Systems | Development | Analytics | API | Testing

You build a data pipeline from natural language by describing the source, destination, and required transformations in plain English to a platform with a prompt-to-pipeline feature, which then generates a draft pipeline with inferred field mappings, transformations, and a schedule for you to review and adjust. This guide is for operations teams, data analysts, and junior team members who understand the desired outcome of a migration but don't write SQL or Python.

You ingest and reconstruct multiple unrelated CSV exports or a PostgreSQL dump from an acquired legacy system by first mapping the dump's underlying schema and relationships, then building a staged pipeline that loads raw files or tables as-is, reconstructs relationships through keys, and only then applies business logic to produce clean, usable records.

You opened the AI assistant for the first time with a fair amount of hope. You typed "write test cases for the login page." You got eight test cases back in about three seconds. Valid login. Invalid password. Empty username. Empty password. The kind of list you could have written in your sleep, missing every scenario that actually matters for your product. So you closed the tool and thought: this is fine for the basics, but it does not really get testing. AI is overhated.

Until recently, we extensively used HashiCorp's CDK for Terraform. Then they announced the end of its development. We didn't want to spend months migrating to a new ecosystem, but we needed a replacement. In the Infrastructure Team at Ably, we like Go. I wondered if there was an opportunity for something simpler.

Most AI content assumes your data is in the cloud. But for a meaningful segment of enterprises, cloud-only AI tools block them at the pass. For regulated industries like manufacturing and healthcare, data residency requirements, compliance mandates, security policies, and simple operational reality mean sensitive data must remain on-premises.