Frequently Asked Questions

Getting to Know Knowable

A Contract System of Record is the most authoritative data source housing an enterprise’s executed agreements, and key structured data surfaced from those contracts. Its purpose is to enable the enterprise to efficiently understand and confidently act on the obligations, entitlements, and risks embodied in its contracts.
No. Contract repositories house contracts (and usually other documents), often in hierarchical folders. However, typical repositories lack accurate contract metadata and cannot represent the complex agreement families that characterize real-world corporate contracting. Instead, repositories require users to open and piece together all the agreements with a counterparty to understand the contractualrelationship and answer basic questions.

There are three standard levels of contract organization.

Contract Cluster: The simplest contract organizational structure is a cluster, meaning all the agreements with a particular counterparty are co-located in a folder. Clusters may include many different, unrelated agreement families between counterparties (e.g., many of our users have both buy and sell relationships with the same counterparty represented in completely different agreement families.)

Contract Hierarchy: Contract “hierarchies” rely on chronology to organize agreements between two counterparties, listing agreements in order of their effective dates. While grouping agreements provides some head start, the reality is that contract families are not as simple as chronological order. For example, some amendments amend MSAs; others amend specific Scopes of Work, etc. With a simple chronological hierarchy, you must still read the contracts to confirm the relationships between the various agreements.

Contract Family: Family mapping shows the nuanced sub-relationships between related agreements, revealing the full, current state of the relationship with each counterparty without requiring users to open and read all the agreements.

To be effective, CSOR systems must be:

Complete: CSORs curate an enterprise’s entire contract portfolio or complete subsets of those contracts, such as those governing a particular function (commercial, procurement, sales), business unit, or region. It does not contain duplicates, drafts, or non-contractual documents such as emails or spreadsheets.

Searchable: Powerful filters and tagging enable users to quickly find agreements within the context of their contract family in a few keystrokes using minimal search criteria such as active status, counterparty, or effective date. More advanced systems may have hundreds of other data elements users can leverage to find answers and data.

Reliable: Effective CSORs use legal-grade quality control processes to transform legal prose into structured data with at least 98% accuracy so that users learn to rely on the system and confidently make decisions.

Family-Capable: About 2 out of 3 agreements in large enterprises live in families with related agreements (MSAs, amendments, Scopes of Work, etc.). Successful CSOR systems must be equipped to make sense of complex agreement families and represent the current state of the full counterparty relationship so that users don’t have to open and read all related contracts to answer basic questions.

Connected: Effective CSOR systems are usually connected to other ERP systems both upstream so that newly executed agreements flow into the CSOR and downstream so that reliable contract position data flows back into other user systems such as CLMs, finance, procurement, data lakes, etc.

No. Contract Lifecycle Management (CLM) systems are primarily workflow platforms that focus on the efficient creation and negotiation of new agreements. These important systems help accelerate contracting cycles and improve policy adherence in newly negotiated agreements.

Knowable is the Executed Agreements Company. We focus entirely on the management of contract information post-signature. Knowable often operates in customer environments with multiple CLM systems or as a standalone, acting as the single source of truth to ensure stakeholders across the enterprise know exactly what’s in their contracts in real-time.

CSOR is the most authoritative data source for executed agreements that houses all your contracts, makes them searchable, and is purpose-built to deliver business value through contract intelligence. Unlike CLM systems that trap contracts in a static repository, CSOR systems are a dynamic solution that enables users to:

  • Quickly answer everyday questions about their existing legal agreements, saving time, money, risk, and embarrassment. What used to take hours or days happens in seconds with a CSOR.
  • Easily ask and answer portfolio-level questions, like “which contracts allow me to raise prices this quarter?” “which counterparty relationships need to be amended to comply with this new data privacy regulation?” or “where can I eliminate redundant suppliers or avoid triggering minimum purchase requirement penalties?”
  • Determine where contracts are out of compliance with our policies and playbooks.
  • Feed clean, accurate contract data into other enterprise systems, solving the most destructive quality gap in the enterprise data landscape.

Because CSORs are explicitly designed to deliver real-time access to executed agreements, these systems eliminate much of the manual labor required to answer basic contract questions using CLM tools.

In addition, CSORs deliver pristine data quality and represent full counterparty relationships, including complex families of agreements. In doing so, CSORs make it easier for users to answer everyday contract questions and support enterprise profit, compliance, and regulatory initiatives.

Knowable and CLM systems are complementary. However, most users use CLMs for creating new contracts and Knowable for keystroke access to information in their existing contracts. Users prefer using Knowable with their other systems because it creates and delivers guaranteed 98%+ accurate contract metadata and represents complex agreement families so that users can see the full, current state of the counterparty relationship.

In environments with Knowable, as new contracts are executed, they are streamed from one or various CLM or e-signature applications into Knowable, where they are converted into structured data and added to the CSOR. In many applications, Knowable then cycles accurate contract data back into the CLM repository(ies) (if that’s the environment users are most comfortable in) as well as other enterprise systems that can benefit from accurate contract data, such as:

  • Procurement systems
  • CRM/Sales systems
  • Finance systems
  • Compliance systems
  • Audit/Third Party initiatives - Data lakes

While CLMs are effective at governing and accelerating the new contracting cycle, CLM contract repositories are their most common point of failure. This is partly due to technological limitations (text-based workflow platforms like CLM are very different from structured database technologies like CSOR/Knowable); and partly due to the fact that CLM repositories tend to rely on system users to populate contract data, which is a technical task requiring expertise, technology and time, none of which typical users have.

While CLM systems are used to efficiently manage the new contract creation and negotiation cycle, Knowable provides keystroke access to information about the positions in executed contracts (which represent 90% of a company’s revenue, cost, and risk at any given time!)

No. More and more companies are electing to tackle executed agreements first and use those insights to better evaluate the need for a CLM, and more smoothly implement CLM if they choose to proceed with one.

Implementation time for a Contract System of Record depends on the volume of contracts and the complexity of the data model required. Typical implementations take from two weeks for small enterprises with basic contract data requirements. However, implementation can take up to two quarters for large enterprises with sophisticated contract data requirements.

While Knowable uses ML to create contract data, and we believe our ML is the best in the world, our ML algorithms are embedded in a workflow that includes human legal experts to provide high levels of quality control.

In nearly all instances, legal applications require very high accuracy levels, which is why Knowable guarantees 98% accuracy in all our deployments. This level of accuracy is not possible with ML alone, especially when faced with complex families of agreements (e.g., an MSA plus amendments), which represent the vast majority of enterprise contracts.

Our solution is built on the premise that accurate data is absolutely necessary for most legal use cases. That’s why we marry our ML technology with human legal experts providing quality control to ensure we always deliver the data quality you need.

Knowable’s proprietary data creation workflow and platform include four layers of quality control review. We don’t just test a sample of our contracts and then guess at the accuracy level; we look at every single agreement and guarantee the accuracy level across your entire corpus. No guestimates here!

It’s far more expensive to lack access to contract information than it is to have it. Why?

Every day, businesses generate questions that require contract information. In most enterprises, these questions, which should take a few minutes to answer, trigger wild goose chases across various repositories, SharePoints, folder systems, and desktops. Simple questions can take hours, and when complex agreement families are involved, it can take days or weeks. One client of ours performed a time-and-motion study and determined that the personnel cost of answering contract questions was more than $10 million per year.

In addition, lack of access to contract data leads to revenue leakage, poor cost management, poor risk management, and reputational damage. In contrast, a Contract System of Record is relatively inexpensive, costing far less (in both money and time) than CLM and other enterprise technology implementations.