AI workflows
& tools.
AI-assisted code review, document assistants and production workflows built around the way your team works. Start with a specific task and a result you can check.
Let’s talk
Where I can help.
AI-assisted code review
Combine repeatable policy checks with focused AI review of a change. Give engineers findings they can inspect, with enough context to decide what needs attention.
Document assistants
Help the team find answers in its project documents, with citations back to the source. Make it possible to check an answer and see where the available information falls short.
Production workflows
Extract tasks and surface risks from production documents, then connect that output to a workflow the team can review. Application logs and usage tracking keep the process traceable.
Tools behind the work.
I built MR Guardian and Studio Agent Nexus and use both in production and in my regular workflow. Both are open source.
MR Guardian
Deterministic Unity policy checks, scoped LLM review, structured reports and stored review history. Model feedback stays advisory; engineers make the final call.
Explore the review tool →Studio Agent Nexus
Document-grounded answers with source citations, task extraction and risk detection, supported by application logs and usage tracking.
Explore the document workflow →Start with one workflow.
Tell me which task takes time, what the input looks like and where the result needs to go. An example document or review, and the tools your team already uses, help make the discussion concrete.
We can define a focused first version: its inputs, expected output, review step and how to check whether it helps. Scope, dependencies and timing come before choosing the implementation.
Scope and pricing
We can discuss the work, dependencies and timing first, then agree on a scope and engagement that fits the project.
For game development, explore Unity Multiplayer or Unity ECS / DOTS.