Contract changes: when to customize a model to categorize them
Suppose a client returns a revised contract. They change a paragraph's layout and, elsewhere, how often a report must be provided. Document comparison highlights both edits. The coordinator still needs to decide who should review them. An AI model can suggest change categories defined by the company, with the wording before and after each edit visible.
Syntalith
Syntalith proposes an application for reviewing and categorizing changes that connects two identified contract versions with suggestions for the coordinator. The employee sees both passages, checks the category, and directs the change to the appropriate reviewer through the team's process. The work starts by comparing a conventional list of differences with an existing model. Fine-tuning, or further training an existing model, is worth assessing when the team keeps correcting the same category errors.
A highlighted difference and its place in the review
In our example, the coordinator compares files labeled “version for discussion” and “version after client comments.” The first says, “Report provided monthly”; the second says, “Report provided weekly.” The team has agreed on a category called “reporting frequency change” and routes those passages to the service owner participating in the review. In this example, the team has defined the categories and assigned the reviewers.
Elsewhere, the paragraph's line breaks have changed. Under the team's rules, that suggestion falls into the formatting category. The coordinator can distinguish the two entries without sending the entire list to every participant. The person reviewing reporting frequency still reads the full context and assesses the wording. A category organizes review; it does not determine legal consequences or acceptance of a change.
Start with text comparison
If the team primarily needs to locate edits, start with the tool it already has. Microsoft Word can compare two documents and, by default, display differences in a third document without changing the originals. That list may be enough when the coordinator can quickly identify the reviewers needed.
Conga also describes semantic comparison of selected clauses, showing similarities and differences once an administrator enables the feature and grants access. If the company uses such a tool, assess which parts of the review this existing capability already supports.
Categorization becomes a separate task when many passages are marked and the team regularly organizes them under the same topics. The model then receives actual differences between the selected versions, along with surrounding text. A new passage alone does not show what changed. The employee also needs to confirm which two files should be compared.
An existing model can be assessed after explaining the company's categories. Its suggestions should stay beside the source passages so a reviewer can correct them. Comparing this work with today's manual labeling helps establish whether fine-tuning is needed.
Experts need to agree on the categories
“Editorial change” can mean different things to different people. One person uses it only for layout; another includes reworded sentences. Before customizing the model, the company's experts need to clarify what belongs in the category and when a change needs individual assessment.
If the meaning of revised wording depends on a reference to another section, a short pair of quotations may be insufficient to assign a category. The review screen should allow the employee to return to the documents and leave the category unresolved. The coordinator can still see that entry and refer it to the designated expert. A change does not disappear from the list because the model could not classify it.
Keep the meaning of a category separate from the person currently reviewing it. A change in team responsibilities calls for an updated reviewer assignment. It need not require retraining the model that recognizes reporting frequency changes.
When further training is justified
Fine-tuning needs reviewed examples. Here, an example is a change between two versions with a category confirmed by an expert. Customization is worth considering when, despite clear definitions, an existing model repeatedly confuses categories that the team can distinguish consistently.
Comments from past negotiations may be insufficient on their own. A note saying “accepted” records a decision in a particular matter but does not explain the type of change. Preparation requires reviewed assignments and the context the reviewer relied on. The team needs to agree on how contracts and comments will be used before model work begins.
On version pairs not used during customization, the coordinator and experts can see which categories the model still confuses and how much work remains before assigning review. They also examine entries with unresolved categories: every actual change should remain visible with its source so it can be referred for assessment.
What to discuss with Syntalith
The proposed project covers category assessment and the coordinator's workspace, connected to the current document workflow within an agreed scope. Syntalith compares the available approaches and assesses adaptation where recurring errors justify it. Your team provides category definitions, explanations of difficult changes, and rules for assigning reviewers.
For an initial conversation about the application, describe a change the coordinator had to reread to choose a reviewer. We can establish whether better use of document comparison is enough or whether the team needs category suggestions linked to specific passages. See Syntalith pricing for information about working with us.
Match a model to the task you need it to perform
Describe where your current AI falls short. We will compare model customization options, data requirements and the cost of running the resulting system.
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