Done-With-You Program starts Jan 2 · Learn more →
VisionList

New Offer: Specifically for digital product builders and marketers who want greater control over AI

VisionList — Done-With-You Implementation

Install the Context Layer Your AI Is Missing

A new & different way to

Achieve up to 50% faster time-to-market for AI-powered products and initiatives.

Without drift, misalignment, and retraining costs that come from using AI without a clear context layer.

This is the flagship implementation for teams who want AI to compound — not just perform demos.

Book a Discovery Call

VisionList installs the business context AI needs to operate reliably — so execution compounds instead of resetting.

Executive Summary

The purpose of this page is to help you decide whether your AI challenges are tooling issues — or decision-infrastructure issues.

VisionList Done-With-You installs the missing context layer by productising your business knowledge — making intent, constraints, and priorities explicit so AI can reason deliberately instead of defaulting to fast, intuitive guesses.

If you’ve reached the point where experimentation no longer compounds, outcomes feel plausible but unreliable, and execution keeps resetting, this page will help you determine whether a Done-With-You installation is the right next step.

This Is Not Coaching. It’s Installation.

Most teams reach a point where advice, templates, and experimentation stop helping. That’s where this engagement begins.

The Done-With-You engagement is designed for teams who need decision quality, not experimentation.

  • We don’t teach theory.
  • We don’t sell templates.
  • We don’t hand you tools and wish you luck.

We work directly with you to install a Unified Context Layer your team — and your AI systems — can trust, using a practical web-based workspace designed to maintain it over time.

What Actually Breaks AI Initiatives

Across startups, scale-ups, and AI-forward teams, the same failure patterns appear again and again:

  • AI outputs contradict each other
  • Teams restart decisions from empty chats
  • PRDs and code drift apart
  • Pilots look impressive but don’t compound
  • Automation amplifies the wrong behaviour

This is not a tooling problem.

It’s a decision infrastructure problem.

What We Install — Only What You Need

We don’t install everything at once. We install only what’s required, in the order that prevents rework and drift.

During the DWY engagement, we make the following explicit — only as needed, and in the right order:

1 · Opportunity & Vision Definition

Required

We capture the core business model and measurable definition of success — without sugar-coating.

  • Opportunity and impact
  • Evaluation and proof points
  • Definition and differentiation
  • Transformation concept
  • Services, scenarios, and priorities

Result: VDD — Vision Definition Document

2 · Extended Definition & Campaign Context

Required

We define the contextual detail behind offers, products, plans, and constraints — so AI understands both intent and limits.

This prevents AI from optimising in the wrong direction.

Result: XDD — Extended Definition Document

3 · System Context Definition

As Needed

We capture the operating reality of the business:

  • Processes and dependencies
  • Systems and assumptions
  • Specifications for new products or initiatives

This layer can also be used to generate PRDs when required.

Result: SCD — Systems Context Document

4 · Execution Metadata Definition

As Needed

We define how the business actually runs:

  • Decision logic and priorities
  • Rules, learnings, and constraints
  • Team structure and escalation paths

This is critical for AI to recommend actions that fit the business — not fight it.

Result: EMD — Execution Metadata Document

5 · Agent-Ready Definition

As Needed

We define what AI agents are allowed to:

  • do
  • decide
  • escalate
  • defer

Each agent receives a clear instruction set tied to a specific task, ensuring effectiveness without loss of control.

Result: ARD — Agent-Ready Definition Document

The Output

A set of structured, machine-readable documents, stored in your V-Wallet — reusable across AI tools, agents, and workflows without resetting.

What You Leave With

You will have a context-first operating layer you can build on for years — not another system you’ll replace:

  • Your core business context is explicit and structured
  • Contradictions and ambiguity are resolved
  • Decision boundaries are clear
  • Context is portable across AI systems
  • At least one AI agent operates reliably within defined constraints

This is the foundation that allows automation to scale without losing control.

Who This Is For

This is NOT a fit if you’re looking for quick prompts, generic agents, or plug-and-play automation without upstream clarity.

This engagement is right for you if:

  • AI initiatives feel busy but unreliable
  • Direction keeps resetting as conditions change
  • You want speed without sacrificing coherence
  • You’re preparing for agents, automation, or scale
  • You don’t want to debug context alone

No technical or engineering background is required. This is a business-level implementation focused on decisions, intent, and operating clarity — not coding or tooling.

How This Is Typically Used

Teams typically use Done-With-You to:

  • Stabilise direction before scaling agents
  • Prepare for automation without locking into vendors
  • Replace fragile prompt chains with governed context
  • Reduce reliance on constant retraining or FDSE-heavy setups

In simple terms, we help you get AI working with distilled business context — giving you control, reliability, and validation before agents operate.

Our Guarantee

You Leave With a Working System

Progress is measured by working artifacts and observable execution stability — not hours, calls, or content delivered.

Done-With-You is not time-boxed coaching.

We stay engaged until the system meets all three conditions below.

1. Control is installed

You have a clearly defined, documented context layer (VDD/XDD/SCD/EMD as applicable) stored in your V-Wallet — and your team knows what must remain true as execution evolves.

2. Reliability is demonstrated

The same context produces consistent AI outputs across sessions, tools, or agents — without re-explaining, prompt rewriting, or contradictory recommendations. If outputs still reset, drift, or conflict, this is not complete.

3. Validation exists before automation acts

At least one AI workflow or agent operates within defined boundaries — with clear rules for what it may do, decide, escalate, or defer. If AI is still acting without governance, this is not complete.

If these conditions are not met, the engagement continues.

Next step

👉 Book a Discovery Call

No pitch. Just clarity on whether this solves your problem.

Book a Discovery Call