Case Studies

Months of work, delivered in weeks.

These aren't client logos or revenue numbers. They're a look at how the work actually gets done — where AI-assisted engineering compresses a build, where it deliberately doesn't, and why the result still holds up.

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How the work goes

Three builds, two timelines each.

Each is a representative engagement, not a named client. The left lane is how the build went before; the right lane is the same build with AI doing the repetitive work. The timelines are typical, not guarantees.

01Data pipeline

Scattered sources into one queryable warehouse

A handful of SaaS exports, a production database, and two third-party APIs — all needed in one place, refreshed nightly, and trustworthy enough to build reporting on. The slow part was never the idea. It was the connector-by-connector grind.

Traditional approach~3–4 months

Hand-write an ingestion connector for every source
Map and reconcile mismatched schemas by hand
Write the transforms, then tests for the transforms
Stand up orchestration and backfill logic

AI-assisted~3 weeks

AI scaffolds connectors straight from each API's docs
Transform models and their tests drafted in bulk, then reviewed
Schema-mapping boilerplate generated, not typed
The time freed up goes to the data model and edge cases

AI compresses

The repetitive surface area — connector boilerplate, transform drafts, test coverage. The 80% that's typing, not thinking.

Stays human

The data model, what "correct" means for each field, how failures are handled, and validating the numbers against reality before anyone trusts them.

02Internal tool

The spreadsheet that finally became an app

A growing team running operations out of one shared spreadsheet — fragile formulas, no access control, a new bug every week. They needed a real internal tool, but a months-long build was hard to justify against a spreadsheet that "mostly worked".

Traditional approach~6–8 weeks

Build CRUD screens for every entity
Wire up an API layer by hand
Add auth, roles, and an audit trail
Re-create every chart the spreadsheet had

AI-assisted~1 week

AI scaffolds the CRUD UI and API routes from the data model
Charts and filters wired from a described spec
Auth and role gating dropped in from a proven pattern
Engineer owns the data model and who-can-see-what

AI compresses

The scaffolding — screens, routes, forms, the chart wiring. Work that's well-understood and just needs doing.

Stays human

The data model, the permission boundaries, and the judgement call on which spreadsheet habits to keep and which to quietly fix.

03Migration

Modernizing a legacy system without the rewrite gamble

An aging system that still worked but nobody wanted to touch. A full rewrite was the obvious risk; a careful, incremental migration was the right call — and historically the slow one, because mapping what the old code actually does is painstaking.

Traditional approach~4–6 months

Read the legacy code to map what it really does
Write characterization tests to pin current behavior
Port the system module by module, by hand
Plan and rehearse the production cutover

AI-assisted~5 weeks

AI maps the legacy surface area and flags the dark corners
Characterization tests generated against current behavior
Migration scripts and new module skeletons drafted
Engineer owns the cutover plan and every risk call

AI compresses

The archaeology — reading old code, writing tests that capture current behavior, drafting the port. The tedious, high-volume part.

Stays human

The cutover sequencing, the risk assessment, and verifying the new system behaves exactly like the old one where it has to.

The method

Fast because of judgment — not instead of it.

The speed-up isn't magic and it isn't cut corners. It's the same engineering, with the repetitive 80% handed to AI and the decisions that matter kept firmly human.

Scoped before a line is written

Every build starts with a tight, written scope. AI accelerates execution — it doesn't decide what to build. That's still a conversation.

AI does the volume

Boilerplate, scaffolding, first-draft tests, repetitive transforms. The work that's well-understood and just needs typing — done in a fraction of the time.

A human owns correctness

Architecture, data models, security boundaries, and verification stay with the engineer. AI drafts; review and judgement are what ship it.

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