Know About GeoIQ, An ML-Based Location Intelligence Platform

Bengaluru-based GeoIQ is a locational intelligence platform that tells the value of each location - people, their behaviours, businesses and potentials - as easily consumable layers on maps. There is a huge requirement for access to reliable external data, data that sits beyond the company’s database and there are multiple problems that would benefit immensely from this additional information. 

GeoIQ has raised around $3 million in the pre-seed round. It additionally provides ML-ready attributes in addition to access to reliable external data. The brand name simply represents Location (Geo) Intelligence (IQ).

"We are building a product that we as data scientists wished we had. As experienced professionals in this domain, we took cognizance that data scientists across the industry have to rely primarily on internal data to develop solutions for real-world problems," Co-founder, Ankita Thakur states.

Artificial intelligence and machine learning capabilities are at the peak of their adoption. Also, there has been immense work done on the data front, especially in enabling data transformation and accessibility to the petabytes of unstructured real-world data. The data accessibility and ML innovations combine to form the new regime of location intelligence to provide hyperlocal insights to businesses and assess opportunity and risk at each geo-location.

While talking about the current trends in the AI and ML space, Co-founder Thakur claims, "Today, prediction models can achieve an accuracy of around 98 per cent which was not possible without the location data element. Overall, location data and analytics is finding applications across industries and use cases."

Talk about your USPs. How do you stand unique from your peer competitors?

Coverage, Accuracy, and a wide variety of attributes are available through a single API.

We cover the whole geography (all possible addresses) within the boundary of India and the USA. The accuracy of our prediction models is unmatchable. Also, we provide a wide variety of attributes that are available through a single API.

Our No code ML platform enables businesses to try out 3000+ attributes in their prediction models with minimal effort and for free. It enables them to do exploratory analysis, shortlist attributes, build models and deploy them as real-time APIs in one click.

What are the key challenges you encounter? How helpful are they for your business development?

When we started out, we realised there was a strong need for this data, but very limited understanding. So we had to educate our users, unsuccessfully at times, on how best to use this vast repository of data. Business analysts always looked for specific numbers that they thought were important, but it changed when we reshaped the product for data scientists.

Then there are challenges with the data.

When we started with the idea of GeoIQ, most of the challenges were around sourcing valuable, accurate, and quality real-world data. Good quality data for Indian boundaries were not very easily available and accessible.

Also, the pin code, city, state, and other boundaries were not very clear beyond administrative limits. We then built a GeoAllocation engine to define the boundaries better (20% more accurate than existing boundary definitions) and mapped the addresses on these new boundaries.

Another challenge was with the sanctity of data. We had to check and validate data points from various sources to ensure the accuracy and truthfulness of information that we had sourced from available government and public data sources.

The challenge that is prevalent to date is around data discovery. Identifying which data is useful and impacts a use case directly is a herculean task and often lands on trial and error. The NoCode ML platform is essentially created to solve this problem. The solution has completed the beta phase and is soon to be launched. Businesses would be able to create multiple models in no time and experiment with data attributes to identify the best fit for their use case.

Overcoming these challenges and imbibing all the developments in our solution has helped us create a service portfolio that is unique, holistic, and difficult to compete with. By overcoming the data challenges, we have achieved an accuracy of up to 98% in our ML models which makes us the best in the industry.

The leadership mantra I go by is empowering others as leaders. When you trust your colleagues with responsibilities, instead of managing or micro-managing, they grow into leaders. Also, when you create opportunities for individuals to grow, the organization grows as a whole

What are the growth plans to develop further? What are your next big plans?

As we target 10 times growth in the coming year, we are expanding to the US market. Our data is already live in the US with more than 5000 data features. Ahead of this, we are opening up our solutions for the US market in a phased manner, keeping in mind the ability of data teams to build models on the US datasets and varying nuances of use cases and market appetite as compared to our current Indian market.

For business users in Retail, our RetailIQ product is suggesting streets open stores across the country within a single click - a process that was fraught with errors and used to take months or even years.

What is the market opportunity you are eyeing currently?

There is a growing need for data and analytics across industries to uncover critical insights, patterns, and trends in user behaviour and market dynamics. This is a massive opportunity to be able to enhance the existing analytics and prediction models with real-world data to improve their accuracy and performance multifold.