Instead of dumping entire contracts on the AI, they first identify which specific clauses, sections, or provisions are relevant to the question being asked. Then they provide only that targeted context to the AI model.
Think of it like having a research assistant who first finds the relevant documents and bookmarks the important pages before handing them to the expert for analysis. The expert can focus on what matters instead of getting lost in irrelevant information.
This approach dramatically improves accuracy while making the AI’s reasoning more transparent and verifiable. You can see exactly which contract provisions the AI used to reach its conclusions.
The multi-agent advantage
Here’s where things get really interesting. The most sophisticated Contract AI platforms don’t rely on a single model doing everything. Instead, they implement what I call “a true mixture of experts”: different AI agents specialized for different tasks, working together in a coordinated pipeline.
You might have:
- Extraction agents that identify and categorize key contract concepts
- Validation agents that cross-reference and verify the accuracy of extractions
- Analysis agents that assess contract terms against company standards
- QA agents that are customized based on each customer’s specific requirements and subject matter expertise
Each agent is optimized for its specific role. Some models are better at document classification, others at logical reasoning, still others at natural language generation. By using the right tool for each job, the overall system performs far better than any single model could. This is exactly why the most successful Legal AI leverages the best available AI models and platforms rather than relying on one that’s been built just for a single purpose.
This is fundamentally different from the “panel of judges” approach some vendors promote, where multiple versions of the same proprietary model vote on outcomes. That’s just the same limited capability repeated multiple times. True mixture of experts means leveraging different AI capabilities optimized for different aspects of the workflow.
The hybrid intelligence approach
Here’s the key insight: the best Legal AI platforms don’t try to make AI do everything. They combine AI capabilities with deterministic, rule-based systems in a hybrid architecture.
AI handles what it does best—understanding natural language, interpreting contract clauses, identifying patterns in text. But deterministic systems manage the overall process flow, business logic, and validation checkpoints.
For example, when analyzing contract obligations, AI might interpret the natural language to understand what obligations exist and when they’re triggered. But a rule-based system manages the workflow logic, ensures all required checks are completed, and validates that outputs meet business requirements before any action is taken.
This hybrid approach gives you the language understanding capabilities of AI with the predictability and auditability that legal processes require.
You get intelligence without sacrificing control.
The demo vs. reality gap
I always tell legal tech buyers: Don’t just watch the demo.
Ask to see the platform work with real contract data that looks like yours. Or, even better, ask to see the platform work with your own data.
Only then will you be able to assess how it handles contracts with multiple amendments, what happens when OCR quality is poor, and if it can manage complex document hierarchies. You’ll also get to see how it performs when contracts reference external exhibits or related agreements.
The platforms that can handle these real-world complexities are built on solid architectural foundations. The ones that struggle reveal themselves as impressive demos built on shaky foundations.
Systems thinking for Legal AI
The most successful Legal AI implementations think in terms of systems, not just models. They recognize that reliable AI requires:
- Clean, structured data as the foundation: No amount of AI sophistication can overcome poor data quality
- Targeted context delivery: Precision beats comprehensiveness when it comes to AI inputs
- Specialized agents for specialized tasks: Different jobs require different AI capabilities
- Hybrid intelligence design: Combine probabilistic AI with deterministic control systems
- Seamless workflow integration: AI insights must enhance existing processes, not create new silos
What this means for your evaluation
When evaluating Contract AI platforms, spend less time asking “which model do you use?” and more time asking “how is your platform architected?”
Questions to ask:
Here are the questions that reveal architectural sophistication:
How do you handle messy, real-world contract data?
Look for detailed answers about OCR cleanup, document normalization, and relationship mapping.
What's your approach to context management?
You want to hear about RAG, targeted retrieval, and precision over brute force.
How do you ensure AI outputs are verifiable?
Strong platforms can show you exactly which contract provisions led to specific conclusions.
What happens when AI confidence is low?
The best systems have clear escalation paths and human-in-the-loop workflows.
How does your AI integrate with our existing systems?
Look for robust APIs and pre-built integrations, not just export capabilities.
The Platform Play
The Contract AI companies that will dominate the next decade aren’t those with the fanciest models—they’re those with the most sophisticated platforms. They understand that AI is just one component in a complex system designed to deliver reliable, actionable insights in production environments.
These platforms can integrate whichever AI models perform best for specific tasks. When GPT-5 or Claude 5 launches with better capabilities, they can plug them in immediately. When new specialized models emerge for contract analysis, they can incorporate those too.
Meanwhile, companies that bet everything on a specific model—proprietary or otherwise—find themselves locked into whatever capabilities that model provides, unable to adapt as the AI landscape evolves.
The future belongs to platforms, not models. Choose accordingly.
The Hallucination Problem: Why "Cautious AI" Isn't the Answer
“Our AI doesn’t hallucinate because it flags uncertainty instead of guessing.”
I’ve heard this pitch at least a dozen times in the past six months. Legal AI vendors position their “cautious” models as more reliable because they say “I don’t know” instead of making things up.
It sounds responsible. It feels safer. And it completely misses the point.
The hallucination problem isn’t about what AI chooses to answer—it’s about the garbage data and broken context you’re feeding it in the first place. A cautious AI working with messy input is like a careful surgeon operating with dirty instruments.
The caution doesn’t fix the fundamental problem.
After watching legal teams struggle with AI reliability for the past year, I’ve become convinced that the industry is solving this problem backwards. We’re trying to make AI models more careful instead of building systems that eliminate the conditions that cause hallucinations.
The "I don't know" trap
Let me paint you a picture: You’re reviewing a complex commercial agreement with three amendments, trying to figure out the current payment terms. You ask your “cautious” AI for help, and it responds: “I’m not confident about the payment terms. Please review manually.”
Helpful? Not really. You’re back to doing the work yourself, which is exactly what you were trying to avoid. The AI’s caution didn’t make your job easier—it just added an extra step to your existing manual process.
Now imagine a different scenario. You ask the same question, and the system responds: “Based on Amendment 2, Section 3.1, the current payment terms are Net 30 with a 2% early payment discount if paid within 10 days. This supersedes the original Net 45 terms in the base agreement.”
The AI is giving you a specific answer backed by specific contract provisions. You can instantly verify it’s correct, and if you disagree, you know exactly where to look. That’s not cautious AI—that’s confident AI working with clean, structured data.
Two philosophies, two different outcomes
The industry has split into two camps on AI reliability, and the difference is fundamental:
- Camp 1: Make the AI more careful
These vendors focus on model-level solutions. They train AI to recognize uncertainty, flag ambiguous situations, and essentially punt difficult questions back to humans. The AI becomes a very expensive filter that tells you when to do manual work.
- Camp 2: Engineer away the uncertainty
These platforms focus on system-level solutions. They clean and structure data upfront, provide precise context to AI models, and implement verification layers throughout the pipeline. The AI operates in an environment designed for accuracy.
Guess which approach actually solves problems for legal teams?
The foundation-first strategy
The most reliable Contract AI systems, like Pramata, start with obsessive attention to data quality. Remember the messy contract reality I described in my last post? That’s not just a performance problem—it’s a hallucination factory.
When AI models encounter contradictory information, missing context, or unclear references, they do what they’re trained to do: generate the most plausible response based on patterns they’ve learned. Sometimes that’s accurate. Sometimes it’s completely wrong but sounds convincing.
The solution isn’t to make AI more hesitant. It’s to eliminate the conditions that cause confusion in the first place.
Here’s how the best platforms approach this:
- Document Normalization: Before any AI sees your contracts, sophisticated systems clean up OCR errors, standardize formatting, and resolve document relationships. When Amendment 2 references “Section 3.1 of the Agreement,” the system already knows which agreement and which section.
- Hierarchical Understanding: Complex contracts with multiple amendments create layers of modified terms. Smart platforms build comprehensive maps of which provisions are current, which have been superseded, and how different documents relate to each other.
- Contextual Precision: Instead of asking AI to analyze entire contracts, they identify the specific clauses relevant to each query and provide only that targeted information. No noise, no contradictions, no irrelevant details to confuse the analysis.
When AI operates on this kind of clean, structured foundation, hallucinations become rare because the conditions that cause them have been engineered away.
The Retrieval Revolution
Here’s a concept that’s transforming AI reliability: Retrieval-Augmented Generation (RAG). Instead of relying on what AI models remember from their training, RAG systems retrieve specific, relevant information for each query and provide it as context.
Think of it like the difference between asking someone to recall contract terms from memory versus handing them the specific contract sections and asking them to analyze what’s written there. The second approach is inherently more reliable because it’s grounded in actual source material.
But here’s the key: RAG only works if your retrieval system is sophisticated enough to find the right information. If your contract data is poorly organized, your retrieval will be poor, and your AI outputs will be unreliable regardless of how advanced the underlying model is.
The best Contract AI platforms implement what I call “surgical RAG”—they don’t just retrieve relevant documents, they identify the specific clauses, provisions, and legal concepts that directly address each query. The AI never sees irrelevant information that could confuse its analysis
Deterministic control in a probabilistic world
Even with perfect data and precise retrieval, AI models are fundamentally probabilistic. They work with probabilities and patterns, not absolute certainties. For legal applications, you need predictable, auditable processes.
The solution is hybrid architecture that combines AI intelligence with deterministic control systems. Here’s how it works in practice:
- AI handles natural language understanding: Interpreting contract clauses, identifying non-standard terms, extracting key dates and obligations. This is where AI large language models excel—understanding human language in context.
- Deterministic systems manage process flow: Business logic, validation checkpoints, escalation rules, and integration workflows. These are rules-based systems that behave predictably and can be audited.
- Verification layers catch edge cases: Before any AI output affects business processes, verification systems check for consistency, completeness, and logical coherence.
This architecture gives you the language understanding capabilities you need from AI while maintaining the reliability and predictability that legal processes require.
The multi-agent verification approach
You may have heard of “AI agents” which is a simple way of explaining the idea of a type of AI that’s specialized to do one thing, and do it very well. That’s why the most sophisticated platforms use a multi-agent approach to implement multiple layers of AI verification, each specialized for different types of accuracy checking.
These include:
- Extraction Verification: One agent extracts information, another agent validates whether the extraction is complete and accurate based on the source material.
- Consistency Checking: Separate agents verify that extracted information is consistent across related documents and doesn’t contradict established facts.
- Confidence Scoring: Specialized models assess the reliability of each piece of extracted information and flag items that require human review.
- Subject Matter Expert Integration: Account-specific agents trained on each customer’s particular contract standards and business rules provide customized validation.
This isn’t just multiple AI models voting on the same answer—it’s a coordinated pipeline where each agent has a specific verification role. The result is AI outputs that are not only accurate but demonstrably reliable.
Real-world reliability in action
Let me give you a concrete example of how this works in practice.
A customer needed to analyze thousands of supplier agreements to identify which ones had specific quality assurance requirements that needed updating.
A “cautious AI” approach would have flagged every contract with quality language for manual review. That’s not helpful when you’re dealing with thousands of agreements.
Instead, the Pramata Contract AI platform:
- Normalized all contracts into a consistent structure, resolving amendment hierarchies and document relationships.
- Implemented targeted retrieval to identify only the sections dealing with quality assurance requirements.
- Used specialized extraction agents to identify current QA terms versus superseded ones.
- Applied customer-specific validation based on the customer’s quality standards and business rules.
- Provided verification trails showing exactly which contract provisions supported each conclusion.
The result? The Pramata system confidently identified which contracts needed updates, which were already compliant, and which had ambiguous language requiring human review. The AI was decisive where it could be reliable and appropriately cautious only where genuine ambiguity existed.
The confidence vs. caution spectrum
Here’s the key insight: reliability isn’t about being universally cautious—it’s about being appropriately confident based on the quality of available information.
When AI has clean, structured data and precise context, it should give confident, specific answers. When information is genuinely ambiguous or incomplete, it should flag that uncertainty. But the goal is to engineer systems that maximize the first scenario and minimize the second.
The Contract AI platforms that achieve this balance are those that invest heavily in data foundations, context precision, and verification systems rather than just training AI models to be more hesitant.
Questions that reveal real reliability:
When evaluating Contract AI platforms, here are the questions that separate systems built for reliability from those built for demos.
How do you handle conflicting information across multiple contract amendments?
You want to hear about hierarchical document modeling and systematic conflict resolution.
Can you show me exactly which contract provisions support each AI conclusion?
Strong platforms provide complete audit trails linking outputs to source material.
What happens when AI confidence scores are low?
Look for sophisticated escalation workflows and human-in-the-loop integration.
How do you customize AI validation for our specific contract standards?
The best systems adapt their verification logic to each customer's business rules.
How do you prevent AI from analyzing irrelevant contract sections?
You want detailed answers about contextual retrieval and precision targeting.
The reliability dividend
Organizations that implement truly reliable Contract AI systems see a different kind of ROI than those stuck with cautious-but-unhelpful tools. Instead of getting AI that tells them when to do manual work, they get AI that actually does the work reliably.
- Contract reviews that used to take hours happen in minutes.
- Obligation tracking that used to require manual calendar management becomes automated.
- Risk analysis that used to depend on attorney availability becomes instantly accessible to anyone with access to the platform.
This isn’t about replacing lawyers—it’s about freeing legal teams from routine, high-volume tasks so they can focus on strategic, high-value work that actually requires human judgment.
The path forward
Ultimately, the problem of AI hallucination won’t be solved by making AI more cautious. It will be solved by building systems that eliminate the conditions that cause hallucinations while maintaining appropriate skepticism where genuine uncertainty exists.
This requires platform thinking, not just model thinking. It requires investment in data foundations, not just AI algorithms. It requires hybrid architectures that combine the best of probabilistic AI with the reliability of deterministic systems.
The Legal AI vendors that understand this distinction are building the platforms that will define the next decade of legal technology. Those that don’t are building expensive cautious assistants that will be quickly surpassed by truly reliable alternatives.
Choose reliability engineering over artificial caution. Your legal team—and your bottom line—will thank you.
If you’re ready to see how Contract AI can work at your organization, contact Pramata today for a custom demo of our Enterprise Grade Contract AI that actually works.