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company

A studio that builds operating layers

DNSHN is not an agency and not a SaaS. We ship narrow products and install them with the client on their stack. We split marketing and sales into nodes, find where the leak is biggest, and put a product as a layer on top.

DNSHN 01 PHILOSOPHY
who we are

Turn scattered marketing into a working system

A client shows up with four vendors, seven ad accounts and one Excel sheet where numbers never reconcile. We come in and say: show us the node where the leak is biggest. We put our product on top, as a layer. Eight weeks later you have a measurable lift — or a clean «no».

Founded in 2026. Distributed team, clients worldwide in USD.

DNSHN 02 DIFFERENCES
02 · 4 PRINCIPLES

What you get that no agency will give you

01

Human-in-the-loop

AI drafts every action — budgets, bids, audiences, scripts. A human approves each one with a checklist and a sign-off. Zero automation without conscious «yes».

02

Vendor-agnostic

We take no kickbacks — not from paid platforms, not from CRMs, not from telephony. We recommend the tool that solves your task best, even when it hurts our margin. Your stack, your call.

03

One live report

Not monthly PDFs after the fact. One dashboard for the entire engagement: paid, CRM, telephony, attribution in one console. You see what we see.

04

Stop-loss in the model

No money without a measurable lift in the Pilot. Retainer cancellable with 30-day notice. Pilot only with a fixed stop criterion.

DNSHN · TODAY

Assembled a 4-product line in 2026

Each product closes one node of the marketing funnel: paid and ops (LOOP), inbound messages (ECHO), B2B intent signals (ATLAS). MOONLIGHT is the cross-cutting AI assistant, embedded inside LOOP as the CMO's assistant.

4 PRODUCTS

LOOP · ECHO · ATLAS · MOONLIGHT

Each with its own spec, pricing and Discovery checklist. Not a «bundle everything», but modules that land on the right node.

4 SEGMENTS

Clinics, events, B2B, brands

Segments where we hold the highest knowledge density and a repeatable engagement model. Each with its own Discovery and typical stack nodes.

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