We recently launched Digits Search, which brings fast, beautiful, full-depth search to business financial data. We’ve received a ton of great customer feedback, but one question just keeps coming up…

How did you build this!?

Well, let’s break down how Digits Search puts your finances at your fingertips.

Digits Architecture

First, some quick background on Digits:

Digits links with your business’ accounting software and your financial institutions to build, and continually maintain, a living model of your business with the most up-to-date data. Once linked, we ingest all of that financial data in raw form, a collection that we call “facts”.

We then use machine learning and data processing to normalize all of that overlapping, unstructured data. We perform a significant number of calculations to fill holes in the picture, such as predicting how your latest transactions will be categorized, detecting and predicting recurring activity, etc.

The end result of all this work is a “view”, which is then efficiently loaded into Google Cloud Spanner as well as encrypted and archived in Google Cloud Storage for secondary processing.

Each view we produce is a complete, standalone picture of your company’s entire financial history.

Views are served by our serving layer, which is composed of a number of services communicating over TLS-encrypted GRPC APIs, written in Go and hosted in GKE. Our serving layer aims to optimize for efficiency, security, and reliability.

At Digits, we strive to push the bounds of technology in order to deliver radically more useful, delightful software experiences for our customers. We’re excited to begin sharing a closer look at the technical foundations that underpin our products in a new series of blog posts called Building Digits. Without further ado…

Let’s talk about viewing complex data. One of our primary goals at Digits is to provide business owners with insightful and holistic views of their company’s finances, in substantially real-time.

Achieving this involves three major, independent steps:

1. We collect all of their relevant data from various sources, such as their QuickBooks, the financial institutions they bank with, their corporate credit card providers, etc.
2. We apply our algorithms and proprietary datasets to extend, interpret, and tease out meaning from all of their data.
3. We consolidate and aggregate the results into a holistic view that we then visualize for them on their dashboard.

Facts

We refer to the pieces of data that we receive from third party systems as Facts. This is not a judgement of the credibility or immutability of these systems, but rather a delineation of what is (and what is not) under our control.

For example, if we receive a transaction from an external source that looks like 05/12/20 - Taxi $15.05, we might classify it as Transportation. Later, we may receive another piece of information that leads us to believe that this transaction was actually a client expense, and is better classified as Meals & Entertainment. In this example, the transaction itself, the fact, did not change—but our interpretation of it did.

Computed Data

We refer to insights and analysis that are performed by Digits, based on all of the Facts that we have received, as Computed Data. In the example above, this involved a category classification of a transaction. In other cases, this might involve determining that two external pieces of information actually represent the same physical transaction, or detecting that a particular transaction tends to recur on a regular interval and that it should be treated as a subscription.