Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
$0
per month
Kira
Score 7.6 out of 10
Enterprise companies (1,001+ employees)
Kira, now from Litera (acquired August, 2021) is software that searches and analyzes contract text. Kira offers pre-built, machine learning models covering due diligence, general commercial, corporate organization, real estate and compliance. Using Kira Quick Study, anyone can train additional models that can identify any desired clause. Kira can be deployed on virtual data rooms and other large repositories of contracts, creating summary analyses.
Kira is a great due diligence tool and can be well utilised on both large and small transactions. It also has good application if you are looking to compare multiple documents against a model form document or market standard templates. Kira is less useful if you are looking to review emails (e.g. as part of a disclosure exercise); or if your review involves non-Latin based script languages.
User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.
Inability to relabel smart fields to suit the review process means it is hard to align it to particular projects (e.g. it would be useful to relabel the "Assignment" smart field as "Is the contract assignable?")
Not enough non-English smart fields.
Needs the ability to resell user-trained smart fields in a marketplace.
Output is not customizable enough.
Built-in analysis tools are useful but a little basic.
If our firm had more contracts in English, the usability of Kira would be rated higher. However, since we have to train clauses in Portuguese in order to use Kira, it makes its usability lower. We still are not able to fully use Kira for reading contracts in Portuguese. It takes a long time and many associate hours to make Kira usable in other languages.
Customer Support is excellent. The online help portal is probably the best I have ever seen. Great videos with content easily found. The HelpLine is staffed by knowledgeable people. The videos have saved us providing a lot of in-house training, which we would struggle to resource. The account managers really know the product and their law firm clients and share best practices and trends.
It is easier to learn, it has a very cost effective license for use, it has native build and created for Azure cloud services, and that makes it perfect when compared against the alternatives. As a Microsoft tool, it has been built to contain many visual features and improved usability even for non-specialist users.
Kira offers a lot more out of the box than other providers and is also more flexible around integrations. This, plus the clear pricing structure, is why we went for it instead of (or as well as) others. Diligen, RAVN, Leverton, Della, Seal not in list.
Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details
