A sample of the architecture, governance, and AI-infrastructure work behind the practice. Details are generalized to protect client confidentiality; the problems and outcomes are real.
A marketing automation platform had accumulated roughly four million email records, over 60GB of near-duplicate HTML, degrading storage costs and query performance across the CRM.
Designed and built a sanitization pipeline that normalized and deduplicated the HTML before it reached the CRM's record objects, cutting stored volume at the source rather than after the fact.
Storage growth flattened, query performance recovered, and the pipeline became the standing pattern for how marketing content enters the CRM going forward.
An automation-heavy CRM hit a platform event limit under real production load, causing an outage that exposed how little visibility the team had into event volume across the org.
Led incident response, then produced a full postmortem documenting root cause, contributing factors, and the specific automation patterns that had been silently accumulating risk.
The postmortem became the basis for new platform-event governance standards, and the response team's work was formally recognized as a model for how the org handles incidents going forward.
A distributed engineering team had several individually-built AI agents scattered across different tools, each with its own partial context and no shared knowledge base.
Merged the existing agents into a single team-wide agent built on a structured, 20-file knowledge base covering the org's systems, conventions, and decision history.
One system the whole team could rely on and extend, replacing individual, undocumented setups with something new members could actually onboard onto.
Whether it's a system under real load, an incident worth turning into a standard, or AI tooling that's grown faster than its documentation, that's the conversation to start.