Work I led before founding Concentric Lab.

AI Studio Buildout.

Standing up an internal AI studio and shipping production systems.

Situation

The company had watched competitors announce AI initiatives for eighteen months. Internal pressure to "do something with AI" had produced three vendor evaluations, two failed pilots with different consultancies, and a 74-slide strategy deck that recommended "establishing an AI Center of Excellence." None of it had shipped anything.

The failure pattern was consistent. Each pilot started with model selection, invested heavily in data preparation, and stalled at integration. The prototypes worked in demos. They broke when connected to production data, internal authentication, and the compliance requirements that govern financial services operations. The gap between "it works on my laptop" and "it runs in production" consumed the entire budget.

Leadership wanted tangible results within one quarter. Not another strategy document. Not a proof of concept. Working systems that processed real data and produced measurable operational improvements.

Approach

I spent the first two weeks not building. I mapped manual processes across three departments, ranked them by volume, repetitiveness, and error cost, and identified the workflows where automation would produce measurable outcomes. The selection criteria were strict: the workflow had to be repeatable, the inputs had to be structured or structurable, and the error cost had to be high enough to justify the build.

I stood up the core studio infrastructure first: authentication, a data pipeline, monitoring, and a deployment pattern that every subsequent product inherited. A document intake classifier was the first system built on that foundation, sorting incoming submissions into categories and extracting structured fields.

From there I shipped an anomaly detection system for transaction monitoring that flagged patterns deviating from historical baselines and routed alerts to the compliance team with context, an automated quarterly reporting workflow that pulled data from internal systems and generated draft reports for analysts to review and approve, and an internal knowledge system that indexed regulatory guidance, internal policies, and historical compliance decisions, answering natural language queries with citations to the source material.

Each product followed the same pattern: process mapping and scope definition, then architecture and data pipeline work, then build and internal testing, then production deployment with monitoring. The shared infrastructure meant each subsequent product moved faster than the one before it.

Outcome

The studio shipped production systems that handled live operational data: a document intake classifier, a transaction monitoring system, an automated quarterly reporting workflow, and an internal knowledge system. Each built on the shared infrastructure established by the one before it.

The difference from the company's prior attempts was sequencing. I built the architecture first and selected the models second, not the other way around.

4

Systems shipped

Shared

Infrastructure foundation

Production

Live operational data

1

Internal studio stood up

What compounded

The engagement validated the AI Implementation Framework: Assessment, Architecture, Build, Integrate, Operate. The client's prior pilots had started at Build. I started at Assessment and spent the first two weeks defining what was worth building. The architecture phase established shared infrastructure that made each subsequent product cheaper and faster to ship.

The deployment pattern, the monitoring stack, and the integration templates became reusable assets in the Studio pipeline. Every subsequent AI engagement starts with the same infrastructure foundation, which means less time on plumbing and more time on the domain specific logic that actually differentiates each product.

Related reading

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