We recently ran a survey of legal professionals to test something we’d been hearing anecdotally from our customers for a while: evaluating whether AI tools will actually deliver on their promises is genuinely difficult. The results have been confirmed. 3 out of 4 general counsels and legal leaders that we surveyed agree that it is very challenging to assess the performance of legal AI tools, and over half of the survey respondents have been asked to do exactly that.
For legal teams already stretched thin, this is an obstacle to making good technology decisions.
So what makes evaluating AI tools so hard? And what should legal leaders actually be looking for?
Most respondents to the survey noted that their frustrations with evaluating AI vendors fell into three major areas:
Vendors promise too much: One respondent told us, “Many companies overstate the AI capabilities. The ideas are there and they may be starting down the road to development, but the reality is not there." In addition, we heard that many vendors show nice demos, but haven’t really dug into the actual use cases legal teams need. “With most companies, you really need a proof of concept to attempt to actually evaluate their product,” one respondent said. “Their usefulness in a demo or on a website just doesn't show how they would work for your use case.”
Every vendor sounds the same: If every vendor says they can do the same thing, how can anyone differentiate one from another? One survey respondent said, “The accuracy of AI is hard to define. Results vary dramatically based on prompt quality, document structure, data cleanliness, and user expertise. And, after a while, I get AI vendor merge where they all seem to offer the same software functions."
Verifying accuracy is difficult: Over a fifth of respondents mentioned this. Lawyers, rightly, are very worried about accuracy and hallucinations, and don’t want to do the manual work of checking and cross-checking AI. We heard from one senior counsel “Sometimes these products do not include the right information when trying to really narrow down a specific law or case. Sometimes I've found fake cases."
We know that attorneys are under pressure. In-house legal teams are leaner than ever and contract volumes are growing. Our customers tell us that increasingly, their leadership expects AI to be part of the solution. But that means that the stakes of adopting the wrong tool are very high.
The data reflects this tension. According to the ABA's 2024 Legal Technology Survey, 74.7% of attorneys identified accuracy as their top concern with AI implementation. And a Paragon Legal study found that over a third of legal professionals have relied on AI-generated outputs they don't fully trust.
Choosing the wrong AI legal tool isn’t just a waste of budget. In the worst-case scenario, it could introduce real legal risk. And when something goes wrong, and the person who championed the tool also has to explain the errors, the stakes become personal, not just professional.
No wonder lawyers are reacting strongly to a crowded AI legal tech market full of vendors making claims that may or may not be relevant to real-life use cases. The cost of failure is very high.
In a market where every vendor claims to have “AI,” here are the true differentiators:
The most prominent tools in this space are redefining what’s possible by collapsing implementation timelines, surfacing patterns across entire contract bases, and keeping playbooks automated and evergreen.
Quora’s approach to solving this problem was both comprehensive and well-suited to their particular needs. They identified seven criteria that were important to them as they considered how an AI tool would fit into their workflow: everything from the UI, to AI features, to customer support capabilities, to security.
Adrie Christensen, Legal Operations Lead at Quora, noted that the process involved defining clear success criteria with her general counsel, which they organized into a detailed scorecard for consistent vendor evaluation.
This evaluation framework gave them a good baseline to agree on what was important to them as a business. This gave them clarity and specificity about adopting technology to serve their needs and integrate into their existing ways of working.
For a starting point to develop your own framework, take a look at our whitepaper, The State of Legal AI: How to Futureproof your Tech Stack. It contains a simple decision-making infrastructure for you to use and customize when choosing AI solutions, as well as a checklist of what capabilities you should expect from your AI tool.
You might be familiar with the Yin-Yang symbol from Taoist philosophy. The black sections are yin - mysterious, chaotic, unknowable. The white sections are yang - obvious, ordered, legible. Yin and yang are fundamental properties of the universe. Just as the Heisenberg Uncertainty Principle balances any attempt to measure a particle's position with chaos in the particle's velocity, the universe conspires to balance any yang - legibility - with yin - mysterious chaos.
To non-programmers, software engineering is yin by nature. Bring any normal person into the engineering department and watch them marvel at all the black terminals and arcane symbols. Of course, this external yin is balanced out by internal yang - all the programmers know what's going on. We can, after all, read each others' code.
Yin-on-the-outside and yang-on-the-inside is the natural balance of a software engineering team. For the programmers, working inside the yang is generally a nice experience. Information flows smoothly, colleagues are helpful, and accomplishments are naturally rewarded with respect and admiration. A well-functioning software engineering team is a wonderful, collaborative environment - on the inside.
For the rest of the company, however, the yin on the outside is difficult and annoying. How is the company supposed to communicate its roadmap, when features just randomly emerge out of a mysterious black box?
The yin is particularly problematic when a non-programmer is granted administrative power over the team. How does the administrator decide who gets what work? Who to reward with raises and promotions or who to performance manage? These are all important questions that must be answered correctly for the engineering org to function properly, but they're very hard to answer from outside the yang.
Unable to penetrate the mysterious yin, the non-technical administrator responds by shining the flashlight of yang over the org. The flashlight usually takes the form of metrics - individual/team KPIs, semi-annual performance reviews, etc.
Unfortunately, Goodhart's law rears its ugly head:
When a measure becomes a target, it ceases to be a good measure.
Once individual performance becomes spreadsheetified, individuals re-orient their behavior to optimize their metrics. All job functions are vulnerable to this phenomenon, but software engineering is particularly vulnerable due to the enormous amounts of yin inherent in the system. The flashlight of yang is unable to eradicate the mysterious yin, merely to redistribute it - down to the atomic, i.e. individual contributor level.
An individual contributor whose compensation is tied to, say, shipping a specific feature, will prioritize shipping that feature over helping out their mates. Intentionally convoluted code will start infesting the codebase, so its maintainers can never be fired. Estimates balloon out of control, fiefdoms are carved out, and helpful information is jealously guarded.
The perfectly yang metrics in the administrator's spreadsheet come at a cost - the internal yang of the engineering team.
You can't have a double-yang situation. Law of the universe. Very sorry, take it up with Heisenberg.
At Ivo Engineering, we believe in working with the universe. We don't want to mess up the yang. As such, we embrace the following principles:
ICs are the bedrock of an engineering org. How do we ensure that our "holistic evaluations" of them are fair and understood? The following is a (non-exhaustive) list of qualities that managers will keep in mind when evaluating engineering ICs' performance, which will be done on a continual basis.
We believe that contracts are fundamental to business. Every professional relationship begins, is maintained, and ends with a contract. They are the cornerstones of human commercial activity. Without them, business simply wouldn’t exist.
Ivo paved the way for the category of AI-powered contract review. We recognized that manual contract review is a tedious, thankless task, and one that creates bottlenecks throughout the business. We’re proud to have solved this challenge for hundreds of businesses around the world, including enterprises like IBM, Uber, Atlassian, Shopify, and Canva.
But as we like to say, paper creates possibilities. Contracts aren’t just meant to be executed and forgotten. There’s a wealth of strategic data buried inside your contract portfolio, and Ivo’s AI-powered platform is perfectly suited to getting it out so businesses can make better, smarter decisions.
The core mission of our company is to turn contracts — and the people who work with them — into strategic assets for the business, enabling contracting teams to discover risks and opportunities for competitive advantage.
We believe that contract intelligence is as critical a business transformation as the advent of the personal computer. The data housed within contracts is a foundational source of business data; AI’s ability to enable organizations to extract and analyze that information will change the way they operate forever, and will be a critical difference between businesses that succeed and those that don’t.
Our new look and feel as a brand highlights the new importance of contracts and contract work in the enterprise, and evokes how powerful an engine of discovery and opportunity the contract really is. Contract work shouldn’t be monotonous. Instead, it should feel vital.
Early legal AI focused on automation; the goal was to move faster, reduce manual effort, and get through the backlog.
That work still matters. We often hear from our customers that they’re being asked to review more contracts in less time with fewer resources. Since we can save 75% of the time it would take to manually review contracts, it’s our pleasure to help them.
But this work is no longer enough.
Today’s in-house contracting teams are being asked to do more than review contracts. They’re expected to spot risks earlier, provide strategic data to the rest of the business, and offer good legal judgement, not just redlines. That requires intelligence, not just efficiency.
Ivo is built for this new reality. Our AI-powered contract intelligence platform doesn’t just help teams review contracts faster. It helps them understand the risk profile of their contracts, the relationships between contracts, and how contracts have changed over time, as well as where opportunities exist — just like talking to a colleague. Moreover, it helps unlock the intelligence that’s already embedded in these contracts, without having to manually examine each one to do so.
When we design our products, we think extremely carefully about how they are built and spend much more time than most vendors crafting the models, the reasoning, and the user experience. As our Head of Engineering, Didier Smith, puts it, “It's easy to build demo-ready contracting software, but the road to good contracting software involves hammering down a very long tail. Our willingness to spend months and years hammering down the tail is our biggest competitive advantage.”
Not only that, but we want to recognize the extremely powerful problem-solving skills lawyers and contracting teams bring to the business. “Lawyers are creative,” a general counsel told us. “They are strategic. We know that we have the ability to solve big challenges and find valuable opportunities. And with Ivo, we now can.”
When we thought about the next evolution of Ivo’s brand, we centered around adjectives like crafted, intelligent, and warm. We thought about our customers, who are solving the biggest strategic questions for their organizations. And we thought about the unique approach we take to building our product, which works to understand contracts like a lawyer would, not like a machine.
Our new brand mirrors the craft of producing our products themselves. Our platform is exciting. It solves contracting teams’ problems in ways they’ve never imagined. We hear from our customers on a daily basis that Intelligence and Review are “awesome” and “mind-blowing.”
Ivo unlocks the strategic and creative abilities of contracting teams. To reflect this important part of our purpose, we have partnered with a digital artist whose own work showcases creativity and technology together. Myriam Wares is a French-Canadian artist based in Montreal, who has worked with clients in editorial, advertising, and package design. We want to underscore the message that AI-powered contracting isn’t always about doing more things faster. Instead, we believe AI has the ability to help our customers do the right things well.
We’ve heard from our customers that sound legal judgement and AI together are driving teams forward. The partnership between human expertise and AI’s access to facts is what’s creating powerful outcomes for businesses of every size.
As AI continues to evolve, we’ll be continually evolving our platform. We'll be guided by our customers’ needs, grounded in our craft and focused on turning contracts into business insights.
The future of contracting isn’t just faster. It’s smarter and more strategic. That’s what we’re building at Ivo and why we’re excited about what’s to come next.
Want to see what Ivo can do for you? Schedule a demo.
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It wasn't long ago that I was a corporate lawyer in New Zealand with big dreams of changing the way people worked with contracts. I spent my days reviewing agreements line by line and digging through iManage folders for precedents I knew existed somewhere. I left and taught myself to code because I believed there had to be a better way.
Starting Ivo was an early bet that AI would transform how in-house legal teams work; not by replacing lawyers, but by giving them superpowers. At the time, "AI for legal" meant keyword search and basic templates. We had to convince skeptical GCs that AI could actually understand contract language.
We’re happy to share that we've raised $55M in Series B funding led by Blackbird. We're deeply grateful to our earliest believers and customers, as well as to the incredible team of builders who've joined us to turn Ivo into what it is today.
The legal teams succeeding with Ivo include the largest, most sophisticated, and most demanding in the world. Our customers span Fortune 500 enterprises to the fastest-growing technology companies. These are organizations that review thousands of contracts per year and need AI that actually works.
We've grown our ARR 6x over the last year because we've stayed relentlessly focused on helping in-house legal teams move faster without sacrificing quality. When we go head-to-head with competitors in trials, we win 85% of the time. Lawyers are skeptical and discerning; no amount of marketing dollars will trick them into working with you. The only way to earn their trust is by delivering a product that works.
Ivo's contract review tool lives inside Microsoft Word, right where lawyers already work, surfacing risks, suggesting redlines, and accelerating negotiations. Our contract repository transforms executed agreements from static files into searchable intelligence, helping teams answer questions about their documents, understand the web of relationships across their agreements, and visualize deviations from their standard positions.
We will use our new funding to deepen our platform, increase our enterprise capabilities, and expand internationally.
Ivo is designed for the unique needs of in-house legal teams, not law firms. We're doubling down on investments to build at the frontier of what's possible across the entire contract lifecycle:
We're also investing in professional services to ensure our largest customers are incredibly successful and we’re opening offices in London and New York.
What excites us most about AI is its potential to amplify human judgment. Legal work is nuanced and contextual. It’s fundamentally about helping businesses move forward, manage risk, and build trust through agreements. The best outcomes happen when experienced lawyers have tools that match the speed and complexity of modern business.
The opportunity in front of us is massive, but so is the responsibility. Legal teams are trusting us with some of their most important work, and we have to get it right.
If you're interested in joining us on this journey, we'd love to hear from you.
Every year, there is a flurry of predictions about what will happen with AI and how it will change and shape the landscape of work. And sometimes, those predictions even come true. Here’s a look at the year’s top predictions about AI that actually came true, and what that says about what we can expect in 2026.
The American Bar Association's Legal Industry Report documented a modest shift in legal professionals’ AI use: 31% of legal professionals used generative AI at work in 2025, up from 27% the previous year. While a 4-percentage-point increase might seem incremental, it represents continued momentum in an industry historically resistant to rapid technology adoption.
Other studies have shown that as many as 79% of legal professionals are using AI. This suggests individual adoption of AI is moving much faster than firm or corporate-level AI implementation. This is not a surprise, with governance concerns and risk management making corporate deployments slower than individual use of AI tools.
Stanford HAI's 2025 AI Index Report showed an increase in AI-related regulatory activity. AI-related legislative mentions rose 21.3% across 75 countries. The United States alone introduced 59 of those regulations. This confirmed the prediction that legal teams, when adopting AI, will need to navigate an increasingly complicated world of compliance.
A number of studies showed that AI tools have evolved from simple chatbots to specialized AI agents. Numerous vendors launched more sophisticated AI products capable of document drafting, search, and metadata extraction. This is indicative of a fundamental change in how legal technology delivers value. It is designed to augment and streamline actual legal work, and lawyers have an expectation that it will deliver work held to the same standard as they are.
Do the predictions that came true in 2025 hold a clue for what will come in 2026? We believe that they do. Here are our predictions for the state of legal AI in the coming year:
We spoke with Evan Wong, the CEO of Checkbox.ai, and he predicted that there would be an increasing capability gap between AI-native legal tech solutions and older, legacy legal tech solutions. He likened the transition to the transformation from paper to digital or on-prem to cloud.
He noted:
“We're now shifting from SaaS to AI-first, which is another technology transition that we're going through as an industry and as a society. As we step into 2026, the gap between companies that can move quickly on AI and those who can't will profoundly widen. My prediction is that buyers will no longer tolerate vendors that are not driven by AI. Buyers will want a best-of-breed approach to AI solutions.”
Legal teams know they need to measure the value of their AI investments, but there are a couple of obstacles to quantifying these tools’ effect on the business. Traditionally, ROI for legal teams has been measured in cost avoidance, risk mitigation, and operational efficiency. But trying to calculate the value of what didn’t happen vs what did is incredibly difficult. And even if legal teams had greater responsibility for revenue-generating initiatives, they’re difficult to attribute directly to the legal department.
When legal teams are evaluated using metrics designed for revenue-generating departments, there is a mismatch in expectations. Legal prevents disasters and mitigates risk, which are valuable to the business but don’t always fit within the neat ROI formulas that finance teams expect. As one of our customers recently told us, “I do not want to be viewed as a cost center. I want to be a business partner.”
Like it or not, in the current economic climate, it’s critical to be able to explain the value of AI tools in a way that finance teams understand in order to lessen the resource pressure on legal teams. The most forward-thinking GCs we’ve met face this problem head-on and define the metrics by which they’re measured themselves, in partnership with their finance colleagues, and we expect to see more of this in the coming year.
The increasing amount of AI-related legislation, as well as greater amounts of end-user adoption, is leading to the presence of in-house AI governance frameworks. We expect that an emphasis on AI oversight roles, vendor guidelines, and policy guardrails will become a standard part of AI implementation in 2026.
The 2025 predictions that came to pass reveal the secrets to successful AI adoption in 2026. In-house legal departments will set up governance, operational, and measurement guidelines for successful AI adoption. Companies will continue to be cautious. And buyers will expect more from technology that evolves extremely quickly.
Learn more about successful AI adoption with the following resources.
Uber reimagined the way the world moves. Ivo reimagined the way businesses deal with contracts. Today we’re announcing that Uber Technologies, Inc., the world’s leading mobility and delivery platform, has selected Ivo’s AI-powered contract intelligence platform to transform its legal operations.
Ivo was selected as Uber’s vendor of choice after a thorough selection process. “The evaluation involved various cross functional teams including Legal, Sales and IT. Uber selected Ivo because it was intuitive to use, demonstrated a high-level of accuracy, could work in multiple languages, and met its confidentiality requirements. Furthermore, the Ivo team was highly responsive to Uber’s needs,” Kate Gardner, Senior Manager, Contract Operations at Uber, said.
Uber needs a better way to search for specific and custom information inside their contracts without manual review. Many parts of the organization need business and legal insights from their library of agreements; Ivo will turn the process of extracting that data from a tedious slog to an operation that potentially could take minutes. In addition, Uber’s team wants the data to be presented clearly and integrated into their current technology stack, and Ivo’s ability to generate reports within its platform or within their existing workflows was particularly attractive.
“High-growth companies like Uber know that their contracts are a treasure trove of insights. However, extracting that information has always proven to be a difficult task,” said Min-Kyu Jung, CEO and co-founder of Ivo. "Our platform empowers legal teams to easily surface those insights and provide key information to the rest of the business, becoming a strategic cross-functional partner and amplifying their impact on the entire company.”
Ivo’s contract intelligence platform uses our proprietary AI engine, AiRE, to analyze contracts, identify key terms and risks, and reveal business intelligence that would traditionally require hours of manual review. The solution integrates with existing legal workflows, enabling global teams like at Uber to maintain their preferred processes while achieving efficiency gains.
This partnership underscores the growing adoption of AI-powered contract intelligence solutions among leading technology companies. These companies seek to understand the wealth of valuable information buried inside their business agreements as well as position their in-house legal teams as strategic business partners that can help their organizations grow.
Hundreds of in-house legal teams are using Ivo to transform their legal operations and transform their contract work into strategic insight. Schedule a demo today and see how Ivo’s contract intelligence platform could work for your business.
One of the most common questions we hear from our customers and prospects is whether products like ours can be built in-house or whether they need to be bought from a vendor like Ivo. And it’s a very good question. Many companies, particularly large technology companies, are used to building their own tools, which makes a lot of sense. These are companies with huge amounts of resources, the most talented engineers, and the technical know-how to build successful products.
One thing we often hear from customers is that they have to go through a robust cost-benefit analysis when considering whether or not to buy these tools. Buying a legal AI solution is not easy right now. There are so many different vendors saying so many different things, and it’s important for legal professionals to make the right decision on which tools will solve the challenges they face. And, in order to select the right tool, contracting teams have to filter through the noise of the myriad of different solutions in the market.
There are three important considerations to think about when calculating whether the right approach is to build a legal tool in-house vs. buying from a vendor. They are:
Figuring out the ways your organization would answer all of those questions will make the answer of whether to build or buy an AI legal solution more clear.
Companies usually undergo a very robust cost-benefit analysis of whether to buy a software tool, and many, many organizations believe that they can build a tool with the right functionality for their contracting teams more efficiently and effectively than buying from a vendor. There are many factors that go into this economic decision. The choice to purchase software creates costs in the ongoing subscription fees, possible professional services, and additional licensing fees, whereas building in-house requires an upfront investment in development resources as well as the hidden opportunity cost. Will the business decide that it makes sense to divert engineering talent to creating a tool for contracting teams? How will those resources be prioritized? And what happens to the project if something else takes priority? Ultimately this decision rests on whether a tool can be procured at a lesser cost than the time and resources to build it in-house.
One of our customers told us, “It’s been a while since we’ve had to deal with something that is so new and so powerful and yet could be such a big mistake if not implemented properly.” Legal and contracting professionals need to be able to trust their AI tools; it is a professional necessity. A small mistake could present unacceptable risk. Sometimes companies have the perception that a tool built in-house is created with more sophisticated technology than tools built by external vendors, and will therefore be more accurate and more trustworthy.
One of the things that we’re particularly proud of at Ivo is that our product is purpose-built for lawyers because lawyers are part of the custom implementation for every customer. We can therefore guarantee that our contract solutions will be accurate, trustworthy, and fit for purpose. Our contract intelligence solution, for example, has a 97% score against the CUAD dataset.
One of the thorniest challenges when it comes to software built in-house is the cost to maintain it. It’s an almost-universal problem; when the engineers who built custom software leave the company, they take their institutional knowledge with them, and there often isn’t documentation or community support that can answer questions or fix issues with the product. Important implementation decisions and key workarounds only exist in the developers’ memories, and when they’re gone, so too is that knowledge. The remaining teams have to struggle to comprehend undocumented code or unconventional design patterns without the context of why the technical decisions were made. And as the software ages, the problem gets worse. Integration with evolving business systems, security updates, and compatibility fixes become difficult without developers who understand the original code. None of the solutions are good: companies can hire an expensive consultant to rebuild the software or rebuild from scratch, both of which negate any cost savings of building in-house, or simply stopping the use of the tool altogether, which negates any benefit to adoption in the first place.
In order to solve the build vs buy question, organizations must honestly figure out not just whether they can build the software today — a thorny question even in the most well-resourced organizations — but also whether or not they can maintain and support it for years to come, even as team members leave the company. Every business needs to ask themselves about the right place to allocate their resources. Is building and maintaining custom software a core strategic business function? Or does that divert valuable engineering resources away from the core business? Being able to clearly assess that question, with dedicated consideration to the factors above, will go a long way to solve the build vs. buy conundrum.
We recently released Research, our legal resource tool that uses trusted, authoritative sources to and in-line citations to fill all your legal research needs. It’s extremely important to use trusted, citable sources for legal research, for obvious reasons, and Research gives you line-by-line citations so you can check the accuracy of the results.
For US matters, those sources include EDGAR / SEC Filings, U.S. Code and Congress.gov, eCFR and the Federal Register, as well as reputable indexes for court case retrieval. For questions involving UK/EU matters, Research uses materials from official databases like Companies House, Parliament, and the Court of Justice.
What kinds of prompts can you use with Research? The feature excels at case law and statute search, multi-jurisdiction research, and compliance. Here are a few example prompts that you can use with it:
Another capability of Research is that it can summarize new and relevant statutes very quickly for executive review or wider consumption. An example of a prompt for this might be:
Research is also able to take into account context about your company through the company profile feature. For example, if Ivo knows that your company is located in California without specifying a jurisdiction in your prompt, Research will look to statutes, regulations, and case law that are specific to California.
Research is a powerful feature and our customers report that it saves them a great deal of time and effort. If you’d like to see how it could be useful in your workflow, schedule a demo today. Happy researching!
Many professionals who have worked with contracts have faced this critical question: Where are all of our contracts, and what's actually in them?
For every organization, particularly those of significant size or age, this isn't a trivial question. It represents a fundamental risk. Every employment agreement, partnership arrangement, vendor relationship, and supplier agreement represents a legal obligation. And for many companies, the honest answer to where, precisely, these obligations are documented is that they’re not entirely sure.
Recently, a general counsel shared with us the experience of trying to centralize their organization's contracts. After a long, hard 12 months, they discovered contracts were scattered across nine different locations including individual employee hard drives. The risks represented by this were incalculable. If an employee’s hard drive crashed, that business relationship could be lost forever. And the 12 month effort wasn’t successful. Even after that investment of time and resources, they weren't confident they'd found everything.
This scenario is more common than it should be. Contracts are processed by different business units, stored where they're worked on, and filed in whatever system seems convenient at the time. Even when companies implement contract lifecycle management tools, their effectiveness depends entirely on consistent metadata tagging, which is a manual process that's both time-consuming and vulnerable to human error.
The risks become particularly critical during litigation or regulatory inquiries, when legal teams need immediate access to every relevant agreement. We’ve spoken with legal teams who face regular litigation and understand they must be able to surface all contracts with specific counterparties on short notice. Many organizations aren't this prepared.
If companies aren’t so proactive, the alternative is scrambling to reconstruct contractual history during a crisis, and that’s exactly the kind of fire drill that no general counsel wants to manage. Some organizations have simply accepted that certain contracts are permanently lost. But that acceptance can introduce unmanageable risk. This simply shouldn’t happen.
The contract management technology that many contracting teams work with is a simple repository where the contracts are tagged with metadata, which can then be queried with a rudimentary search system. This approach works when everything is properly tagged and filed, but metadata tagging being a difficult, manual process, there are always errors.
But what if the approach could be different?
Ivo Repository uses AI to extract information from contracts at scale, without requiring upfront metadata tagging. You can load your existing contracts from wherever they currently live and immediately begin querying them, identifying relationships between agreements, spotting historical changes in terms, and flagging potential risks or opportunities for renegotiation. Not only will you be able to find your contracts, but you’ll be able to get critical information out of them quickly and easily, without hours or days of manual work.
Schedule a demo and see just how powerful Repository is, and whether it could solve the challenges your company is facing with contracting work.
Connect with us now to learn how we can streamline your contract review process
