CapeStart: Making Machines Learn and Become Efficient

CapeStart

Gaugarin Oliver, CEO, CapeStartGaugarin Oliver, CEO
Machine learning (ML) is similar to human learning. The machines need to be exposed to topics that they need to be experts in. They also need a continuous feedback loop to become efficient. This is the reason why implementing ML and artificial intelligence (AI) solutions is not like installing other technology solutions. It requires a lot of preparation, especially with data. Cleaner data leads to results that are more accurate. However, preparing raw data to be effectively ‘usable’ for ML initiatives is one of the most overlooked prerequisites in the enterprise AI solutions arena today. The consequences of failing to implement a well-planned data enrichment process can range from a slower pace in achieving optimal ML output, increased instances of human-in-the-loop for feedback, data scientists spending time on unavoidable yet largely manual ‘filtering’ of data. Not to mention associated cost overheads. With their novel approach and solutions for Data Preparation and Enrichment, and implementing human assisted-feedback mechanisms, CapeStart helps enterprises address these predicaments and sets the groundwork straight for strategic AI undertakings. The company’s CEO Gaugarin Oliver shares insights on the unique value propositions that his company offers to its clients to gain a head start in their AI initiatives.

What are some common mistakes enterprises make in their move toward AI/ ML solutions?

While many data preparation processes tend to be largely manual and labor intensive, enterprises go about assigning the tasks to interns and crowdsourcing solutions. Such approaches cannot guarantee the utmost quality of training data. Furthermore, it becomes all the more complicated from an execution and management perspective. Any place where there is plenty of unorganized data, one has to render them in an organized way so that machines can understand. We staff our client’s teams with distinct domain experts and offer the right tools to enable them to streamline their data enrichment workflows.

How has CapeStart positioned itself to help the enterprises in their data enrichment initiatives? Please walk us through your solutions portfolio as well.

Clients who approach us lack the expertise in terms of the full lifecycle of implementing AI solutions; that’s where we come into play. We identify what kind of ML platform they want to use, and help them figure out what data sets have to be fed. We help them discover, identify, collect, clean, and label the raw data stacks to leverage their ML implementation to the fullest.

We staff our client’s teams with distinct domain experts and offer the right tools to enable them to streamline their data enrichment workflows


Furthermore, if there is a need for a human intervention to provide corrective feedback to the machine, we can handle that as well.

Machines always append a certain ‘confidence level’ when they give an answer. Here the enterprises may decide that for a confidence level more than 90 percent, they will go with the results from the machine, and for the confidence level less than 90 percent, they would seek human intervention. Based on our extensive industry experience, we help our clients understand what confidence level is suitable for what market. This is the type of expertise that interns cannot provide.

We offer our services based on the client needs. If they want us to train their ML platform or resolve a conflict concerning ML output, we are there for them. We get involved in everything from strategy, planning, and execution; we are all in one AI solution provider.

What are some of the aspects that set CapeStart apart from the rest of its competitors in the space?

There are AI/ML solution providers in the market who insist that customers have to use only their platform. However, that does not always meet the needs of enterprise customers who may not want their confidential and proprietary information outside their digital premise. On the contrary, our services and technology stack are platform agnostic. Our analysts and tools are purpose-built for data pre-processing and can work on any back-end platform that our clients use. In situations where they may not have such a platform, we can help build it for them. Partnering with us will not, in any way, hurdle our client’s data security, privacy or compliance stances. The data will always remain within their reach and firewall.

We take the extra effort to choose and train our employees so as to deliver clean training data that is at par with the high-quality standards, and always on time. Also, unlike crowdsourcing, we become the single point of contact standing behind the work that our employees and dedicated client teams perform.

Who are your typical customers? Please elaborate on a few use cases where you have helped clients achieve their goals.

Our clients range from banks looking to train their bots to better respond to customer inquiries; to healthcare institutes building image analysis tools. Our services have played a key role in the development of a fully fledged stock purchasing decision maker tool that can analyze news and happenings across the world, in a chronological fashion, to identify and predict market fluctuations. We have also helped train medical AI programs to scan radiology reports and images to provide deep insights for radiologists at a moment’s notice. We also play an instrumental role in the development of smart-workforce solutions which are a combination of robotic process automation (RPA), human agents, and agent-assisted robots. Our intense data enrichment process and continued support to make machines self-reliable in terms of providing accurate outputs has made all these achievements possible.

Talking about the company, how have you envisioned the future for CapeStart?

We will create more intelligent labeling solutions using machine learning itself; it does not have to be exclusively humans who do all the work. We are also building premade data sets so that companies can readily use them to build their ML models quickly. The third area where we are focusing is really the continuous human-in-the-loop services for end-to-end RPA solutions.

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