AI Workflow Automation
Reduce the time spent moving data, sorting requests, building routine reports, and handing work between people.
Remove repeat work from one process before buying a large software system.
Use AI to cut repeat work and protect your margin.
Great Lakes Computing Office builds focused workflow automations, content and image systems, and measured software improvement for owner-operators. Each engagement starts with one business task, a bounded budget, and tests that show what changed before a small team decides whether to ship or expand it.
Which repeat tasks cost your team the most?
Copying order data, sorting intake, rebuilding reports, resizing assets, and checking routine code changes can take skilled people away from work that needs judgment. The cost shows up three ways.
- Manual transfer: the same facts move between email, sheets, forms, and business software, and every move is a chance to introduce an error.
- Slow production: each article or image starts from a blank page and a new set of checks, so volume stays low and review stays expensive.
- Unmeasured software work: code changes ship without a clear score or a full user task test, so regressions surface after they reach customers.
One workflow. Ten business days. $5,000.
We map one repeat process, estimate its current cost, build a tested prototype, and report what changed.
Three ways to remove costly repeat work.
Each service starts with a bounded business task. The tools follow the job, not the other way around.
AI Content and Image Systems
Create repeatable article, campaign, and image pipelines with source checks, review gates, and web-ready output.
Turn content production into a visible process that a small team can run and review.
Agentic Software Improvement
Give a coding agent a clear start state, desired result, safe edit area, tests, and a score that protects the real business task.
Let an agent search for a better code change while tests and a skilled operator control what ships.
Systems Lab shows the method and the limits.
These are implementation records, not invented customer stories. Each entry names its evidence and what was tested.
Codebase Improvement Loop
The measured GLCO loop used to replace a WebGL hero while protecting navigation, layout, accessibility, build health, and contact behavior.
Image Production Pipeline
A tested GLCO path from a reviewed generated source to versioned AVIF and WebP files with dimensions and useful text alternatives.
Technical Publishing Workflow
The tested GLCO article system for structured drafts, primary sources, writing checks, responsive images, and Git review.
Technical articles for practical AI work.
Source-backed notes on coding agents, image systems, ComfyUI, and measured code experiments.
Autoresearch for Codebases: Turning a Goal into Measured Experiments
How to adapt a fixed-budget research loop to codebase work with one score, regression tests, browser checks, and human approval.
A Codex Image Generation Pipeline You Can Review and Repeat
A tested workflow for briefing, generating, reviewing, processing, and publishing Codex image assets without fake technical detail.
Improving Image Quality with ComfyUI and ESRGAN
A sourced guide to the ComfyUI ESRGAN upscale flow, model choice, hardware limits, and the checks that prevent false detail.
How does a GLCO engagement run?
Every project moves through four connected steps. GLCO records the starting facts before changing the process, tests the smallest useful system against normal and failed work, then hands over the result and its limits in writing.
- Measure: record the current steps, time, cost, errors, and exceptions before changing anything.
- Build: change one bounded part of the process with the smallest useful system.
- Test: check normal work, failure cases, permissions, and the full user journey.
- Handoff: explain the result, limits, maintenance needs, and the next decision in writing.
Bring one repeat process.
Bring one repeat process. We will map it, build a tested prototype, and show what changed.