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Case Studies

Work you can see. Systems you can use.

Explore our published client websites, the engineering behind representative builds, and the way we run MomentumQ on our own software.

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01Client projects

Designed and built, end to end.

Two recent public builds — each taken from first sketch to live domain.

Invora Immobilien AG · Corporate site

Invora

A clear address for a Swiss property business.

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Monzarié · Brand site

Monzarié

A considered digital home for a knitwear label.

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02How 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.

Data pipeline

Scattered sources into one queryable warehouse

Explore the workflow 02

Internal tool

The spreadsheet that finally became an app

Explore the workflow 03

Migration

Modernizing a legacy system without the rewrite gamble

Explore the workflow

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.

03Running the company on it

MomentumQ runs on VorenQ.

Not a client story — our own operations. VorenQ is the agent workspace we're building, and MomentumQ is its first customer. These are the two places it already carries the load, with real numbers from our own week.

04Bookkeeping · Bexio

The books, on 15 minutes a week.

Supplier invoices arrive by email, sales invoices go out of Bexio, payments want matching, and the VAT method has opinions. That used to eat half a day every week. Now a VorenQ agent does the grind — and we approve.

Traditional approach~4 h / week

Pull every invoice out of the inbox, download, rename, file it
Type it into Bexio — supplier, dates, amounts, VAT code
Match incoming payments to open invoices by hand
Chase due dates and reconcile at month-end

With VorenQ≤ 15 min / week

An agent reads each invoice from the mailbox and drafts the Bexio booking — account, dates and VAT method checked
Duplicates and wrong billing entities are refused automatically
A person approves each booking — nothing posts unseen
Payment matching and a weekly reconciliation run on schedule

AI compresses

The grind: reading PDFs, typing bookings, matching payments, filing originals — the part that made bookkeeping a weekly afternoon.

Stays human

Every booking is approved by a person before it posts, and the tax decisions stay ours. The 15 minutes are the approving — not the typing.

05Marketing & sales

80% automated, 100% reviewed.

Content drafts, social posts, lead research and follow-ups run as scheduled VorenQ workflows. Roughly 80% of the volume is produced by agents; strategy, taste and the final yes stay human — which is exactly why the quality holds.

Traditional approach~2 days / week

Plan and write every post and newsletter by hand
Research prospects one tab at a time
Write each follow-up from scratch
Output depends on whoever had time that week

With VorenQ~80% automated

Agents draft posts and newsletters on a content schedule, in our voice
Lead research and qualification run as workflows
Follow-ups are drafted and wait in the approval queue
A person reviews everything outbound — the 20% that decides quality

AI compresses

The volume: first drafts, research, formatting, scheduling — about 80% of the hours marketing and sales used to take.

Stays human

Positioning, tone, and the final approval on anything a customer sees. Nothing ships unreviewed — that's the deal that keeps quality up.

Meet VorenQ

04The 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.

05Get started

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