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Can AI and OCR replace manual accounting entry? Discover the future of automated accounting.
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Last update:
May 7, 2025
5 minutes
A Fortune report shows that poor supplier contract management can cost up to 9% of annual revenue. Instead of static PDFs, turn contracts into a searchable, filterable database. With OCR and AI—especially LLMs—that’s now possible. Let’s see how.
Using LLM and OCR, extract key data from your supplier contracts to better anticipate renewals, manage risks and identify cost savings opportunities
Why do procurement and legal teams struggle to manage supplier agreements?
Here’s what happens when you don’t extract the right data
Good news, yes. But it’s not really only an OCR (Optical Character Recognition).
It involves two main steps:
It spans from recognizing characters to understanding the meaning of legal clauses. In that sense, contract OCR isn’t just a scanning tool—it’s a document data management system.
It doesn’t just extract information; it also deduces, calculates, and infers the key data points your team actually needs.
Analytics means having a complete, centralized list of all your agreements—each enriched with the key information you need to take action.
Here are a few examples of the insights you can extract:
With structured data, you can filter, sort, and build the aggregate key figures you need.
Let’s focus on building a business-oriented list of key information.
The goal is to avoid getting overwhelmed by overly legal or irrelevant details that don’t serve your procurement workflows.
In most cases, you don’t need to dig into every legal nuance—what matters is capturing the actionable data that helps you manage suppliers, control costs, and anticipate risks.
You can test this exact approach using your free Koncile account, which includes 50 credits to get started.
This may seem simple at first, but legal naming conventions can quickly complicate things.
If contracts aren’t accurately linked to the correct supplier entity, the risk of errors increases significantly.
For example:
To avoid these issues, your system must support reliable automatic matching with your supplier database.
And because accuracy is non-negotiable here, the tool should include a human-in-the-loop process to flag and correct mismatches or uncertain cases.
Thanks to the combination of OCR and large language models (LLMs), you can now automatically assign the right category to each contract—based on the full context, not just keywords.
But categorization isn’t always obvious.
Take this example list of categories:
Now imagine you’re dealing with a contract from NovaLex Solutions, which provides analysis and design of cloud infrastructure.
At first glance, this could fall under either Business Process Outsourcing or Contractor Intellectual Services.
In this case, it should be assigned to Contractor Intellectual Services, because it involves expert-based, knowledge-driven services delivered by independent professionals.
So how can AI make the right classification?
We’ve built very detailed list of procurement contract categories. You can take a look.
Some contracts include tacit (automatic) renewal clauses, which can catch you off guard.
If you miss the termination notice deadline, it’s often too late—you’re locked in until the next renewal cycle.
That’s why early detection and proactive alerts are critical.
Here’s how AI can help anticipate renewals:
First, it spots the right termination clause and the exact date:
Second step, in your prompt or extraction logic, you can include buffer period to trigger early warnings. For instance, if the agreement requires a 90-day notice, the system should raise an alert by July 25, 2025, giving your team enough time to act.
Following the Pareto principle, identifying your most valuable contracts helps you prioritize the ones that matter most.
Let’s focus on the 20% of agreements that drive 80% of your spend.
As a rule of thumb, your data capture model should include two key fields:
For many contracts—like SaaS subscriptions—this is relatively straightforward.
But for others, such as telecom agreements, it can be more complex. The value might depend on variables like the number of phone lines or the scope of services across your entities.
That’s where AI inference becomes valuable.
To improve the accuracy of AI-estimated values, include contextual information such as your company size, number of users, geography, and legal entities in your prompt or extraction settings.
This helps the model make better assumptions when values are not explicitly stated.
Service Level Agreements (SLAs) and penalty clauses are often underutilized by clients.
They’re negotiated up front—but rarely enforced or even revisited.
Yet these clauses can be critical for performance monitoring, leverage in renegotiations, and financial recovery in case of service failures.
So how do you capture them efficiently?
This approach makes SLA and penalty data usable—not just stored.
Not all contracts can be exited immediately—and knowing which ones you can terminate now versus those you need to wait out is critical for proactive contract management.
Many procurement teams would love a simple, actionable list of agreements they can walk away from today, without penalties.
But beware: termination for convenience often comes with conditions or financial consequences.
For example:
“If termination is initiated by the Licensee for convenience, an exit fee equivalent to 25% of the remaining license term’s value shall be payable to the Licensor within 30 days of termination.”
To capture this effectively, your data model should include:
This gives your team the ability to make informed decisions—well ahead of renewal or negotiation deadlines.
By capturing and centralizing the payment policy of each supplier, you can better manage your working capital and reduce exposure to late payment penalties.
For example, Koncile is based in France, where the law imposes a standard maximum payment term of 30 days, unless otherwise agreed in the contract. Knowing when exceptions apply is key to staying compliant and negotiating favorable terms.
And this isn’t just theoretical—payment delays can be costly. 55% of all invoices issued in the U.S. are paid late, and small businesses typically receive payments 8 days after the deadline.
Capturing this data lets you stay ahead of due dates, flag risky terms, and avoid preventable losses.
The goal is to be able to filter and segment your agreements by geographic area—so you can focus efforts where they matter most.
To do this effectively, you can include simple fields in your data capture model such as:
Kickbacks and rebates are typically annual refunds or discounts based on total spend—and they’re often overlooked. If not tracked, they can quietly expire, leaving valuable savings on the table.
To capture them effectively, your data model should include:
Provide as much context as possible—like your company’s spend volumes, entities, and historical contract values. This helps the AI better assess whether the rebate is relevant and worth pursuing.
It may sound like pure legalese, but liability caps are essential for good risk management.
These clauses limit the amount a supplier can be held responsible for in case of damages—and directly affect your ability to recover losses in the event of a dispute.
While interpretation and enforcement may depend on local laws, it’s crucial to flag them early.
In most cases, your legal team should be looped in, as these clauses are often subject to strict legal frameworks.
While relatively rare in procurement contracts, exclusivity clauses can limit your ability to engage with alternative suppliers or competitors.
Even if they’re unusual, it’s important to detect and monitor them—they can affect your sourcing flexibility and negotiating power, especially in critical categories.
This clause is more common in M&A or strategic partnerships, but it’s worth watching for—even in supplier agreements.
A change of control clause may impose restrictions or termination rights if the supplier is acquired, particularly by a competitor.
While uncommon in operational procurement, it’s good practice to track these clauses in case corporate ownership shifts unexpectedly.
Sounds a bit legal stuff, but for good managmemnent, and risk control, you must be warned about any lilimitaeiton to damages that can be imposed, and we’ll limit your ability to
Though, let’s put the legal department in the loop for them, as they are strictly encadrées by the applicable law.
even if they are very rare, you want to know about them. You’re restricted to deal with another supplier, another
Beware of this for M&A purposes. Highly unrealistic in procurement agreement. But again you need to know them when they arise. Notably if the company is acquired by a competitor of your supplier. One never knows.
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