Case Study: Skopos Labs achieves scalable, real-time legal and financial data analysis with Elastic (Elasticsearch Service)

Skopos Labs Our experience with Elasticsearch and Elastic Cloud

Skopos Labs is a legal and financial data provider that uses a validated machine-learning platform to analyze massive, semi-structured datasets and produce real-time forecasts of government policy impacts (for example, predicting the likelihood that bills become law). As a two-developer startup, they needed a fast, low-friction way to turn growing piles of text data into a scalable, Python-friendly full-text search engine to feed their analysis pipeline without heavy DevOps overhead.

They consolidated on Elasticsearch Service on Elastic Cloud, which let them get up and running quickly, run locally for heavy batch jobs, and scale as their datasets grew into the hundreds of gigabytes. Elastic’s managed features (plugins, machine learning, snapshots and support) not only saved developer time—once enabling a quick recovery after an accidental bulk_insert—but also let Skopos expand Elasticsearch into logging and anomaly detection, making it an end-to-end component of their pipeline and supporting continued product growth.

Skopos Labs

James Daily

Head of Legal Data Science