Bay City
Artistry Lounge Hair Studio
A place to explore services, meet the team, and start a salon or bridal inquiry.
- Salon & bridal
A look at the work.
Business websites, useful tools, and technical projects. Explore what I’ve built and the thinking behind it.
Websites
Essexville
King’s Liquor
Store information, directions, product categories, and a way to ask about special requests.
- Local retail
Mid-Michigan
Merchant American Concrete
Project examples, service-area information, and an estimate form that collects the details of the job.
- Contractor
AI tools & technical projects.
Public tools, published methods, and academic work. Each project keeps its status, contribution, and supporting evidence.
AI Visibility Technical-Readiness Scan
A no-signup check of one public page against a technical-readiness rubric authored by Cleland & Co. The result exposes the weights, observed evidence, unmeasured evidence, and ranked gaps rather than presenting the score as an answer-inclusion prediction.
- One published, weighted checklist drives both the score and its explanation
- Designed, implemented, and maintained by Jeremy Cleland for Cleland & Co.
Evidence and limitations for AI Visibility Technical-Readiness Scan
Also demonstrates
- Safe fetching with limits on time, size, redirects, and request rate, stated in the result
- Findings limited to selected structured-data fields, HTML metadata, a limited crawler-policy check, and root discoverability files — not a full site audit.
- Unmeasured signals and reported convention files stay out of both sides of the score rather than counted against the page
Limitations
- It does not observe the configured provider surfaces or any named consumer answer interface.
- It does not inspect a Google Business Profile, reviews, or third-party directory consistency.
- It does not run a Lighthouse lab test or a site-wide crawl. Origin-level Chrome field data may appear as an unscored observation when configured.
- A readiness score is not a promise of placement, traffic, leads, or revenue.
Related consulting
AI Visibility Measurement
The free scan checks selected site foundations. Once live, the planned service adds repeated checks of AI assistants and scoped fixes.
AI Contingency Framework
A five-phase method for defining the mission, mapping the operating environment, comparing courses of action, rehearsing failure, and sustaining an AI system after launch. The full method and six rehearsal formats are public.
- Falsifiable definitions of done and explicit stop conditions
- Authored and published by Jeremy Cleland for Cleland & Co.
Evidence and limitations for AI Contingency Framework
Also demonstrates
- Comparable build, buy, and hybrid courses of action with reasoning attached
- Controlled exercises for prompt injection, data poisoning, dependency failure, bias, misuse, and recovery
- Named artifacts for monitoring, decision records, incident response, and after-action review
Limitations
- It is a published method, not evidence of a client outcome or independent validation.
- It is not a penetration test, compliance certification, or guarantee that every failure mode will be found.
- The exercises have to be adapted to the actual system, data, authority, and threat model.
Related consulting
AI Strategic Advisor
For a team that will run the work itself and needs the mission, criteria, and rehearsal design pressure-tested before they do.
Fractional CAIO
For a part-time executive mandate that has to own the framework and the cadence it runs on—not a method handed off without an owner.
Image-to-LaTeX Sequence Model
An academic sequence-to-sequence study for translating images of mathematical expressions into LaTeX. The public repository and report show the architecture alternatives, experiment configuration, training history, evaluation code, and tests.
- CNN and ResNet encoder alternatives paired with an LSTM decoder
- Independent academic project by Jeremy Cleland.
Evidence and limitations for Image-to-LaTeX Sequence Model
Also demonstrates
- Configuration, training, prediction, metric, and analysis code kept as inspectable artifacts
- Tests for the data loader and encoder plus a static technical report
Limitations
- This is an academic experiment, not a hosted converter or production deployment.
- The repository does not publish a trained checkpoint, and its experiment registry includes unfinished runs.
- Published figures are validation results from the study, not claims about client or production performance.
Related consulting
AI Strategic Advisor
Shows how architecture choices, experiment setup, and evaluation claims can be inspected before anyone treats a model result as production evidence.
Fractional CAIO
Shows the kind of architecture, experiment, and evaluation record a part-time executive would inspect before treating a model result as an operating input.
Parking Optimization Simulation
A university team simulation combining dynamic pricing, route optimization, demand forecasting, behavioral modeling, and city-level coordination. The source, tests, generated run artifacts, and static report are public.
- A decomposed simulation spanning pricing, routing, demand prediction, coordination, and driver behavior
- CIS 505 team project by Jeremy Cleland, Saif Khan, and Asem Zahran. Presented here with full team credit.
Evidence and limitations for Parking Optimization Simulation
Also demonstrates
- Unit tests and checked-in run artifacts that make the prototype inspectable
- Technical communication through an executable repository and static project report
Limitations
- This is a simulation and course project, not a municipal deployment or production traffic system.
- It does not establish real-world throughput, latency, utilization, or revenue outcomes.
- The work belongs to the credited team; it is not presented as a solo Cleland & Co. engagement.
Related consulting
AI Strategic Advisor
For reviewing how a prototype was decomposed, what it assumes, and how it was evaluated—before treating the simulation as a plan.
Fractional CAIO
A prototype that can be reviewed for assumptions, evaluation, and what would have to be true before it belonged in an operating plan — not evidence that this simulation was deployed.
PlantDoc Disease Classifier
A CBAM-augmented ResNet18 study for classifying plant-leaf images into 39 disease and healthy classes. The public repository and report include the architecture, training history, confusion matrix, and labeled classification examples.
- A documented encoder-plus-attention architecture with two trained variants and published training reports
- Independent academic project by Jeremy Cleland.
Evidence and limitations for PlantDoc Disease Classifier
Also demonstrates
- Held-out evaluation figures: confusion matrix, ROC curves, calibration, and labeled correct/incorrect examples
- An inspectable dataset analysis dashboard covering class balance, image size, and augmentation examples
Limitations
- This is an academic experiment, not a mobile field tool, agronomy product, or production deployment.
- Published accuracy and F1 figures are study metrics on the project’s dataset split; they are not claims about farm outcomes or unseen growing conditions.
- The work is not presented as a Cleland & Co. client engagement or as medical/agricultural advice.
Related consulting
AI Strategic Advisor
Shows how classification claims can be inspected against labeled errors, calibration, and the dataset they came from—before anyone treats a model card as an operating input.
Fractional CAIO
The kind of architecture, split, and error record a part-time executive would require before a vision model entered a production or safety-relevant workflow.
What are you working on?
Tell me what you need, what you have now, and where you want to go. I’ll suggest a useful next step.