Solutions

Our existing Solutions

Contract owners are not always aware of exposure and risks hidden in contracts. With our solutions, you will have the ability to auto-generate contracts, direct insight into the status of a process, find the right person to perform a certain (part of a) project or limit the overall costs of a fund caused by regulatory requirements 

Contract Analyzer

Compare clauses and find negotiation patterns in contracts

Red Flag

Analyze risks in income generating deals and contract

Intelligent Agreement Bot

Build and control contracts   

TeamSketcher

Effective (re-) allocation of teams and persons

 

Smart Fund

Create and operate an investment fund with the help of A.I. 

Checky

ML feedback on LMA contracts considering context and jurisdiction

Showcases

Our Solution Examples

Contract analytics

Compare clauses and find negotiation patterns in contracts

Description

As an organization grows it starts collecting many agreements both on the side of suppliers as well as on the side of its customers.  Over time, new clauses become in vogue or necessary but also the amount for warranties might show trends. Many of these insights are beneficial if upfront available when changing and adapting a contract.When sufficient history is available, it is possible to derive negotiation patterns on specific clauses either by sector, organizations involved,  etc.

Description

As an organization grows it starts collecting many agreements both on the side of suppliers as well as on the side of its customers.  Over time, new clauses become in vogue or necessary but also the amount for warranties might show trends. Many of these insights are beneficial if upfront available when changing and adapting a contract.When sufficient history is available, it is possible to derive negotiation patterns on specific clauses either by sector, organizations involved,  etc.

How does it work

To leverage potentials for an organization, our Genie framework was used to create the solution, an A.I.-driven solution that advises experts by making available the know-how previously locked-up in contracts. The solution regularly ingests new contracts and adapts to these by updating its models.

Although initially only with internal data, the solution now also leverages semi-public information to enhance the level of advice it can give. As part of the ongoing evolution of the solution, experts regularly provide feedback for example by correcting and annotating documents and the interpretation by the machine learning models. The machine learning models are expanded by feeding different types of contracts in it.

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Analyses of clauses across contracts and insight in clauses

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Insight in trends in contracts such as liabilities.

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Counterpart Analyses & Negotiation patterns

Red Flag

Analyze risks in income generating deals

Description

Redflag is conceived as a way to add more value to financial institutions and other advisors associated with a transaction such as tax advisors and lawyers. The premises is to leverage the wealth of information already available from past transaction to start profiling a new transaction in an early phase. The risks/rewards associated with the deals are adapted as more information becomes available. The solution does not intend to create a better Due Diligence process but should eliminate the need for it over time as predictions get accurate enough.

Description

Redflag is conceived as a way to add more value to financial institutions and other advisors associated with a transaction such as tax advisors and lawyers. The premises is to leverage the wealth of information already available from past transaction to start profiling a new transaction in an early phase. The risks/rewards associated with the deals are adapted as more information becomes available. The solution does not intend to create a better Due Diligence process but should eliminate the need for it over time as predictions get accurate enough.

How does it work

Although there is much effort in reducing the manual labor by automating due diligence, this addresses only the cost of a process step, but not the core: better risk assessment of the transaction. Hence Redflag starts with minimal information when a new transaction is still at its infancy phase and as more information becomes available the information is put into context using machine learning models. As larger real-estate deals develop over time with more and more information becoming available, often in an unstructured format such as contract texts, etc, these machine learning models employ a large variety of techniques like Natural Language Processing and Classification. Furthermore, deep Learning Models are used to predict risks. The training of machine learning models is done based on historical information available in the organization both in terms of transaction information, as well as the results after the transaction was completed. Where possible, external information is used to enrich the quality of the models, for example, leveraging regional economic indicators from the past as well.

The solution is based on the GENIE framework where the machine learning models and the human interaction need to be tailored to the organization using it based on in-house available data and details on the type of transactions it has to support.

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Use historic deal information to provide early prediction of risks

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Proactively advise on a deal before detailed due-diligence

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Improve the machine learning models with usage feedback

Intelligent Agreement Bot

Build and control contracts

Description

In international organizations that operate from many different locations, it is often complex to keep track of what is out there. Is an NDA signed or not, who generated it, and if it was signed, were there special risks taken in the agreement. To facilitate corporate legal departments, an A.I.-driven solution was created to track and alert on contracts that are agreed. In addition the system has at all points in time insight into the outstanding parental guarantees and notifies on the ones that have expired.

Description

In international organizations that operate from many different locations, it is often complex to keep track of what is out there. Is an NDA signed or not, who generated it, and if it was signed, were there special risks taken in the agreement. To facilitate corporate legal departments, an A.I.-driven solution was created to track and alert on contracts that are agreed. In addition the system has at all points in time insight into the outstanding parental guarantees and notifies on the ones that have expired.

How does it work

For this purpose, an intelligent agreement bot is created based on the Machine Learning models of our GENIE framework. This bot helps in managing contracts that the people in the field, such as sales, generate on behalf of the organization. The bot helps to allow effective review and monitoring of agreements/drafts provided by other parties, it supports the corporate legal team by reviewing on contracts and it keeps track of outstanding risks. The feedback from the corporate team is captured, so insights can be gained to update the machine learning models.

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Automatic Suggest changes for a specific Contract type (LMA / NDA)

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Automatic review if suggested changes have been incorporated in subsequent versions

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A.I. assistant to facilitate collaboration between corporate law departments and other teams

Team Sketcher

Build the best (international) team

Description

An increasing number of organizations run into the problem of finding the right person to perform a certain (part of a) project. Whether it’s a business expansion to a new country, or new, specific expertise is needed for a project, the fast-paced business world often requires making new connections and get insight into how to use your people best, considering time and financial constraints.

TeamSketcher has several functionalities. From selecting individuals to building entire teams, based on information available, either internally (company sources) or externally.

 

Description

An increasing number of organizations run into the problem of finding the right person to perform a certain (part of a) project. Whether it’s a business expansion to a new country, or new, specific expertise is needed for a project, the fast-paced business world often requires making new connections and get insight into how to use your people best, considering time and financial constraints.

TeamSketcher has several functionalities. From selecting individuals to building entire teams, based on information available, either internally (company sources) or externally.

 

How does it work

An algorithm starts with collecting important information that needs to be considered- such as geographical location, expertise, seniority, previous experiences. The user decides on the variables of importance. Once the algorithm is fed these contingencies, it sieves through the database, making sure to find all the matches. The first output is a list of potential candidates, that then gets pruned and refined. 

In the second step, the TeamSketcher gives an opportunity to combine the individuals and build teams, depending on relevant criteria. An unlimited number of skills can be chosen, and the algorithm will always make sure to respect the main criterion- resourcefulness. 

The algorithm can handle a huge amount of data and variables and can help to make decisions, potentially avoiding over- or understaffing, using, if available, even the information on the soft skills, personal career plans, and motivations, for a well-rounded team that can work together.

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User Historic Employee Information enriched with external data

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Leverage both formal and informal people networks

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Dynamic creation of teams and manual override to act as feedback to improve M.L. models

Smart Fund

Create and operate an investment fund with help of A.I.

Description

As investment funds are more and more subject to regulatory requirements, these requirements are more and more a burden on the overall costs of a fund. At the same time, transparency is required that investment funds make investments within their scope. To get this transparency, monitoring the activities linked to a fund’s purpose would be a great help. This is why the concept of A.I. assisted investment funds, so-called smart funds, was created. Leveraging a mixture of language processing to extract insight from agreements, as well as past funds that were managed.

 

Description

As investment funds are more and more subject to regulatory requirements, these requirements are more and more a burden on the overall costs of a fund. At the same time, transparency is required that investment funds make investments within their scope. To get this transparency, monitoring the activities linked to a fund’s purpose would be a great help. This is why the concept of A.I. assisted investment funds, so-called smart funds, was created. Leveraging a mixture of language processing to extract insight from agreements, as well as past funds that were managed.

 

How does it work

The machine learning models assist in dynamically adapting the subscription agreements based on the contextual profile of the investor in conjunction with other investors. In later stages, it will advise and warn on upcoming filings and/or potential issues. As the machine learning models have access to the underlying repository, they become like an assistant for a specific fund. The machine learning models can be created if an organization has sufficient in-house historic information on its investment fund or on investment funds it helped create. Also, small machine learning models are used to identify specific contract information such as specific dates/times in agreements that need to be monitored, legal entities mentioned, etc. Naturally, new regulatory requirements have to be added by experts. Although the machine learning models can be used to identify possible trends and regulations that might be coming as these often reside in documents from various regulatory bodies.

The solution of the  A.I. assisted Smart Fund was conceived by combining a number of solutions already created on the GENIE framework such as the contract analytics for Share Purchase Agreements and the Tax Monitor to monitor the corporate income tax for a large set of companies.

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Instant insight in options for investors based on context and Data-Driven storytelling

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Provide transparency by monitoring the activities linked to a funds purpose

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Dynamic Agreement Generator and Proactive advice on filings

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Monitoring & Suggestions linked to fund’s purpose

Checky

ML feedback on LMA contracts considering context and jurisdiction

Description

In banking, LMA contracts are highly standardized contracts, but they always require reviews and specific modifications due to various jurisdictions involved. Once a review has been done, often, the remarks need to be effectively traced to make sure they are not mistakenly excluded in the final version.

 

Description

In banking, LMA contracts are highly standardized contracts, but they always require reviews and specific modifications due to various jurisdictions involved. Once a review has been done, often, the remarks need to be effectively traced to make sure they are not mistakenly excluded in the final version.

 

How does it work

For this purpose, based on historic contracts available in multiple versions, machine learning models are created to make the initial suggestions for change, before the senior lawyer made a detailed review of the more complex parts of the LMA. The final review is also fed into the bot, to help keep track of properly incorporating the suggestions in the final agreement.

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Analyze LMA Contracts and automatically suggest changes for an LMA Contract

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Track versions of specific LMA Contracts and automatic review if suggested changes have been incorporated in subsequent versions