[CapitalOne SES hackathon] Amazon Alexa Skill that can make complex queries on purchase history and predictions on your future spending
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README.md
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README.md

Financial Insights with ElasticSearch

Using Amazon Alexa advanced queries about your transactional history can be served via ElasticSearch.

The Call Stack

Using api.ai a variety of UI can be used to call our backend. We choose Amazon Alexa. Our Alexa skill would interact with a simple Python backend served on an AWS Lambda. This fronted our core API as a SSL authenticated service (which is required for Amazon Alexa skills) and converted utterances to Intents and Entities. This Python script delegated these calls to our Spring boot application.

API stack

Our core application was a Spring Boot backend running on a 64GB AWS EC2 instance. A single cluster ElasticSearch was also run on the same EC2 instance. From Intents the backend would extract and transform the correct information to query ElasticSearch for matching transactions (hits). The Google maps Geocoding API was used to convert locations to latitude/longitude coordinates for ElasticSearch. ElasticSearch was populated with transaction data using the Capital One Nessie API.

The questions you could ask

  • What was my spending between july 1 2017 and july 8 2017 in Arlington on food?
  • What was the distribution of my spending from August 4 2017 to August 18, 2017?
  • How much will i spend on uber during next week?

Setting up the backend

Unfortunately the frontend python script to handle Alexa utterances were lost, so only the backend can be built.

nohup ./mvnw spring-boot:run > ~/spring.out &
ps -Af | grep spring