Developer Brief
Control Tower – Marketing Process Governance Platform (MVP)
Project Overview
We are building a lightweight web-based workspace for managing marketing and communications processes, starting with product launches.
The goal is to create a structured system that connects strategy → content creation → quality checks → approvals → export, ensuring that all marketing assets align with the defined launch strategy and pass structured quality checks before they are published.
Today, marketing teams operate across disconnected tools such as documents, email, Slack, design platforms, and AI tools. Strategy often gets lost in translation and content goes live without proper alignment, review, or compliance checks.
This product solves that problem by introducing a single operational workspace called the “Control Tower.”
Within this workspace, teams define launch strategy once, generate or upload assets, run structured quality checks (“Pre-flight”), and approve assets before publishing.
The first version is a focused MVP built specifically for product launches, which will later expand to campaigns, events, and other marketing workflows.
What We Want to Build
1. Web Application (Control Tower Workspace)
A browser-based platform where users can:
- Create and manage product launches
- Define strategy and constraints
- Upload or generate marketing assets
- Run automated quality checks
- Review issues and approve assets
- Export approved assets with metadata
No desktop software.
Everything runs in the browser.
Target scale for MVP is small teams and pilot customers.
Core Product Components
1. Launch Workspace
Each product launch acts as a container for strategy, assets, and approvals.
Key capabilities:
- Create new launch projects
- Define launch metadata
- Track assets related to the launch
- View status of assets and approvals
Typical users:
- Marketing lead
- Communications lead
- Brand reviewer
- Legal reviewer
- Final approver
2. Backbone Editor (Structured Strategy)
The Backbone is a structured form that captures the launch strategy.
It acts as the single source of truth for all messaging.
Example fields:
- Launch ID and metadata
- Target audience segments
- Positioning statement
- Messaging hierarchy
- Proof points
- Brand constraints
- Terminology to avoid
- Legal claims and disclosures
- Channel plan
- Success metrics
All assets must reference the Backbone to prevent messaging drift.
Control Tower_ Product Launch M…
3. Brand Vault
A repository for brand and compliance materials that guide asset creation.
Examples:
- Corporate Visual Identity guidelines
- Tone of voice guides
- Legal templates
- Required disclaimers
- Approved terminology
Users upload these once and the system references them during asset checks.
4. Asset Management
Users can create or import assets such as:
- Web copy
- Email campaigns
- Social media posts
- Sales presentations
- PR announcements
Assets can be:
- Generated using integrated AI tools
- Imported from external tools
Each asset is linked to the launch and tracked through its lifecycle.
5. Pre-flight Quality Engine
This is the core differentiator of the product.
Every asset must pass a structured quality check system before it can be approved.
Pre-flight checks evaluate:
- Strategic alignment with Backbone messaging
- Tone of voice compliance
- Brand terminology usage
- Claims and proof point validation
- Presence of required disclosures
- Channel specific formatting rules
- Factual integrity
Output includes:
- Scorecard (pass / partial / fail)
- Issue list with severity
- Location of problems in the text
- Suggested fixes generated via AI
Pre-flight should complete within roughly 10 seconds for a typical asset.
Control Tower_ Product Launch M…
6. Approval Workflow
Assets move through a simple approval process.
Flow example:
- Asset owner uploads asset
- Pre-flight runs automatically
- Issues are flagged
- Asset owner resolves issues
- Asset is submitted for approval
- Approvers review and approve or reject
The system should maintain:
- status tracking
- notifications
- timestamps
- reviewer notes
7. Immutable Audit Log
The platform must maintain a permanent log of actions for compliance.
Examples:
- asset uploaded
- pre-flight run
- issues flagged
- approval decision
- user who performed action
- timestamps
The audit log should be exportable for compliance purposes.
8. Import / Export
Import capabilities:
- text assets
- documents
- structured content
Export capabilities:
- approved assets
- compliance metadata
- pre-flight report
AI Tool Integration
The MVP should support 2–3 AI tool integrations via API.
Examples:
- LLM for text generation
- Image generation API
- Document generation / export
Important constraints:
- No model training on customer data
- AI tools used through API only
- Vendors must be replaceable
Architecture should support swapping providers without major code changes.
This system will rely on retrieval based context (RAG) rather than training models on user data.
Control Tower_ Product Launch M…
Tool Registry
The platform must maintain a registry of all AI tools used.
Purpose:
- compliance transparency
- vendor flexibility
- data handling visibility
Registry must track:
- tool name
- capability
- API usage
- data processed
- compliance classification
Roles and Access
Basic role system for MVP.
Roles include:
- Marketing Lead
- Communications Lead
- Asset Owner
- Brand Reviewer
- Legal Reviewer
- Approver
- System Admin
Users may hold multiple roles.
Role based access control should exist but can be simplified in MVP.
Non Functional Requirements
Security
- HTTPS everywhere
- authentication (OAuth2 or SAML)
- encryption at rest and in transit
Performance
- page load under 2 seconds
- pre-flight analysis under 10 seconds
Reliability
- 99.5% uptime target for MVP
- automated backups
Concurrency target
- about 20 simultaneous users
Logging
- application logs
- audit logs
- versioning for strategy and assets
Compliance Requirements
The system must support:
GDPR transparency
- list of subprocessors
- record of AI tools used
- exportable audit records
EU AI Act transparency
- documentation of AI systems used
- disclosure capability for AI generated content
Data handling
- no training on customer data
- clear data retention policies
- deletion support where required
What Is NOT Included in the MVP
The first version intentionally avoids enterprise complexity.
Not included:
- custom AI model training
- deep integrations with CMS or DAM systems
- multi tenant SaaS infrastructure
- complex enterprise permissions
- advanced analytics
- full creative production tools
The goal is to prove the product concept quickly with a working pilot.
Technology Expectations
We are open to developer recommendations, but the solution should likely include:
Frontend
- React or similar modern framework
Backend
- Node.js / Python / similar
Database
- relational or document database depending on architecture
Infrastructure
- cloud native deployment
- containerized architecture preferred
API architecture
- integration layer for external AI tools
- modular tool adapters
What We Need From the Developer
We are looking for a development partner who can:
- Validate the architecture
- Propose a technology stack
- Estimate development effort
- Build the MVP
Deliverables expected during proposal:
- architecture overview
- recommended tech stack
- rough effort estimate
- timeline proposal
- team structure
