“We are seeing a lot of traction towards data side”

What are the offerings for BluePi for your targeted customers?

One was the cloud services; the second one was piStat where we spoke about the product. The third thing that we do is big data analytics which is AI, ML, data science piece as well as building online warehouses. And all of that is also on cloud. So you can’t really segregate these by saying this is cloud, this is big data. But the fact is that this needs a very different set of skill sets because this is not migration per se. It is more about how do you bring value out of the data that is already out there with the customer.

What’s your total customer base?

I would think it would be 50 plus for sure. On the media side I would think we have 10 plus large scale media customers who all have at least a 100 million page views plus. They are like big stuff. And on the cloud side last year we did about 12 migrations.

Tell us about your partnership with AWS? In terms of the partner relation how does that work?

We’ve been an AWS partner for 4 years or so. There are three primary areas that we focus on from a solutioning services perspective. The first one is what we call cloud services which is the piece where we essentially migrate large customers on to AWS or other public clouds from wherever they are hosted. AWS implementations are pretty unique to find to be honest. There are some examples where we have done such an engagement. Here it’s not so much about what AWS brings to the table. It brings of course a lot to the table. But then it is more about how do you solve a specific business model. For example we did GoIbibo which is like a very large migration from Apollo to Cloud. Now the interesting thing there is that when you get in there you realize that over a period of time they have built their solution. So every organization starts with a small NVB and then you add adjacent pieces on top of it and then it becomes a solution. So historically what also happens is a lot of the people who put the initial frameworks in place, initial solution in place move on. The biggest value that we bring to the table is first of all untangling that entire ball of wax, figuring out how you break this down into smaller pieces because you can’t just lift and shift. You can’t just say from tomorrow onwards you are on the cloud.

From your perspective what would be the 2-3 key focus areas?

I think we are seeing a lot of traction on the data side. So we started our data practice probably about a year and a half back. We are seeing a lot of traction there. And the traction is manifesting in some specific used cases. For example Customer 360 degrees is a big-big used case right now. Second thing that we are seeing is creating a data lake. It is more of a technology solution but then the biggest problem that solves is the customers do not have their data aggregated. Unless and until you have that in a single place you can’t really do analytics on top of it. So that’s a used case that is coming up really often. So at this point in time I think we are seeing a lot of traction on the data lake pieces, the data warehousing pieces, the visualization pieces, that’s one part and the second piece where we are seeing a lot of traction on is AI ML. So everybody wants to do something or the other either predictive in nature or forecasting. For example we are doing forecasting for one of the larger buddy format retail chains but we are building a sales forecasting for them.

Are there any plans to focus on other geographies?

Right. So we are seeing a lot of traction down south as well. Specifically on the data and AIML side of the house. And the reason why we opened an office in Bangalore was also because of the latent talent pool out there. So these two things go hand in hand very well. So we have like local clients and then we have local delivery capabilities.

How big is your organization?

We are about 100 people and then 80 odd is north, 20 odd is Bangalore. But right now most of the hiring is happening in Bangalore because that is where we are seeing the maximum amount of traction. So that will continue I believe. And the whole idea for us right now is to focus on piStat and take that product globally. So we have like 3 or 4 -- so one thing that I didn’t mention about piStat is that there is actually – there is a lot of NVP capabilities built into it. So there is a lot of AIML and intelligence. This works across multiple different languages. So it works for Hindi, it works for Gujarati, it works for Bengali, it works for Punjabi. We have clients down south who are using the same thing for Telugu and Tamil. The second thing that we want to get to quickly is automated translation across languages. Because we already have the engine. We understand. So the NLP and the NLU is already there which is the natural language understanding. We just need to do NLG which is the generation.