Approach

Public → hybrid → private. An evolution, not a bet.

Model choice isn’t a one-time decision — it’s a trajectory. Start on public frontier models to ship fast; move workloads private only as they earn it. You don’t have to pick perfectly on day one.

Book an audit →See services

Public→Hybrid→Private

Public

Start here

Frontier APIs — Claude, GPT, Gemini. Fastest to value, lowest upfront cost, best raw capability. Right for proving the use case and for any non-sensitive workload. Most clients should begin here; shipping beats theorizing.

Hybrid

Where most mature orgs land

As usage grows, some workloads develop reasons to move — sensitivity, cost, latency, control. A gateway classifies and routes: sensitive or high-volume work runs private, everything else stays on the frontier. Providers stay abstracted, so there's no lock-in and no all-or-nothing bet.

Private

For the workloads that warrant it

Self-hosted, data-sovereign deployment for the highest-stakes slice — regulated data, sustained volume, strict latency or control. Rarely everything; deliberately the part that earns the added effort.

What moves a workload along the curve

Five forces, weighed per workload.

Force	Stays public	Moves toward private
Data sensitivity / regulation	Low-sensitivity, non-regulated	PII / PHI / privileged; HIPAA / GDPR
Quality bar	Needs the absolute frontier	Well-served by strong open models
Cost at scale	Low or spiky volume	High, sustained volume
Latency / availability	Fine with network round-trips	On-prem / edge / offline
Control / lock-in	Speed-to-ship matters most	Long-term control, portability

The hybrid gateway

One boundary that classifies, redacts, and routes.

Sensitive data is classified and kept home; non-sensitive work goes to the frontier. PII is redacted at the boundary, every call is logged, and providers are abstracted — so switching models is a config change, not a rebuild.

On a retainer, we keep that routing correct as models, prices, and regulations move.

How we engage

The path is also the engagement arc.

A public AI System Build to start, a hybrid gateway as you scale, a private deployment for the workloads that earn it — with a managed retainer keeping it all correct over time. We meet you where you are and move only what warrants moving.

The Marain Method

Why our automations get adopted.

The failure mode of automation isn’t technical — it’s that nobody uses it. We treat human and cultural context as a first-class engineering constraint.

Listen

Map the real workflow — formal, informal, political. Find who must adopt this, and what would make them resist.

Locate

Pinpoint the highest-ROI, lowest-adoption-risk opportunities. Quantify them.

Design

Architecture, data governance, eval plan. Privacy and human-in-the-loop by default.

Build

Iterative, demoed early. Ship a tracer-bullet slice first, then expand.

Land

Roll out with the humans: training, docs, champions, feedback. Adoption is a deliverable.

Sustain

Transition to managed. Measure realized ROI. Find the next opportunity.

Let’s find where your workloads belong.

The Opportunity Audit places each one on the curve — and gives you a plan to act on.

Book an audit →