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.
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
Solution: We have a proprietary in-house forecasting engine that we can customize to your business needs. Our ML forecasting engine is extensible and can model seasonality in your time-series data and non-linear trends. We will work with your team to implement the data pipeline, and to move your data from its data source (i.e. time-series database, or SQL databases) and to feed it into our time-series modeling engine. Your business team can use our forecasting tool to slice and dice their data and forecast the future.
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.
Solution: We have designed and implemented a number of fraud and scam detection systems in-house. Specifically, we discovered a number of signals that we can extract from a user's device at the time of signup that our machine learning models can detect and label early on.
We also designed and implemented a machine learning system that computes a fraud score for each user based on their activity on your website or app. Scoring allows you to apply rules to users and automatically disable users with high fraud scores. We can also help you implement a human-in-the system where accounts with suspicious activities get assigned to your fraud detection team for human review. Our models have been used to fighting fraud in online dating apps, financial products, credit scoring, and online identity detection services.
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.
Solution: We have implemented a number of machine learning models for several ad networks by which they could learn the optimal ads for each user and maximize revenue. We have also launched a real-time bidder product for two game studios, built using a customized version of Gradient Boosted Decision Trees to compute optimal bids for buying mobile traffic.
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
Solution: One solution to increase user engagement is to recommend the right item (e.g. content, product) to the right customer at the right time. We have designed a number of recommendation systems to help companies personalize their offerings to their users. We can customize a recommendation system for your unique business requirements using state-of-the-art techniques.
Our recommendation systems not only will personalize the recommended items for your users based on their past consumption behavior but also will take into account the freshness of the contents, making sure your users are getting the latest items in your catalog.
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
Solution: We have implemented a number of natural language processing algorithms that are used to process large amounts of text data and extract human-readable insights. One system takes a large corpus of medical documents and learns how to extract and label medical tags such as “DISEASE” and “TREATMENT” and provide summaries of documents, which can then be searched in a Google type search engine to find the relevant document.
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
Solution: We have helped a B2B company to figure out where they need to spend their marketing budget in order to attract high value customers. We have achieved this by pulling marketing leads and customer data from the company’s Salesforce account. We fed the Salesforce data to a machine learning model to predict the value of each market segment (i.e. location, industry, company size, etc). Next, we used the outcome of our machine learning models to allocate the campaign budget for the high valued market segments.
We also helped the product team of a well-known dating company to model and predict newly signed up users' lifetime value. We achieved this by extracting a large number of attributes of their users (e.g. gender, age, city, ethnicity, device, interests etc) and fed the attributes into an advanced machine learning model which predicted the likelihood of a newly signed up user turning into a paying user. This modeling helped the product and marketing teams to segment their users and show them different offers to maximize company sales.
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
Solution: We have done extensive research in predicting the US Presidential election using Twitter data. We built the data pipeline by which we call Twitter API endpoints to retrieve relevant tweets and feed them to our Natural Language Processing engine for sentiment analysis and topic modeling. We have published some of our work, which has been cited by many researchers and has also been covered by Forbes.