What We Build
AEA helps digital-service agencies build the foundations, workflows, controls, and systems that let people and AI work together more consistently across the business.
The Replatforming Program is organized around eight configurable modules. The Audit determines which areas are already sound, which need validation or repair, which need to be built, and what should wait.
You do not automatically buy all eight. You do not automatically rebuild everything you already have.
The information AI-supported work depends on: SOPs, company knowledge, service knowledge, client context, brand guidance, process documentation, examples, and source ownership.
Create a clearer, more authoritative source environment so people and AI are not repeatedly reconstructing the same context from memory, scattered files, or individual prompts.
How approved company, service, workflow, and client context is made available inside the AI working environment.
Reduce repeated context rebuilding and make shared AI use more consistent, governed, and useful across roles.
Priority workflows, including inputs, outputs, ownership, handoffs, review points, exceptions, client variation, and where AI should or should not support the work.
Redesign work around the right combination of human judgment, AI assistance, and repeatable process rather than adding AI on top of an unclear workflow.
The systems and handoffs that move information and work through the agency, plus controlled automation where the underlying process is stable enough to justify it.
Connect or rationalize systems where there is a real operating case, reduce unnecessary manual transfer, and automate defined work without automating chaos.
Who can access what, what information may be used with AI, how sensitive or client data is handled, where approvals are needed, and how ownership and escalation work.
Make AI use more deliberate and accountable without pretending governance is solved by a one-page policy.
Quality criteria, review gates, testing, approval rules, exception handling, human-in-the-loop controls, and accountability for consequential outputs.
Build human review and quality control into AI-supported work instead of treating QA as an informal final check.
The role-level guidance, practice, ownership, and change support people need to operate the new system consistently.
Move beyond "give the team an AI tool" toward a shared way of working that people understand and can actually adopt.
Baselines, KPIs, tracking, review cadences, operating signals, and improvement loops.
Create a way to determine what changed, what is working, what is not, and what should be improved next instead of assuming AI created value.
AEA does not automate unstable or undocumented processes simply because automation is technically possible.
If the Audit finds material foundation gaps that make downstream implementation unreliable, unsafe, or difficult to maintain, those gaps need to be addressed first.
If the agency already has a sound foundation, AEA can validate and retain it rather than rebuilding it for the sake of selling more work.
The Audit determines:
This section is important because it converts the eight-module architecture from what could otherwise look like a service menu into an evidence-configured Program.
The modules are a way to organize the work, not a menu of disconnected consulting products. AEA starts with the operating model the agency actually has, then builds the parts the evidence supports.
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