Investment Research Platform

Investment Firm
The problem

An investment team needed a more consistent way to gather and reuse research spread across several systems.

The work

I learned how the analysts worked, then built screening, reporting, and data-access tools around that workflow. I also connected the tools to interfaces the team already used.

What changed

Analysts can reach screening, reporting, and connected research through their existing workflow. The work continues as new use cases are added.

Lessons from this work: AI in Research Teams
LLM Applications Data Infrastructure Analyst Tools

Production ML Pipeline Assessment

Fintech Lending Company
The problem

A lending company wanted an outside review of a production ML workflow that made model changes slow and required repeated engineering work.

The work

I interviewed the data science and engineering teams, traced a representative change through the system, and documented the current workflow and its bottlenecks.

What changed

The team received a sequenced implementation plan explaining what to change, why, and what to address first.

ML Infrastructure Technical Assessment Production ML

Document Generation System

Healthcare Technology Company
The problem

A healthcare technology company needed a reliable way to turn complex source documents into structured, reviewable output.

The work

I combined AI-assisted extraction with conventional software for repeatable document assembly, then built tests against expert examples.

What changed

A scoped assessment became an implementation project. The client gained a reproducible document workflow and a way to measure output quality.

Document Generation Evaluation

AI-Assisted Development Rollout

Technology Company
The problem

An engineering team wanted to make AI-assisted development more consistent across the group.

The work

I coached engineers in their own codebases and built shared documentation and tooling that gave AI systems better project context.

What changed

The team adopted the practices, began sharing them internally, and continued extending the tooling.

Project Documentation AI Infrastructure Engineering Coaching

Predictive Scheduling Model

Logistics Technology Company
The problem

A logistics technology company wanted scheduling predictions running live inside their product.

The work

Trained a combined set of models on historical data, tested it against past outcomes, and deployed it as a live service the product could call.

What changed

The predictions moved from offline analysis into the live product, where the application could request them in real time.

Predictive Modeling Production ML Logistics

Applied AI Workshop

Healthcare Organization
The problem

A healthcare organization wanted practical AI training built around the team's actual work.

The work

I developed and ran a hands-on session with supporting exercises and follow-up materials.

What changed

The engagement produced a tailored working session, exercises, and follow-up materials.

Team Training Healthcare AI Curriculum

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