Who We Are
Euler Systems is a sector agnostic, Enterprise AI consulting and custom implementation partner for its clients and is run and backed by veterans from Wall Street and Silicon Valley. Euler’s ability to comprehend the client’s financial goals, translate them to define a data problem and implement autonomous systems is unique and stems from an eclectic foundation of the team.
Euler helps its clients identify opportunities to monetize data streams, both internal and external, apply ML to optimize business processes like sales (eCommerce store-fronts, offline store sales, outbound sales), pricing and yield management, customer support, vendor/borrower/channel appraisals at the least.
At Deutsche Bank, he was responsible for CEEMEA Structured Commodity Assets Business. Himanshu is an AI and data science leader from Yahoo!, eGain & ClosedLoop; Co-founder of Bixee. He has a BTech in CS from IIT Delhi, MSCS from University of Washington, Seattle and MBA from IIM Bangalore. Himanshu was a Bronze Medalist at International Mathematical Olympiad.
Head of Engineering
Sandeep was VP Engineering for Saavn and responsible for the 30million MAU business. He has several large-scale implementations to his credit having been part of the teams building Yahoo! Buzz, Yahoo! Photos and Yahoo! Search. Sandeep has a BE in CS from Maharashtra University and an MSCS from University of S. California.
Boosting the quality of search engine results
Finding what to buy/consume is one of the primary means for consumers to interact with an Internet business, be it eCommerce (travel, consumer goods, etc), media and enterprise social media. To achieve Google-like experience within an Internet business needs an adaptable way to process queries intelligently and boost search relevance quality. Search Click-Thru-Rates (CTRs) are an easy way to measure the quality of search implementation. What is good CTR for a search engine? What is the framework to measure search implementation quality? How does search translate to real USD for our clients? Reach out to us!
Growing sales by using ML to up-sell & cross-sell
It is very expensive to bring traffic to your internet business. SEO, SEM and other advertising strategies are expensive and marginally less valuable over time due to the competition. Typical eCommerce businesses convert at a rate of 1-3% with such expensive traffic as opposed to 30-35% in offline stores. Several businesses build out statistical recommendation engines that lift this to about 3-5% by presenting upsell and cross-sell options to its consumers as visual cues. The relevance, sequence, count, speed of such recommendations have a tremendous impact on customer basket sizes. Euler uses a hybrid approach using ML to optimize these recommendations and these cues generate 17% of sales of an 800m USD eCommerce company. Reach out to know more about how it is applicable to your industry!
AI in CRM Systems
Autonomous categorization and resolution of customer support tickets
Euler has built ML models that predict issues in customer support tickets based on unstructured text data and metadata associated with tickets. The models are generated and trained with historical tickets (upto 10MM in some cases) and are deployed as a service to make issue predictions in real time with very low-latency response times (sub 50-milliseconds). The system can be integrated to auto-resolve tickets. Our experience suggests 25% reduction in ticket categorization time within the first 90 days.
Credit Appraisal and Early Default-warning Systems
Banking and Fintech clients
The lending process is limited by an expensive process to assess the applicant’s ability and willingness to repay. Euler’s supervised ML based appraisal systems not only provide go/no-go decision for a loan application (business/personal/asset backed) but also enable Early Warning Systems (EWS) to highlight and incorporate risk due to external/macro/locational/sectoral sources into the appraiser. Go/no-go accuracy is approx. 95% and our systems are being actively used to augment/speed-up human judgement (increased risk-adjusted appraisal throughput by 4-5x).
ML-based Forecasting and Pricing
Hospitality, On-demand transportation assets
Euler’s systems ingest historical occupancy/demand along with pricing information available from clients, augments the data with alternative data streams like deep-web-data (buried in OTAs), peer-to-peer asset pricing (Airbnb), events data, etc. and create machine-learning models to forecast demand. Further, based on market and competition data and price-elasticity, Euler’s systems suggest pricing assets. Our experience with hospitality lifted hotel average daily rates (ADRs) by 600 bps.