What do we offer?

We are a Machine Learning and data engineering service company. We partner with a limited number of startups to help identify potential ways businesses might utilize the value of ML/NLP technologies. Our goal is to help you augment and differentiate your product with advanced Machine Learning capabilities.

If you are an early stage startup, we know that you might have a hard time attracting the best ML/NLP engineering talent to join your firm, that’s why we are here to help. We will work closely with your business and engineering team to identify areas where data and ML/NLP can add significant value. If you are thinking of using an open source ML/NLP model available in the market (Amazon or Google) but are struggling to build the required data pipeline; from moving data from source to destination, to transforming the data into the models expected format, don’t worry we got your back. We have the required data engineering and ML experience to help design and build the data pipeline, train your models, and close the product loop.

If you are a later stage startup and are thinking about designing and implementing your own customized models but don’t know where to start, we can help you calculate the value and cost of building your own model. If the value is substantially greater than the cost, we can implement the first customized ML/NLP model and build the required data pipeline for you. As your partner we are able to help maintain the data pipeline and ML/NLP services for you, or train your engineering team to learn and internalize the technology.

See the following for a list of use cases that we have delivered for other companies.

Use Cases

Forecasting

Problem: There are a number of use cases where companies have to forecast business metrics over different time frames. One example is demand prediction. Software teams need a way to predict demand so that they will know how many servers they need to allocate or how many data centers need to be built. Demand prediction is essential for inventory management as a way to avoid the loss of fast spoiling products, and to avoid running out of inventory. Anomaly detection is another important problem when businesses need to see what’s happening in aggregate. Detecting anomalies is critical and allows you to quickly respond and correct the root cause of the issue, which would otherwise go undetected.

Demand Forecasting

Fraud Detection

Problem: Online fraud is growing massively and digital companies are dealing with larger volumes of fraudulent activity than ever before, there are a number of reports showing that fraud costs consumers more than $16 billion a year. Fraud can appear in many different forms, one form is when a fraudster tries to scam or hijack a legit customer’s account through social engineering methods, another tactic is through automated attacks, where hackers develop bots that perform large scale attacks against online platforms.

Fraud Detection

Advertising and Marketing

Problem: If you’re a company that relies on online advertising, serving the right ad to the right user is critical. As a publisher, you constantly need to evaluate and target the correct users. This is a complex optimization problem that requires you to compute the lifetime value of an online user in real time and target them with the right offer so that they click your ad.

Advertising and Marketing

User Engagement

Problem: Entertainment and E-commerce companies such as Netflix and Amazon that own a large catalog of digital content and physical products, and one of the main challenges these companies face is maximizing user engagement and increasing their sales.

Recommendation Systems

Document Intelligence

Problem: There are a number of companies in the medical and legal industries which have large volumes of unstructured text data, in which reading, extracting key insights, and indexing data quickly becomes a highly laborious task. Businesses and researchers could greatly benefit from a virtual assistant to automate and facilitate research across your unstructured data.

Natural Language Processing

Customer Lifetime Value, Retention, and Churn

Problem: Companies’ marketing and sales teams are constantly trying to optimize their customers funnel, from signup to becoming a paying customer, and including retaining them. If you do not have an accurate model to predict your customer lifetime value, your marketing team will not be able to figure out how much money they should spend to attract and keep profitable customers.

Churn Prediction

Public Opinion Prediction

Problem: There are a number of use cases that require a company to measure public opinion about a matter (e.g. political, product, etc). For example, polling organizations need to follow the US presidential elections and predict its results ahead of time. Hedge funds need to monitor public opinion about public or private companies to help predict stock prices.

Sentiment Analysis