Gavagai vs. Kortical

Gavagai is well suited for a B2C business that receives a lot of customer feedback in a form of open-ended text. It makes life easier for the customer experience team to efficiently identify the strengths and areas of improvement for the business. It saves a lot of time and also the hassle of analysing text data manually. It is not just a word cloud tool that shows you the words with the most number of mentions. Gavagai directs you towards actionability.

Incentivized

Kortical is really widely applicable to many use cases, although it doesn't handle images or video it is great to help you build really great ML models without needing to plan ahead what you are going to try, you let the platform build you the best model. It is suited to beginner and more advanced data scientists as you can edit the code to narrow the search space which makes model creation more you build it without AutoML. Hosting the model behind an API that is ready to go is great as it saves so much time vs doing that dev work from scratch

• Simplifies text data.
• Lets you customise your dashboard.
• Sentiment analysis
• Supports 47+ languages

Incentivized

• The NLP models results were much better than the ones that we did outside of the platform.
• It is really easy and quick to build a good model with a lot of the manual boring tasks all done automatically like one hot encoding, etc.
• Kortical shows the features and their importance for any model type as part of the platform which is great for understanding the models.

• Auto generated filters based on the data uploaded.
• Adding more Indian regional languages.
• Competitive pricing

Incentivized

• It would be ideal to have Jupyter built into the platform, they say it is coming.
• Also while it is easy to use, at the start it would have been helpful to have more help guides.

Their support is great as we use Slack and we have our own channel and they always respond really quickly. Data Science support is available to help unblock you as well as dev support as we're setting up the data feeds. It would be great if there were more FAQ or self-help guides in the platform but the personal touch is also really appreciated and probably gets us there quicker anyway.

I didn't evaluate many options while choosing Gavagai, I had explored a few local vendors whose capabilities were either incomplete or were not up to the mark. Their customer support was also quite poor. Also, the tool was debugged enough which led to frequent crashing. Alchmer although is not a direct competitor to Gavagai, since it's more of a customer feedback tool with additional capabilities of text analytics. I found Alchemer to be extremely expensive. Zonka on the other hand was quite welcoming to feedback from me and promised to develop additional capabilities for my specific requirements although the plan didn't go through due to internal reasons.

Incentivized

• Helps save man hours.
• Reduces the effort required to analyse big data.
• Helps to act upon areas of improvement faster.
• Indicates the strengths or things that are particularly liked by your customers.

Incentivized

• ROI is great as what we would spend on compute we get the AutoML for essentially the same price so it is cost neutral as Kortical comes with compute built-in.
• The results mean that we can automate so much more than our previous model so that is key to the positive ROI.
• The platform auto trains new models and lets us know when there is a better model so it has saved a lot of time so we can focus on new business problems to solve with ML.