This launch has been several years in the making. Not because the technology was difficult, but because the problem it solves has never been a technological one. The real challenge in contracting has always been judgment, alignment, and consistency. And the story of how we arrived at a Standards-powered SimpleAI begins long before AI entered the mainstream.

Where It Started

In 2020, during my second year running TLB, a legal consultancy, my team and I found ourselves reviewing an overwhelming volume of NDAs. They made up nearly two thirds of our workload but represented only a fraction of revenue. It was obvious that something was broken. These agreements were low risk, rarely litigated, highly repetitive, and still chewing up thousands of hours across the industry.

I posted a simple question on LinkedIn: What if we all agreed to use one standard NDA?

What happened next reshaped my career. The post went viral. Within months, partners at major firms and in-house lawyers from global brands volunteered to help create the first version of oneNDA. More than a thousand lawyers peer reviewed it. By 2021, it had become the most widely adopted NDA standard in the world.

But oneNDA was never the destination. It was the beginning. Because the moment thousands of teams aligned on a single NDA, a larger truth became impossible to ignore:

Lawyers were not resisting standardization. They simply lacked a credible, neutral, expert-led way to do it together.

From a Single Standard to a System of Standards

When Law Insider acquired oneNDA in 2024, we had the chance to expand the idea that had started with that NDA. Law Insider brought something no other organisation could: the largest contract and clause library in the world. That data, combined with the community that shaped oneNDA, created the conditions for something new.

We began developing a principle based system of playbooks and templates, designed not as rigid documents but as frameworks for judgment. These playbooks capture:

  • How experienced in-house lawyers evaluate clauses
  • Why certain positions matter to the business
  • What fallback rules apply when language shifts
  • Where teams can be flexible and where they cannot

This was critical because templates alone are too brittle. Real negotiations rarely follow a script. Language changes, business models shift, and counterparties are unpredictable. What teams need is not just a template, but the underlying reasoning required to adapt.

This is why Law Insider Standards were built as living playbooks rather than static documents. Every fallback rule, every comment, every alternative position reflects not abstract theory but the lived experience of practitioners across industries.

Why These 50 Playbooks

People often ask how we chose the first set of playbooks. The answer is practical: these are the agreements negotiated every day across almost every company. NDAs, MSAs, DPAs, SaaS, licensing, vendor agreements, supply agreements, professional services, channel partnerships, contractor agreements, revenue share terms, commercial terms, and more.

If we wanted to help teams move quickly, we needed to start where the time was being spent.

These 50+ playbooks represent the most common points of friction we see in commercial negotiations. They cover the areas where internal alignment breaks down, where teams rely on institutional memory, and where junior lawyers are forced to guess.

They were built to solve the real problem: not drafting, but judgment.

How AI Entered the Picture

By the time we started integrating these standards into SimpleAI, one thing had become obvious: AI without standards is fast, but unreliable.

AI can summarise and redline all day long, but it cannot understand risk on its own. It does not know what is acceptable to your business, what positions you fought hard to establish, or what compromises you are willing to make.

Without grounding, AI produces text that looks confident but can be commercially harmful.

What changes with the SimpleAI Playbook Library is not the speed of the tool, but the quality of its judgment. When the AI suggests a redline, it is no longer guessing. It is applying structured logic drawn from expert developed guidance and community peer review.

This transforms AI from predictive text into applied legal reasoning at scale.

The Legal Engineering

Legal engineering is not a buzzword here. It is the discipline that makes this entire system function.

Behind every playbook in the AI Playbook Library sits the work of lawyers who specialize in translating legal judgment into structured logic that automation can use responsibly. These are practitioners who understand both the nuance of contract negotiation and the frameworks required to operationalize that nuance at scale.

Legal engineers serve three essential roles:

1. Translating expertise into structured rules
 Most legal knowledge sits in the minds of senior counsel or is buried in internal playbooks that only a few people understand. Turning that tacit judgment into explicit, reusable logic is an engineering task as much as a legal one. It requires breaking down clauses into decision trees, fallback positions, exceptions, acceptable alternatives, and explanatory notes that ensure both humans and AI can follow the same reasoning.

2. Creating systems that scale across teams and workflows
 A well designed playbook must work whether a contract is reviewed by a GC, a junior counsel, a business stakeholder, or SimpleAI itself. Legal engineers build this scalability. They anticipate edge cases, design for imperfect inputs, and ensure that the guidance holds even when counterparties deviate from expected language.

3. Ensuring AI outputs remain defensible
 AI does not set policy. It applies it. Legal engineers create the guardrails that define what “good” looks like for a given organization. They encode principles such as risk tolerance, commercial preferences, negotiating posture, and jurisdictional constraints. This prevents AI from improvising and ensures every output reflects the company’s standards, not the model’s guesses.

This work is not theoretical. It requires deep knowledge of contracts, risk allocation, and negotiation strategy, along with the technical ability to express that knowledge in structured frameworks. It is also continuous. As laws evolve, business models change, and new data emerges, legal engineers adapt the playbooks so the system stays current.

In other words, legal engineering is the backbone of standards driven automation. Without it, AI would simply accelerate inconsistency. With it, AI becomes a force multiplier for sound legal judgment behind It.
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Bringing standards into AI is not as simple as uploading a template. It requires legal engineering: translating expert judgment into structured rules that AI can consistently apply.

This includes:

  • Clause taxonomies
  • Principle based fallback logic
  • Conditional branches for alternative negotiation positions
  • Contextual commentary for why positions matter
  • Data informed insight from Law Insider’s contract library

This is where the combination of community, data, and legal engineering becomes so important. AI cannot set standards. Humans must. But once that judgment is encoded, AI can apply it rapidly, consistently, and transparently.

Where a lawyer may forget a policy or overlook a nuance after a long day, the system never does.

Why Standards Matter Now More Than Ever

AI has created a strange paradox in legal work. Teams can now automate more than ever before, yet risk drifting further away from defensible outcomes. Without standards, automation accelerates inconsistency.

Standards provide the governance layer. They give AI something to align with, and they give legal teams a way to scale judgment without scaling headcount.

This is not about removing lawyers from the process. It is about ensuring that every decision reflects the organisation’s principles, not the quirks of whoever happened to review the contract that day.

Where We Go Next

We are now launching a Steering Committee to refine, expand, and evolve these standards. Just as oneNDA was shaped by the legal community, the next phase of Standards will be shaped by the lawyers who use them in practice.

This work is not static. It will grow. It will adapt. It will continue to reflect the realities of commercial negotiation.

But the direction is clear: the future of contract review is not AI alone. It is AI anchored in standards.

Together, we are building a world where negotiation logic is shared, explainable, and scalable. A world where legal teams gain leverage rather than lose control. A world where technology amplifies judgment rather than replaces it.

And this is only the beginning.

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