Caffe Deep Learning Framework vs. Litera Kira

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Caffe Deep Learning Framework
Score 7.0 out of 10
N/A
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research and by community contributors.N/A
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.N/A
Pricing
Caffe Deep Learning FrameworkLitera Kira
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Caffe Deep Learning FrameworkKira
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeRequired
Additional Details
More Pricing Information
Community Pulse
Caffe Deep Learning FrameworkLitera Kira
Top Pros
Top Cons
Best Alternatives
Caffe Deep Learning FrameworkLitera Kira
Small Businesses
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10

No answers on this topic

Medium-sized Companies
Posit
Posit
Score 9.1 out of 10

No answers on this topic

Enterprises
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Conga CLM
Conga CLM
Score 8.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Caffe Deep Learning FrameworkLitera Kira
Likelihood to Recommend
4.0
(1 ratings)
7.6
(21 ratings)
Usability
-
(0 ratings)
7.6
(21 ratings)
Support Rating
-
(0 ratings)
7.5
(21 ratings)
User Testimonials
Caffe Deep Learning FrameworkLitera Kira
Likelihood to Recommend
Open Source
Caffe is only appropriate for some new beginners who don't want to write any lines of code, just want to use existing models for image recognition, or have some taste of the so-called Deep Learning.
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Litera
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.
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Pros
Open Source
  • Caffe is good for traditional image-based CNN as this was its original purpose.
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Litera
  • UI/UX - tagging and naming feels much easier than you'd expect machine learning to feel
  • Accuracy - Kira's built-in models perform well out of the box
  • Assistance - Kira's support team gets back to me same day if I have a question
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Cons
Open Source
  • Caffe's model definition - static configuration files are really painful. Maintaining big configuration files with so many parameters and details of many layers can be a really challenging task.
  • Besides imagine and vision (CNN), Caffe also gradually adds some other NN architecture support. It doesn't play well in a recurrent domain, so we have to say variety is a problem.
  • Caffe's deployment for production is not easy. The community support and project development all mean it is almost fading out of the market.
  • The learning curve is quite steep. Although TensorFlow's is not easy to master either, the reward for Caffe is much less than the TensorFlow can offer.
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Litera
  • 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.
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Usability
Open Source
No answers on this topic
Litera
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.
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Support Rating
Open Source
No answers on this topic
Litera
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.
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Alternatives Considered
Open Source
TensorFlow is kind of low-level API most suited for those developers who like to control the details, while Keras provides some kind of high-level API for those users who want to boost their project or experiment by reusing most of the existing architecture or models and the accumulated best practice. However, Caffe isn't like either of them so the position for the user is kind of embarrassing.
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Litera
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.
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Return on Investment
Open Source
  • Since we stopped using Caffe before it can reach the production phase, there is no clear ROI that can be defined.
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Litera
  • Positive ROI: Increased comfort level of attorneys and use of tech
  • Neutral ROI: It has not significantly change how we handle projects, since there still is a need for manual review
  • Negative ROI: It has been cost prohibitive to scale it
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ScreenShots

Kira Screenshots

Screenshot of Login ScreenScreenshot of Kira DashboardScreenshot of Document & Contract ReviewScreenshot of Training Custom Smart Fields