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Selected work

4 inspectable artifacts

Evidence, with the limits left in.

Selected systems, methods, and technical studies. Every entry says what exists, who contributed, what you can inspect, and what the artifact does not prove.

Evidence standard

This is not a client-results page. No entry implies a deployment, endorsement, revenue result, or production benchmark that the linked record cannot support.

Live tools are labeled live. Published methods are labeled methods. Academic and team work keep their original context and attribution. Relevance to an engagement describes the capability the artifact helps evaluate—not a customer outcome.

  1. Public-page technical-readiness tool

    Live public tool

    ClelandCo Technical-Readiness Scan

    A no-signup check of one public page against a ClelandCo-authored technical-readiness rubric. The result exposes the weights, observed evidence, unmeasured evidence, and ranked gaps rather than presenting the score as an answer-inclusion prediction.

    Contribution and provenance

    Designed, implemented, and maintained by Jeremy Cleland for ClelandCo.

    What the record demonstrates

    • A published, author-defined weighted checklist shared by the scanner explanation and scoring code
    • Guarded fetching with DNS, redirect, response-size, timeout, rate-limit, and simplified robots-policy controls
    • Literal findings for selected structured-data fields, HTML metadata, limited crawler policy, and root discoverability files
    • Explicit neutral treatment for signals a single page fetch cannot reach

    What it does not prove

    • 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.
    • A readiness score is not a promise of placement, traffic, leads, or revenue.

    Inspect the evidence

    Open the scanner

    Consulting relevance

    AI Visibility Measurement

    The free scan checks selected site foundations. After live validation, the planned engagement is designed to add repeated provider sampling and scoped remediation.

  2. Operating and rehearsal method

    Published framework

    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.

    Contribution and provenance

    Authored and published by Jeremy Cleland for ClelandCo.

    What the record demonstrates

    • Falsifiable definitions of done and explicit stop conditions
    • 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

    What it does not prove

    • 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.

    Inspect the evidence

    Read the full framework

    Consulting relevance

    AI Strategic Advisor

    Useful when the client team owns execution and needs the plan, criteria, and rehearsal design reviewed.

    Fractional CAIO

    Useful when one part-time executive mandate must own the framework and its operating cadence.

  3. Model architecture and evaluation study

    Public technical study

    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.

    Contribution and provenance

    Independent academic project by Jeremy Cleland.

    What the record demonstrates

    • CNN and ResNet encoder alternatives paired with an LSTM decoder
    • Configuration, training, prediction, metric, and analysis code kept as inspectable artifacts
    • Tests for the data loader and encoder plus a static technical report

    What it does not prove

    • 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.

    Inspect the evidence

    View source repository (opens in a new tab)Read the project report (opens in a new tab)

    Consulting relevance

    AI Strategic Advisor

    Relevant evidence for reviewing model architecture, experiment design, and evaluation claims.

    Fractional CAIO

    Relevant evidence for technical implementation and evaluation inside a defined executive mandate.

  4. Algorithms and system-design prototype

    Course team project

    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.

    Contribution and provenance

    CIS 505 team project by Jeremy Cleland, Saif Khan, and Asem Zahran. Presented here with full team credit.

    What the record demonstrates

    • A decomposed simulation spanning pricing, routing, demand prediction, coordination, and driver behavior
    • Unit tests and checked-in run artifacts that make the prototype inspectable
    • Technical communication through an executable repository and static project report

    What it does not prove

    • 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 ClelandCo engagement.

    Inspect the evidence

    View source repository (opens in a new tab)Read the project report (opens in a new tab)

    Consulting relevance

    AI Strategic Advisor

    Relevant evidence for decomposing a prototype and reviewing algorithms, assumptions, and evaluation.

    Fractional CAIO

    Relevant evidence for connecting technical work to an operating system and decision model.

Bring a decision, not a blank brief.

If one of these artifacts maps to a live operating question, the first call is for scoping the decision and the evidence it would require—not forcing it into a preset engagement.

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