What is the difference between Lexnus and a standard CLM?

Standard CLMs manage contract workflows: routing, storage, signatures, reminders. They make the contract process faster and more organised. Lexnus enforces legal policy. It determines whether a contract meets your standards and stops it if it does not. These are different problems. Lexnus can be used alongside a CLM or as a standalone policy enforcement layer.

Is policy enforcement truly deterministic? Does AI never make the judgement call?

Correct. The AI model handles the interpretation step only: reading contract text and extracting structured values. The policy judgement is made by your rules, not by the AI. When the AI model extracts a liability cap of $5M from a contract, Lexnus's rule evaluates whether $5M exceeds your policy limit. The AI did not decide whether it was acceptable. The rule did.

What if our policy is complicated: multiple thresholds, different rules for different counterparties?

Playbooks support conditional rules, Smart Field dependencies, and contract-type-specific configurations. Rules can reference any structured data point extracted from the contract. Complex policy can be encoded faithfully. If your policy is genuinely too complex for structured rules, the affected provisions can be flagged for manual review rather than forcing a deterministic outcome on them.

How long does it take to go from zero to a running policy enforcement layer?

For most legal teams with existing Word templates and signed contracts: under an hour from upload to first analysis. Lexnus extracts your implicit policy from existing documents automatically. You review the draft, adjust, publish. Onboarding is included in all plans at no extra cost.

Does Lexnus replace legal headcount?

No. Lexnus handles the enforcement of standard, rule-governed issues. It does not replace lawyer judgement on novel situations, negotiation strategy, or relationship-sensitive decisions. The goal is to give lawyers back the time they spend on routine checks so they can focus on the work that requires judgement.

Use Cases

Automating Legal Policy Enforcement at Scale

Legal policy enforcement is the problem most CLM tools do not solve. Templates define what a contract should say. They do not stop the wrong thing from leaving. Every contract still requires a human to check it against standards that exist somewhere else, in someone's head or a document no one can find. The same violations recur. The same clauses get missed. The same liability caps get exceeded.

The underlying problem

Most GCs have defined their standards. Liability caps. Termination notice periods. Data protection requirements. Approved governing law. Indemnification limits. These standards exist: in template documents, in the GC's institutional knowledge, in email threads that explain why the last contract needed changes.

The problem is that these standards do not enforce themselves. Each new contract is a fresh opportunity for a deviation to pass undetected. The sales rep using the wrong template version. The lawyer who did not notice the counterparty changed the liability cap from 1x to 5x ACV. The business user who deleted the data protection clause because they thought it was optional.

The GC sees these issues when contracts come back broken, or when they happen to be in the review loop. Most of the time, they are not. The standard is known. The enforcement is manual. The manual enforcement does not scale.

The Lexnus model

Lexnus does not build a faster workflow around the contract. It builds the enforcement layer that the contract workflow has never had.

Policy as structured, versioned rules

Your GC encodes standards as rules in a Lexnus Playbook. Rules are written in plain language: "Aggregate liability must not exceed 1.5x annual contract value." "Data processing agreements are required for all counterparties handling personal data." "Termination for convenience requires 90 days notice."

Rules are version-controlled. When the GC changes a standard, the new version takes effect from the next published playbook. Historic contracts are linked to the policy version in effect at the time they were processed.

Deterministic enforcement, not AI suggestion

Policy enforcement in Lexnus is rule-based, not AI-driven. The AI model interprets contract text and extracts structured values, such as liability caps, notice periods, and governing law, from natural language. The playbook rule then evaluates those values against your standard. The outcome is binary: pass or fail. There is no probability score. There is no "the AI thinks this might be an issue." A rule is either satisfied or it is not.

Probabilistic AI suggestions require the lawyer to exercise judgement on every result. Deterministic rule evaluation removes the standard cases from the human review queue entirely. The lawyer's attention goes to genuine exceptions, the things that actually require judgement.

Enforcement across all contract flows

The same playbook governs every direction:

Outbound contracts assembled from approved clauses cannot contain language that violates a rule. The policy is built into the assembly process.
Inbound counterparty paper is analysed against the playbook before review. Violations are flagged before the lawyer reads page one.
AI-generated contracts drafted by ChatGPT, Copilot, or any other AI tool are subject to the same analysis. Lexnus is the policy check on top of AI output.
Contracts via MCP — when an AI tool operates in Word with Lexnus connected, every clause the AI model retrieves or generates can be evaluated against the playbook inline.

What scale looks like

A 200-person B2B SaaS company closes approximately 40 to 60 contracts a month: NDAs, MSAs, employment agreements, vendor contracts. With a two-person legal team, manual review of every contract is not viable. Some go unreviewed. Violations accumulate invisibly.

With Lexnus, every contract, regardless of who initiated it and what tool generated it, passes through the same policy check. The legal team reviews the exceptions flagged by Lexnus: the unusual terms, the high-value violations, the items their playbook routes for GC approval. Standard issues are resolved by the system. The legal team's work scales with judgment-intensive matters, not contract volume.

The audit trail

Every analysis, every approval decision, every published playbook version, and every clause change is logged in Lexnus's immutable audit trail. The GC can answer three questions at any time:

What policy was in effect when this contract was signed?
Was this contract checked against our playbook?
Who approved the exception in clause 8?

This is the infrastructure question behind legal policy enforcement: not just whether your policy is enforced today, but whether you can prove it was enforced on any given contract, at any point in the past.

Who this is for

General Counsel at B2B companies with 50–500 employees where contract volume is growing faster than the legal team's capacity to review everything manually.

Legal Ops leaders building a scalable, auditable contract function that does not depend on individual lawyers remembering all the rules.

Founders and CEOs at scaling companies who have started to see legal become a bottleneck and need a structural solution, not a headcount solution.