Corporate transactions take months (or years) of hard work out maneuvering the competition and PE firms while also obtaining government approval. Anyone who has been through one remembers the long nights of intense negotiations to ensure that all risks, obligations, and opportunities are identified, neglected, and managed before the deal is done.
However, once the deal is done is when the real hard work actually begins – ensuring the ROI of a recently acquired company, divested product line, new clients, new assets, or new properties that are being brought into a company’s processes and culture.
Deloitte, in their State of the Deal, M&A Trends 2019 report, cites 40% of respondents saying half of their deals over the past two years have failed to achieve ROI objectives. About a third said gaps in integration execution during the acquisition led to the weak value and ROI.
Our solution empowers teams to prioritize, provide or understand context, and then scale large review projects in an actionable way. Combining structured and semi-structured data is an art form that can provide immense value when aligned with a singular goal that ties it all together. Heretik helps ensure the organizations are aligned on deal expectations, requirements, and execution at a department level.
Global banks provide global loans to other banks and property managers to help them with large property investments.
Large scale property managers are in charge of multiple tenants and properties, so every detail and term has a huge impact on their business.
ABC (purchaser) is buying all the shopping center properties owned by real estate company XYZ (seller). They need to review the leases that XYZ has signed with all of its tenants to determine what restrictions are in place and will have to be assumed by ABC when the deal closes.
Lender makes a loan to real estate company ABC that is secured by ten shopping centers it owns. In the event of a default on the loan, the lender needs to be able to assume all rights and liabilities of the landlord.
Banks use algorithms to determine what package of mortgages they want to acquire that will generate profit for them. They need to review these mortgages to identify information like durations, fixed or adjustable rates, rate amounts, whether the mortgage has signatures, etc. before they can acquire these mortgages.
Watch how Bricker & Eckler is tackling oil and gas lease review and abstraction projects using Heretik Analysis
Heretik’s machine learning models help identify all lease agreements and related legal documents that can then be unitized to ensure all relevant context is available in a single easy to view interface.
Heretik’s segmentation analysis and workflow functionality allows for key sections of leases to be identified and easily compared amongst the rest of the leases to understand the variances of each critical section.
Heretik’s keyword search, term highlights, and regular expression analysis allows for every key term and data point to be identified and with a single click a reviewer can be taken to where in the lease that key term was identified to ensure the full context is available for a proper interpretation.
Curious how Heretik utilizes machine learning to help turn an enormous amount of information into digestible and well-organized data? In this blog, we’re focusing on high-level overviews of our contract analysis techniques!
We love sharing our clients’ wins with the world! We partnered with our friends at TransPerfect to create an infographic on a recent tech-enabled due diligence project. By using Heretik, they successfully cut the review time down by nearly one fourth of the time, reallocating strategic resources to more meaningful work. Check it out!
Part one of this series covered how our Segmentation and Classification analysis models work. In part two of this series, we are covering how our new analysis models, Extraction and Unitization, work!
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