Case studies

Selected work.

Current and recent engagements, described the way we run them. Client names are withheld and details generalized; the numbers are real and shared with permission. References available in scoping conversations.

Modernization & AI-assisted migration Education-services company

AI-accelerated SQL Server → MySQL reporting modernization

Situation

A SQL Server reporting estate with hundreds of procedures, reports, subscriptions, SSIS packages, and SQL Agent jobs needed to move to MySQL 8 without changing report meaning or output.

Where it stands

More than 300 reports and procedures now move through one tracked workflow for discovery, conversion, parity testing, exception review, and approval. The migration remains in progress; objects are not counted as complete until output parity is proven.

Approach

Built an inventory that connects database objects to reports, packages, schedules, and downstream dependencies.
Used specialized AI agents for high-volume analysis and first-pass conversion while routing unsupported patterns and design decisions to senior review.
Created a QA harness that compares SQL Server and MySQL results row by row for representative test cases.
Required human approval for parity exceptions, architecture changes, and release readiness.

Verification
Object counts come from the migration factory's tracking inventory; parity is proven per object by a harness comparing source and target result sets row by row, with human sign-off gating approval.

Limitation
The migration is in progress — figures describe the working queue and validated objects, not a completed cutover.

Database infrastructure National logistics provider

End-to-end SQL infrastructure management

Situation

A growing SQL Server estate across production, staging, QA, and development lacked one operating standard for builds, access, refreshes, maintenance, performance, and recovery.

Where it stands

The estate now runs to documented standards across environments. Refreshes end with validation reports, job failures have named owners, and one partitioning change reduced a measured nightly maintenance window from roughly 42 minutes to 12.

Approach

Standardized capacity planning, server builds, Availability Groups, deployments, monitoring, backup, and recovery.
Put staging refreshes, job-health review, data sanitization, access control, validation, and rollback on repeatable runbooks.
Used Query Store and operating reports to prioritize query, index, storage, and maintenance work.

Verification
Refresh runs are driven by control tables and end with a validation phase and a written report; job health is tracked in dated remediation reports; and the partitioning work's Query Store-based impact report measured the nightly maintenance window falling from roughly 42 minutes to 12.

Limitation
Figures describe the estate as currently operated — the maintenance-window measurement is from its first full cycle, and job-failure counts vary week to week.

Reporting & data automation Real-estate financing advisory

Deal-sourcing signal engine — validated before it was built

Situation

An advisory working in federal tax-credit financing (NMTC) asked whether an automated "signal engine" could surface manufacturing expansion projects earlier than the usual intermediaries — and whether building one was within reach.

Where it stands

All three reviews rejected the original premise. The client avoided a premature build and adopted a narrower plan in which each remaining unknown can stop the project for under $1,000 before larger spending is authorized.

Approach

Tested the load-bearing claims against county agendas, permit feeds, a live geocoder-to-census-tract call, and commercial data pricing.
Used three independent, reject-by-default reviews to challenge source timing, identity resolution, geographic precision, and build-versus-buy assumptions.
Reframed the useful window as post-announcement but pre-financial-close, then completed a retrospective backtest.
Limited any future build to the screening layer vendors do not sell, with human QA for extraction, geography, and event matching.

Verification
Every refuter verdict is documented with per-claim evidence — live checks of the public sources, a live geocoder-to-tract call, and verified vendor pricing — and the adopted plan declares its gates in writing.

Limitation
Validation and the retrospective backtest are complete; the conversion pilot — the gate that decides whether anything gets built — is still ahead.

Other automation work

A production workflow built with the same operating controls used in the database practice.

Reporting & data automation Real-estate operator

Automated content and distribution pipeline

Situation

A solo operator needed a steady weekly content presence across social networks without hiring a team or spending hours a day posting manually.

Where it stands

A hands-off weekly production and daily distribution cadence running on tens of dollars a month of cloud infrastructure, operated by one person.

Approach

Built a scheduled AWS pipeline for scripts, captions, rendered vertical video, and distribution through a publishing API.
Stored market rules in configuration so a new market does not require a code rewrite.
Provisioned the system as code and put changes through CI before deployment.

Verification
Infrastructure cost is read directly from the cloud bill; generation and posting runs are scheduled, logged, and reviewed through the project's CI pipeline.

Limitation
The system measures output cadence and cost, not audience outcomes — distribution results accrue on the client's channels, not in this pipeline.

When clients engage us

A business-critical database environment with recurring incidents, performance problems, or operational risk
A SQL Server, MySQL, PostgreSQL, reporting, or cloud migration that requires dependency discovery, validation, and controlled cutover
Reporting, ETL, SQL Agent, backup, recovery, or environment-refresh processes that are fragile, manual, or poorly owned
A need to design, build, standardize, or manage SQL infrastructure across production and lower environments
An AI, data-product, or automation idea that should be validated before significant development spending
A repeatable content or operational workflow that can be governed, automated, monitored, and scaled
Limited internal senior database capacity for a high-risk initiative
A need for an independent technical assessment before a major architecture, migration, or investment decision

When another provider is a better fit

General IT help desk or desktop support
Commodity website or mobile-app development
Generic chatbot projects without a defined data, workflow, or business problem
Standalone social-media management or creative marketing services
Experimental AI projects without measurable outcomes, usable data, or human review controls
Low-cost staff augmentation without clear ownership, scope, and acceptance criteria
Projects that require unsupported claims, fabricated metrics, or uncontrolled automation
Routine implementation work where the solution is already known and no senior engineering judgment is required

Not sure whether the engagement fits? Use the initial consultation to define the problem, determine whether specialist involvement is warranted, and identify the smallest responsible next step.

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