Proof of work
Enterprise and federal engagements are shown with real metrics and anonymized clients, per engagement agreements. EnChaptered is our own product — built, shipped, and public.
"This overhaul turned our flaky regression suite into a robust quality gate integrated into modern DevOps pipelines." — Automation Architect, federal agency engagement
"We built the first bridge between traditional QA and Big Data validation — enabling scalable, automated quality assurance for massive datasets." — Lead QA Consultant, federal healthcare data program
The framework works, but it only runs inside a Databricks notebook. We audited it module by module and found the coupling concentrated in one small file — a hardcoded naming convention roughly every six lines. The fix is to compile each check down to plain SQL instead of Spark DataFrame calls. The same suite then runs from a laptop, from CI, or on a cluster — and finally gets a unit-test suite that doesn't need one.
The assessment also surfaced something nobody had raised. The framework captures real failing values into its results tables. The moment an AI agent can read those tables, source data becomes text a model treats as instruction — an indirect prompt-injection path running straight through a data-quality report. A per-column redaction policy is now a prerequisite, not a follow-up.
Scoped from there: a portable check library, a ten-tool MCP server so a failed run can be triaged conversationally instead of by hand, and the existing root-cause runbooks packaged as agent skills.
"What used to take two weeks to patch after a platform release now takes an afternoon. The CI gate catches what our code reviews were missing." — Lead SDET, enterprise Salesforce engagement