Updated: LEVERTON’s deep learning tech accelerates German real estate deal for Freshfields as relationship grows
In a good example of a specific client deal using AI-backed data extraction, Freshfields Bruckhaus Deringer in Frankfurt has leveraged the deep learning technology of German-founded extraction and document management vendor LEVERTON for an accelerated real estate transaction for leading German investment company Conren Land AG. This is the latest in a succession of projects that the Magic Circle giant has worked on with LEVERTON – following the signing of a framework agreement in 2016.
Conren Land, which focusses on acquiring and selling office spaces, purchased the ‘Machtlfinger Höfe’ office building located in Munich, built in 2003. The building is comprised of 21,500 sqm of leasable space and includes over 300 parking lots.
Freshfields used LEVERTON during the due diligence process, simplifying the time-consuming and resource intensive process through the automated extraction of relevant key data points such as rent charges, break options or overhaul clauses. LEVERTON also provides a traceable audit between a structured data output and underlying documentation.
Freshfields was able to improve its service offering by making use of the LEVERTON platform in order to identify missing documents and discrepancies in the rent roll compared to the documentation existing within their data room.
Since signing the framework agreement in 2016, Freshfields, which also uses Kira Systems, has now worked on around 10 projects with LEVERTON. Other global law firm clients of the document extraction vendor include Baker McKenzie.
Where wins in this space can be vague and inflated, speaking to Legal IT Insider, global sales director Richard Belgrave said: “We want the market to realise how we apply our technology to real life, tangible deals and use cases.”
In February, Sports Direct announced that it has engaged LEVERTON for its lease data management in a multi-year engagement. This engagement will see the FTSE-listed retailing group make use of LEVERTON’s multi-language capabilities for its portfolio in Continental Europe. Maintaining a centralised repository of all critical operational data is crucial and using the LEVERTON platform, the retailer is able to ensure that all information regarding their assets across various regions, in multiple languages can now be easily extracted, accessed and interrogated.
Belgrave told us: “Sports Direct wanted to better understand its portfolio – and we did it better and in a cheaper way and with transparency.”
The wins follow fundamental changes to LEVERTON’s product, which originally took unstructured documents and presented structured data in Excel. Belgrave says: “We soon realised that lawyers don’t like Excel. So we created an external app outside of the core platform, which allows customers to extract that data using AI, but put it in a fact sheet based in Word – lawyers love word, and they love fact sheets.”
That change was made in Autumn 2017 and Belgrave says: “We are now using it to benefit quite a few of our clients, particularly in the United States.
“It has made a difference in the legal sector and definitely re-opened doors of firms we previously worked with went quiet. The app also allows us to do some neat things with the data. We just did a huge project with a UK bank that wanted to validate the data in an Excel spreadsheet that LEVERTON had captured. They transformed the data to a side-by-side comparable document with the data that the client had given us so we could spot mistakes.”
Our original report said that Freshfields was based in Munich but this has been amended to Frankfurt, we’re sorry for the error.
We were originally informed that Freshfields took advantage of recent changes to the way LEVERTON presents gathered structured data: in a lawyer-friendly Word document instead of Excel. While LEVERTON has made this change, as detailed above, we have subsequently been told that Freshfields did not use a Word report in this instance and are happy to amend the article to reflect that.