An independent consulting practice focused on the operational layer of enterprise data and AI infrastructure.
This practice focuses on the space between prototype and enterprise: designing, auditing, and remediating the infrastructure that data and AI systems depend on to run predictably. Most engagements start with a symptom — rising cloud costs, unreliable pipelines, failing compliance audits — and involve tracing those symptoms back to their architectural and operational root causes.
The work is architectural and diagnostic. It is not implementation body-shopping.
When Databricks environments grow from a handful of pipelines to hundreds of scheduled jobs and dozens of concurrent users, the default configurations that worked at the start become the primary source of operational risk. We audit cluster strategies, workload isolation, Delta Lake health, and cost attribution frameworks.
AI platforms introduce failure modes that traditional data engineering does not encounter: embedding drift, vector database consistency, inference latency variability, and the orchestration complexity of mixing ML training cycles with real-time inference pipelines. We design and remediate the infrastructure layer beneath the model.
Organizations that scale their Databricks environments without centralized governance eventually face a reckoning — a compliance audit they cannot answer, a data breach traced to over-permissioned compute, or a lineage gap that prevents rollback. We lead Unity Catalog migrations and design IaC-driven access control frameworks that make governance sustainable.
Data platforms designed for 1 TB/day do not automatically scale to 100 TB/day. The failures that emerge at scale — shuffle memory exhaustion, metadata bottlenecks, orchestration dependency cascades — require a different diagnostic lens than the failures at prototype scale.
Most engagements begin with an infrastructure assessment: a structured review of cluster configurations, pipeline execution profiles, governance posture, and cost attribution. From the assessment, we produce a prioritized remediation roadmap with realistic effort estimates and projected operational impact.
Engagements are scoped, time-bound, and outcome-oriented. We diagnose, recommend, validate, and document.
Infrastructure audit: clusters, pipelines, governance, cost attribution. Produces a written remediation roadmap.
Ranked recommendations by operational impact vs. implementation effort. No one-size-fits-all prescriptions.
Implementation support for priority changes, with validation of results against pre-engagement baselines.
Written operational guides, runbooks, and architectural decision records delivered to the engineering team.
We do not build web applications, design machine learning models, provide general software development services, or take on engagements where the primary deliverable is lines of code rather than architectural clarity.
If the problem is "our data platform costs too much, fails too often, or cannot be governed reliably" — that is the work.
Selective intake. Inquiries that include technical context about your infrastructure stack and operational problem receive priority response.