Strategic approach

A Delivery Model Built for Speed, Clarity, and Control.

Implementation from EUR 1,800. Monthly maintenance from EUR 300. Phased payments available.

Operating model

A delivery structure designed to move from bottleneck to live system without losing control.

This process is designed for companies that want clarity before complexity. The method prioritizes leverage identification, disciplined solution design, safe integration, and post-launch improvement rather than automation for its own sake.

Analysis and Strategy

We review your workflows, decision points, and commercial goals to define where automation will create the strongest leverage before any build begins.

Design and Training

We shape the agent logic, tone, and operating behavior around your business rules so the system reflects the way your team actually works.

Integration and Validation

We connect the solution to your stack, test real use cases, and validate outputs carefully before the system moves into production.

Launch and Optimization

We activate the workflow, monitor live performance, and refine the operating layer continuously so it improves after rollout instead of standing still.

Clear first scope before build

Human oversight where risk matters

Post-launch optimization built in

Delivery profile

The process is built around speed, oversight, and measurable business logic.

Core delivery phases

From diagnosis to optimization with a clear sequence of work.

EUR 1,800

Implementation starting point

Initial build scope starts from a defined commercial baseline.

EUR 300

Monthly maintenance entry

Ongoing refinement and operating support after launch.

Approval logic retained

Human review stays where quality, pricing, or brand control matters.

Phased

Payment structure available

Commercial rollout can be staged rather than forced into one block.

Live

Optimization continues post-launch

The workflow is refined after deployment, not abandoned.

Delivery sequence

A more executive way to understand the rollout path.

Analysis and Strategy

We identify the best first automation target and define what success should look like before build work starts.

Design and Training

We shape the logic, operating behavior, and output quality standards so the workflow matches your real environment.

Integration and Validation

We connect the workflow to your stack, test edge cases, and validate safety before the system goes live.

Launch and Optimization

We launch, monitor, and improve the system based on actual performance rather than assumptions.

Execution principles

What makes this delivery approach commercially safer.

Leverage first

The first question is where time leaks, where response speed matters, and where consistency failures already hurt the business.

Business rules before tooling

Agent logic, approval rules, and output standards are shaped around the way the company actually operates.

Safe integration

Systems are connected carefully, tested on real use cases, and validated before they are relied on live.

Continuous refinement

The rollout is treated as an operating layer that improves over time rather than a one-off technical handoff.

Safeguards

The process is structured to prevent the most common automation mistakes.

No generic rollout

The workflow is not copied from a template and dropped into the business without adaptation.

No blind launch

Validation happens before live deployment so the system is tested against real scenarios.

No loss of control

Human approval remains in place where strategic, financial, or quality-sensitive decisions still matter.

No static handoff

Optimization continues after launch so performance can improve in live operating conditions.

Readiness lens

The strongest first implementation usually starts where three things are already true.

The team is spending noticeable time on repetitive operating work.

Response speed or consistency already affects commercial performance.

There is a workflow clear enough to define inputs, outputs, and approval logic.

Operational clarity before automation scale.

The process is intentionally conservative where risk matters and fast where leverage is obvious. That balance is what makes the delivery model more credible for SMEs that want practical gains, not technical theatre.

Pricing plans

Simple, transparent pricing with selectable plans.

Every engagement starts with a free consultation. We map your processes, identify the biggest time wasters, and recommend the right delivery level before implementation begins.

Starter

Implementation

EUR 800

Retainer

EUR 150/mo

Agents

1 agent

Your first AI agent with 2-week deployment, human approval setup, documentation, and 1 month post-launch support.

Starter

Growth

Implementation

EUR 1,500

Retainer

EUR 250/mo

Agents

2 agents

Two connected agents with cross-tool integration, Airtable data layer setup, optimization calls, and 2 months support.

Growth

Professional

Implementation

EUR 2,500

Retainer

EUR 400/mo

Agents

3-4 agents

A multi-agent operating system with live KPI dashboard, 1-hour team training, and priority response under 24 hours.

Professional

Enterprise

Implementation

Custom

Retainer

From EUR 600/mo

Agents

5+ agents

Full discovery, process mapping, enterprise-grade integrations, dedicated reporting, and flexible scaling support.

Enterprise

50% upfront at contract signing and 50% upon delivery and handover.

Monthly retainers are billed on the 1st of each month with a 3-month minimum commitment.

The retainer covers monitoring, maintenance, bug fixes, minor updates, and monthly performance review.

StratonOak context

The Strategic Rationale Behind the Delivery Model.

StratonOak was built around a simple premise: small and medium businesses do not need more software clutter. They need a more disciplined operating layer that reduces friction, improves response quality, and creates leverage for the team behind the business.

That perspective comes from a blend of finance, quantitative modeling, and applied automation work. The systems are designed not just to function technically, but to make commercial sense in live operating environments.

The result is a more focused delivery model: practical AI, measurable outcomes, and direct collaboration from strategy through implementation.

WhartonFinance and quantitative modeling background

n8nCertified AI agent development

SMEsFocused on real operating environments

Strategic fit

Why this approach tends to work better for smaller and mid-sized companies.

SMEs usually do not need enterprise-scale transformation programs. They need one disciplined operating improvement that recovers time, reduces drag, and proves its value quickly enough to justify the next step.

Start Now Request a Consultation

If the process makes sense, the next step is choosing the first workflow worth automating.

That first workflow should be the one where time loss, response delay, or reporting friction is already visible today.