Month: December 2019

Re-Inventing Supply Chain Management — How Intelligent RPA and AssistEdge 18.0 is Playing a Role in Retail

Retail Forwards

Undeniably, the retail industry has evolved at a rapid pace over the years, and in order to stay competitive retailers have had to modify their strategies. The global retail sales is projected to be around $28 trillion US dollars by 2020, up from $22 trillion in 2016. Moreover, businesses are moving from growing physical retail stores to launching new digital sales models and acquiring businesses to enhance their fulfillment processes. The costs to increase market share continues to grow, which creates the need for retailers to hone in on areas where they can optimize the spend as well as generate greater value.

  • One such area to focus on is emerging technologies. Retailers are figuring out how to use these technologies to scale their operations and embed them into their way of doing business.
  • Supply chain as a differentiator. Making supply chain faster, more predictable and cheaper is crucial for retail survival.

Indeed, many areas of the retail business can be optimized. McKinsey estimates that over 50% of the work in the retail sector can be automated with activities ranging from logistics and supply chain management, accounting and finance, inventory management through managing customer campaigns.

Supply Chain Management Challenge

As mentioned earlier, supply chain in retail is fast-changing, and the use of emerging technologies should be leveraged for differentiation. With higher expectations from customers for faster delivery and cheaper products, retailers need to source their products from an ever-growing list of suppliers and have warehouses strategically located for expedited shipping. RPA can help manage all related processes, from stock management to creating necessary invoicing for these large operations. RPA also enables the seamless and accurate transfer of data between the different systems, including warehouse, finance and market reporting.

The Role of Intelligent Process Automation (IPA)

Though RPA is an initial first step for rules-based repetitive tasks to generate better efficiencies in overall inventory management, there are many aspects of data collection where some aspect of ‘intelligent’ automation is required. With inventory management, there may be exceptions in invoice processing depending on vendor and warehouse location that are out of the scope of regular rules-based only RPA software. Historically, retailers would need human management of these details, which would take significant time as well as cost. When it comes to IPA, Artificial Intelligence technologies including computer vision, machine learning and cognitive automation can help augment the human worker by assisting in processing the data further, so that minimal human interaction is required with data management and communications. With IPA, the transfer of data can be seamless, allowing the employee to focus on analysis and resulting in better-informed decisions.

How Can AssistEdge18.0 Empower the Supply Chain Process?

A Closer Look
In a related case, one of our clients, a global retail giant had challenges in automating their procurement processes that involved working with 2,000 vendors and standardizing processes across 60 warehouses. A typical process would require 3-way matching of purchase order, invoice and delivery receipt before entry could be made into SAP and vendor payments could be processed. This required manual intervention/human decision to verify the quality of goods, the quantity of goods, price and the invoice payment which often led to SLA failures, payment delays, and human error.

With AssistEdge 18.0, the client was able to leverage AI capabilities in addition to an element of human interaction to fully automate as well as ensure that the ‘last mile’ of the procurement automation process could be enabled with 100% accuracy.

  • AssistEdge RPA, powered by Albie, the cognitive engine, was implemented to automate the procurement processes. With Albie, the client was able to leverage the unique Computer Vision capabilities and complex Machine Learning models to identify and classify relevant parts for data extraction with over 90% accuracy. Identified data was then easily extracted into excel using Advanced OCR.
  • Where 3-way invoice didn’t match, and manual decision-making was required to approve these invoices, Albie sophisticated decision modelling was applied to verify the reasons for mismatch and to make recommendations on how the invoice should be handled based on inputs such as invoice amount, vendor category, the reason for mismatch and past history.

Outcomes:

  • 288 hours in time saved spent on processing an invoice
  • 80% reduction in cost of processing an invoice
  • 52% increase in invoice processing without human intervention

A process that could previously only be automated up to 10% using basic data extraction and deterministic automation was now fully automated.

For more case study details, please go to: https://www.edgeverve.com/assistedge/global-retail-giant-assistedge-rpa-18-0/

So how does this impact retail business?

With business still growing, albeit focused more in the digital arena, retailers need to move fast to service an increasingly demanding consumer audience. Using IPA, retail supply chain management can adapt to the needs for global sourcing and delivery.  There are many aspects to supply chain management, but as evidenced in the case study, procurement processes involving a multitude of suppliers can be streamlined and automated intelligently. As IPA becomes more mature, retailers will be able to continue to leverage the technology to enhance other areas of their operations and pursue with their aim of digital expansion.

Sources:

www.retailtouchpoints.com

https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet

https://www.statista.com/statistics/443522/global-retail-sales/

https://www2.deloitte.com/

Distributor collaboration for emerging markets

FMCG companies would be the first to admit that there is no one-size-fits-all solution for success across global markets. Just as the opportunities presented by mature markets differ from that of emerging markets, so do the challenges they throw up.

Emerging markets are typically dominated by traditional trade, where the key players are wholesalers or large distributors and channel intermediaries. Therefore, it’s imperative for brands to establish a symbiotic and collaborative relationship with their channel partners — one in which communication is seamless, and data flows easily both upstream and down. This is not only beneficial for the brands, but for every player in the ecosystem.

A collaborative distributor relationship helps all parties involved.

Local channel partners know their markets and customers intimately. Yet, most often, they are unable to adapt to their customer’s needs because the FMCG companies aren’t listening. A collaborative relationship helps the local channel partners serve their customer better.

Distributors, who have so far been working on ‘instinct’ will have access to data — and even predictive analytics — through a mutually beneficial distributor relationship. They can improve processes, manage stock better and run an overall more efficient system.

You, the brand, of course have the most to gain. You’ll hear more often and more clearly from channel partners, who will help you tackle the growing and heterogeneous set of demands that emerging markets pose. POS data from distributors and retailers will help you glean actionable business intelligence that will, in turn, help improve efficiencies and develop stocking and promotional strategies.

All of this can’t be achieved through ad hoc initiatives. Brands need proactive distributor collaboration programs. You need to groom distributor networks where individual distributors, whether large or small, work closely to implement corporate strategies on the ground and provide data on customer tastes and preferences.

One of our clients, a global CPG leader, rolled out a distributor collaboration solution across various markets they cater to, and benefited immensely from it. They gained:

  • ~12% revenue growth in under two years
  • 4-5% optimization in costs
  • 3-4% reduction in non-productive inventory
  • ~60% improvement in distributor data accuracy
  • 6-8 days improvement in the timeliness of daily secondary sales and inventory data

Preparing for a successful distributor collaboration plan

The first and possibly the most important step is to assess the particular market’s collaborative needs. What do you (the brand) want to achieve through this? Establish clear goals for yourself and for your distributors. Plan for the coming 1-3 years.

Next, create the right team to drive the project. You will also need to help your key participating distributors do the same at their end.

Be prepared for resistance from your distributors. How well you communicate their benefits from the program will determine how soon you get their buy-in.

Remember, even the most enthused of your distributors might need handholding through the assessment and implementation process. Create training and enablement programs to make the transition smoother.

Building a good distributor collaboration plan

When you are charting out a plan for the distributor collaboration program, follow the time-tested Build-Test-Scale approach. In the following table, we have outlined the three key phases you should plan for.

Often, a deal breaker here tends to be the training and education your distributors need. In our experience with clients, the simplest of steps have helped: simple workbook templates for inventory management, hands-on training on changed processes or the use of software tools, etc.

Second, we cannot stress enough the advantages of running a pilot. Trying out the collaboration program with a smaller, select set of distributors will not only be easier but will also provide visible results that can be used to onboard other distributors who might be resisting. Obviously, not all of your distributors will be in the same place in terms of (a) interest and conviction in the benefits of the program and (b) their technology and resource readiness to utilize it. Assessing them in terms of their readiness and maturity and identifying the first set of distributors with whom to run the pilot is very important.

Lastly, create a continuous feedback loop and establish checks and balances at regular intervals to ensure that the program is working as expected and continuing to benefit all parties. Another good initiative is an annual review to see how the program can be further improved/scaled; distributor participation here is a must.

If you are keen to explore how you can use technology to facilitate distributor collaboration, do get in touch.

Personalized bots in loan processing

With the evolution of the Indian economy and hordes of banking products and services on offer, the banking sector has seen exponential growth in recent years. The spur in the banking business has resulted in a tremendous reduction in application processing time, without a significant increase in the workforce. Consider an application in process for a commercial loan request comprising five directors — This application involves details such as credit rating and any previous borrowings for each from a credit rating portal, which takes around 15 minutes for processing each of the details. Imagine having a personalized bot that will not only help reduce the time taken to 2-3 minutes but will also be a morale booster for the processing office.

When it comes to loan processing, the request for a loan application goes through an entire complex process of approvals. Also, more recently, banks are under a lot of scrutiny to rectify their business processes, have minimal losses, and maximum repayment of loan amounts. Several activities are involved in loan processing — For instance, collecting data from multiple sources on a case-by-case basis especially in the analysis of a director’s profile and their net worth and borrowings for the organization who has requested for a loan. The need for robots to take up these tasks whenever required is the need of the hour as they help the processing officer achieve timelines as quickly as possible and having a personalized bot that is available 24/7 for the processing officer will also increase the efficiency.

What is a personal bot?

“Personal bots work on employee’s machine, mostly in attended form and perform tasks for the employee — pulling data from multiple sources to create reports, storing client contact data and even creating regular presentations.

Personal bots can be looked at as a digital concierge for employees in an organization. Through advanced mobile interface and virtual assistants, employees can interact with these personal bots installed on their office machines/systems. The personal bot triggered on-demand or scheduled by the employee, can perform tasks on behalf of the employee, even in his/her absence. Just like a digital concierge, this personal bot on the employee’s machine will be well-equipped to take requests and execute.”1 The personalized bots will prove to be a quick help for executing tasks that are mandated in the approval cycle that is followed by the bank authorities. As a part of commercial loan processing, each loan account undergoes a review cycle at the end of the financial year to have a check on the account health which involves the following:

  • Obtaining details of the installment paid over the past financial year
  • Borrower organization’s director details are re-verified to check if any additional loan has been sanctioned
  • One-to-one comparison with the planned cash flows to actual

The time frame in which these details are required varies as there is no specific rule when the processing officer will require the information. This is where a personalized bot comes to the rescue. With a personalized bot, the processing officer can receive the details for further analysis on need basis in a few clicks. Depending on an application, these details can be fetched at the first stage to pass the criteria. However, the initial check can be skipped, and the details can be acquired at a later stage if any of the directors have a borrowing with the bank.

Consider a home loan application process — It needs manual intervention because it is a pre-approved project by the bank and it funds maximum 80% of the property amount, but there has been no given rule to the applications asking for more and is taken case-by-case for approval. Usually, the application is approved based on a lot of factors including credit rating, existing loans or income of the applicants. The processing officer requires these details so he/she can share them with the sanctioning authority and make them available on specific sources like credit rating websites. The officer can also use a personalized robot to obtain these details via a unique ID like PAN card, which will be beneficial considering crunch timelines.

The biggest bottleneck in the banking industry is the repetitive nature of activities being performed which end up taking a lot of time because the tasks run on old-school methods. Identifying the tasks/deliverables by individuals will not only help save time but will also reduce the turnaround time, thereby increasing the efficiency for each person.

Personalized bots as a resource, if immediately available in loan processing cycle for the agent will yield quick returns with the same workforce in the department. The banking industry is evolving and so is the requirement of customers. For communication on what the status of the customer’s loan application is, aiding the processing officer with a personalized bot will serve the purpose and increase customer service, giving you an edge over your competitors. Also, quick processing can be one of the USPs, giving your organization an X-factor in the market.

Addition of personalized bots will play a significant role in bringing about digital transformation, empowering enterprises to reach new heights. So let’s start!

Sources:

[1] https://www.edgeverve.com/assistedge/blogs/digital-concierge-embracing-future-bots/

Prevent Revenue Leakages with AI-powered Contracts Analysis

The word leakage can never leave us with a positive feeling. But when the leakage is that of revenue, it can be devastating, as it creates a huge hole in the enterprise’s pockets. In contracts management, revenue leakage is seen as lost opportunities. Revenue is lost when sales and billing are misinformed or misinterpreted. Apart from being a signed agreement between two parties, a contract also brings with it a lot of opportunities, which when tapped, can bring in more revenue than expected. Opportunities such as renewals, cross-sell, negotiations or billing timings are the time to revisit these contracts and improve their terms for long-term relationship with clients or suppliers, adding value to your businesses in terms of revenue and reputation.

However, when such opportunities are missed, it leads to revenue leakages. Though it is an unnoticed or unintended loss of revenue, statistics indicate that most companies end up losing anywhere between 1 and 5% of their earnings, before they can identify or realize the leakage.

A recent study by International Association for Contract and Commercial Management (IACCM) reveals that poor contract management can result in revenue losses up to 9.2 percent every year.

As someone new in procurement you may ask, how can a contract prevent or cause revenue leakage? Apart from the contract terms, which need to be followed to the tee to avoid revenue leakages, it is also the additional provisions within the contracts, which lead to creating value or leaking revenue for the enterprise. With hundreds and thousands of contracts being created and amended in an organization, it becomes nearly impossible to get into the details and manage the contracts impeccably resulting in value realization and avoiding losses. While, there may be various reasons of revenue leakage, the most common ones are errors in data entry, unpaid accounts, client management issues, incorrect reporting and discounting, which are rooted in lack of visibility, transparency, automation and accountability that have financial impact on the organization.

Recently, the city of New Orleans overpaid a whopping $4million, thanks to poor contract enforcement practices. In another case, revenue leakage was on a rise due to invoices being held up by customers or not being paid on time. According to a recent study conducted by Aberdeen Group, it was found that 13-21% of invoices arrive as incorrect or without complete information, which ends up being disputed and therefore unpaid. Such errors resulting in non-realization of invoices could affect both the top and bottom line.

How can AI-powered contract analysis prevent revenue leakages?

The first and foremost step towards preventing revenue leakage is to be thorough with the terms and conditions of a contract and its details. It could be information about the products or services covered by the contract, the agreed upon rate at which the invoice has been raised, the incentives or discounts, reimbursements or expenses, renewing terms, protection of intellectual property, or even the confidentiality clauses. It is the very crucial to know every part of the contract to be able to prevent revenue leakage. This can be particularly difficult when contracts are created, stored and managed by human resources. Enterprises need automated monitoring and alert mechanisms that keep the sales and finance teams on top of operations.

Preventing revenue leakage requires proactively monitoring contracts, while creating, amending and implementing. Identifying, recouping and preventing revenue leaks is possible by scrutinizing contracts and critically analyzing processes. With technological innovations such as machine learning, natural language processing (NLP), and text analytics, artificial intelligence (AI) is gaining traction from across business functions and processes including contract analysis and management.

A cognitive contracts analysis platform enables automatic monitoring of contracts, flagging whenever required and alerting the system to intervene, thus helping prevent revenue leakages. It consolidates the contracting processes into one single system, eliminating the need for multiple chain of e-mails. This helps streamline the review process eliminates duplication and clearly steers the focus to constructive discussions and contract closures.

It can also automate classification of risks and thus assign them to the right stakeholders, enabling automation of workflows, better cross-functional coordination, contract standardization, higher visibility and better enforcement of terms ushering in the best contract management practices.

By automating review and monitoring, contract analysis ensures that operations and sales teams are freed to do core work, without ever missing an important deadline or contractual obligation.

How XtractEdge Contract Analysis can Help

XtractEdge Contract Analysis utilizes advanced Machine Learning (ML) techniques to automate contracts extraction, risk analysis and review of unstructured contracts. It acts as a single source of truth to answer any kind of contracts-related information. It leverages ML techniques such as vision-based, semantics-based, and language-sequence-based to transform the process of analyzing and reviewing contracts, providing an intuitive workbench with different personas to configure and train ML models.

Conclusion

Most enterprises become an easy prey to the opaque and inaccessible contracts. As per IACCM, the cost of a standard, low risk procurement contract from draft to signature has gone up by 38% in 6 years costing almost $6,900. This is where AI-powered contracts analysis can be of help. Not only does it provide visibility and accessibility to contracts, it also simplifies the entire process and reduces the dependency on legal process outsourcing thus cutting down the legal operational costs, while empowering the in-house procurement teams. A clear contract analysis platform can help identify the anomalies, call alerts to rectify the same and help fulfill the contractual obligations, thereby preventing revenue leakage at all costs.

How to improve compliance postures through cognitive contract analysis

“If we keep doing what we’re doing, we’re going to keep getting what we’re getting.” – Stephen Covey

This is particularly true to contract management scenario of yesterday, today and tomorrow. Even the biggest players haven’t escaped the tangles of contractual non-compliance. The search engine giant, Google has been fined over $9 billion in multiple antitrust penalties levied by the European Union since 2017. Facebook lost its market share of over $119B as a result of user growth falling in the wake of the Cambridge Analytica data breach.

Businesses have suffered extensively over the years due to inefficient management of contracts. And they continue to do so. According to the International Association of Contract and Commercial Management, nearly 40 percent of contracts do not deliver their full expected financial benefits, which cost the enterprises an average of 10 percent of their annual revenues. Among the many reasons, non-compliance seems to be one of the significant ones. While, contractual compliance remains a critical function at any enterprise, leaving the legal and procurement teams with sleepless nights, non-compliant contracts can cost companies a fortune (literally) in penalties and reputation.

Importance of contract management in the 21st century

Today’s contracts aren’t documents to be archived, but strategic tools that drive operational value to the enterprise. With 60-80% of business transactions governed by written agreements, tracking and managing them effectively is an urgent requirement to extract maximum value from them. With globalization of contracts and ever-changing laws creating disparities in pricing and compliance, contracts need to be updated regularly to remain compliant. Organizations in the EU and outside of the EU who process the data of EU residents have to comply with GDPR. Totally, 89,271 data breach notifications have been sent from all data protection authorities in Europe, since May 2018* (Source: The European Data Protection Board). Though the Percentage of compliant contracts was voted “Very Important”, which is two times the Contract Authoring Cycle Time (Source: Ultria CLM Survey 2019), yet 75% of contracts lack KPIs and TCO reporting (Source: McKinsey). As per recent study, non-compliance costs twice more than the cost of meeting contract compliance requirements (Source: Ponemon Institute).

Adding to these woes are manual drafting and ad hoc monitoring, which expose enterprises to many potential risks. This is where AI-powered Contract Management solutions play a significant role.

Leveraging AI to improve contractual compliance

In the form of Machine Learning, Robotic Process Automation, intelligent risk analysis and Natural Language Processing, Artificial Intelligence takes Contract Management to a whole new level, simplifying creating, tracking and managing contracts while ensuring maximum compliance, thus reducing risk. By leveraging AI, enterprises can save significant time and productivity by automating compliance verification—the fundamental process in ensuring both internal and external compliance.

Cognitive contract analysis can help improve compliance posture by:

  • Performing contract classification, identifying what compliance mandates are applicable for each contract.
  • Automatically checking the extracted contract information for compliance with both company policy and governing regulations.
  • Enabling ease and speed of compliance verification.
  • Performing red lining of MSA with contracts.
  • Performing compliance risk analysis.
  • Automating the creation of compliance manifests.

By automating the review aspects, contract analysis presents you with all the information you need to stay compliant—file reports, renew certifications etc. It also proactively alerts you of compliance risks, reminds you of upcoming deadlines and prepares you to stay compliant even as laws evolve.

As contracts involve multiple stakeholders for review and modifications, each of whom have different needs, AI determines where contract clauses deviate from standard language and automatically routes documents to the right stakeholder for review & modifications. The approval process moves much faster when negotiators can quickly be informed how close the current version of the contract is to the predefined standards. It identifies clause deviation from defined standards, ensures addition of important clauses and update reviewer about any missing/redundant or even buried clauses and also suggests clause additions based on context. It also monitors compliance across the entire contract database, including the content of each contract, saving time and reducing manual effort.

In conclusion

Until very recently, enterprises were bearing financial losses due to non-compliant non-performing contracts. That need not be the case anymore. With the advent of AI technologies such as ML, NLP and cognitive automation, today’s enterprises can analyze their contracts and have real-time visibility of their risks at a fraction of the price and effort.

Apart from analyzing the risks and predicting future outcome based on the past trends, AI can also identify the root causes of the missed KPIs and SLAs by the third party and contribute to resolving such operational challenges. This enables insights on cycle times, deviations, risks, statistics (expiry, renewal, pending, etc.), procurement and sales business metrics. By leveraging AI and improving compliance postures through cognitive contract analysis, enterprises can experience unexpected financial recovery with increased visibility and forge healthier and truthful business relationships.

The XtractEdge Contract Analysis Advantage

XtractEdge Contract Analysis utilizes advanced Machine Learning (ML) techniques to automate contracts extraction, risk analysis and review of unstructured contracts. It acts as a single source of truth to answer any kind of contracts-related information. XtractEdge Contract Analysis leverages ML techniques such as vision-based, semantics-based, and language-sequence-based to transform the process of analyzing and reviewing contracts. It provides an intuitive workbench with different personas to configure and train ML models, thereby helping you ensure contractual compliance, mitigate risks and prevent revenue leakage.

How to Mitigate Risks with Contract Analysis

Hundreds of processes, thousands of people and infinite risks – This is how we define business. A complex landscape, which apart from employing talented and efficient people to carry out its functioning, also engages with several vendors and suppliers across the globe, adhering to a myriad of frequently changing local laws and compliance demands.

It is this complexity that exposes an enterprise’s operational teams such as procurement, vendor management and even human resources (HR)—to a wide range of risks throughout the contract authoring process, and long after. Why long after? Because, contracting is not a one-time exercise but a continuous process. One cannot simply create a contract, get the parties to sign and keep it aside. Contracts are like written constitution for conducting business between parties. One single issue of non-compliance can lead to inefficiency and cause huge losses. To avoid this, there are various questions to be identified in a contract and answered time to time.

  • Are all terms of the contract enforced?
  • Has the delivery been audited as per the contract?
  • Is the vendor meeting service level agreement (SLA) targets?
  • Are sub-contractor insurances up to date?
  • Is limitation of liability addressed?
  • Are background checks performed in accordance with the contract?

Most enterprises fail to answer these questions due to various reasons. Most often, the operational teams aren’t familiar with the exact terms and conditions of the contract, which leaves the contracts ambiguous, forcing the enterprises to collate all historical documentation and link related information only in the aftermath of a trigger, say a breach or when risks manifest.

Identifying Contracting Risks – First step towards successful contract analysis

Though contract risks pose a major danger, they are often overlooked by companies. Not only do companies lose out on revenue, they also become inefficient and put themselves in danger of noncompliance with a client or partner, risking their reputation and future business opportunities. Research findings say that inefficient contracts lead to losses between 5% to 40% for enterprises across industries. This is cited as one of the reasons why enterprises have started leveraging artificial intelligence to overcome such challenges of contracting. But before we explore AI for contracts analysis, let’s understand the three types of contract risk.

First one is Maverick contracting, wherein the contracts are executed that violate company controls to ensure regulatory or commercial compliance. Second risk is when the buyer or supplier doesn’t meet contract’s terms, thereby increasing the possibility of losses for the organization. Third is when a contract doesn’t perform as per expectations, causing losses for the organization.

To avoid possibility of such risks leading to financial losses, non-compliance and unfavorable lawsuits, it is imperative to have key procedures in place to identify and monitor contract risk. Effective contract risk management reduces risks and gives enterprises better control over their contracts. This enables the companies to control what contract language is used across the enterprise, recognize underperforming contracts and take informed actions, when a contract’s terms are not being met.

This can be done by leveraging AI and machine learning, which help proactively identify risk from contracts and send out alerts for overlooked risks.

Mitigating risks with contract analysis

As we often say, “god is in the details”. When enterprises have clear, specific and on-demand visibility into their contracts, it becomes easy to mitigate risks. A contract analysis solution can give you exactly this by breaking down contractual provisions (aka Intents) and surgical abstraction (aka Entities), enabling ease of reading and interpretation.

Manually performing these tasks would require weeks or even months of human efforts and basic automation might not provide accurate results. ML powered technologies help automate this process and give accurate and quick results. Empowered with advanced capabilities, leading solutions provide improved contract oversight, proactive risk identification, and continuous risk prevention.

A cognitive contract analysis can help identify operational risks by:

  • Analyzing risks at document, portfolio and intent levels.
  • Red-lining contracts and automating risk analytics.
  • Making risk-related predictions.
  • Performing workflow-enabled quality checks, when algorithms make a prediction, with straight-through processing for high prediction accuracy.
  • Allowing easy and unambiguous comparison of provisions and entities across documents.
  • Auto-detecting discrepancy in contract and related artifacts.
  • Auto-verification and approval of proposals / invoices against MSA, purchase order etc.

Conclusion

Ease of reading and interpreting the documents can mitigate revenue leakage and ensure accurate and timely recognition. With the advent of artificial intelligence technologies, machine learning, natural language processing and cognitive automation, enterprises are able to analyze their contracts and have real-time visibility of their risks at a fraction of the price and effort. By setting the right expectations and taking adequate steps, technology can assist you in managing large volumes of contracts efficiently and in a cost-effective manner.

About XtractEdge Contract Analysis

XtractEdge Contract Analysis utilizes advanced Machine Learning (ML) techniques to automate contracts extraction, risk analysis and review of unstructured contracts. It acts as a single source of truth to answer any kind of contracts-related information.

XtractEdge Contract Analysis leverages ML techniques such as vision-based, semantics-based, and language-sequence-based to transform the process of analyzing and reviewing contracts. It provides an intuitive workbench with different personas to configure and train ML models.