Exploring and developing a new SaaS platform fueled by data engineering
Explore complex product idea while hiring a team
Design best practices, data engineering, Ruby on Rails, Scala
Product-market fit, $9M ARR, upskilled engineering team
Video of Aatish talking about why he reached out to thoughtbot
Explore an entirely new product direction that uses Machine Learning
Aatish Salvi joined Teikamatrics as CTO to take on an ambitious plan he had formulated with CEO Alasdair McLean-Foreman: explore an entirely new product direction that uses Machine Learning to help small to medium-sized online sellers compete in the market. In addition to the technical challenges, Aatish needed to develop his engineering team and hire principal engineers at the same time.
In the video above, Aatish talks about why he reached out to thoughtbot to help him explore the concept with design thinking, architect their data pipeline, and ship an MVP. In the end, thoughtbot not only gave him the bandwidth to focus on hiring, but developed his existing team's capabilities and culture for future success.
Quote from the Teikametrics project
The tech behind the solutions
When Teikametrics reached out for help with their platform, thoughtbot initially built out a rapid MVP using Ruby on Rails.
As customers started getting accepted into the system, it became clear that more firepower was needed on the data side.
In order to keep up, thoughtbot built a separate service to process data from Amazon. For this service, Scala, Akka, RabbitMQ, and Postgres were used to build a lightweight but scalable data platform for Teikametrics. By utilizing distributed data streams, the platform can break down a company's entire advertising history in minutes, providing continuous recommendations to sellers. Because the stream processes data in constant memory and applies backpressure, massive influxes of data won't overwhelm the system, and additional data can be processed faster by adding new workers to the cluster.