Platform
Performance intelligence, built on evidence.
Our vision is a Google Ads audit that a finance director and a performance marketer can both trust: rigorous calculations, transparent evidence and recommendations framed around profit.
How it's built
Four layers working together.
Deterministic Python engine
Rule-based checks that calculate the same answer from the same data every time, so findings are reproducible and auditable.
Evidence verification
Each finding linked to the underlying account evidence, with clear flags where data is missing or insufficient.
AI-assisted recommendations
AI used to interpret verified findings and explain them in plain commercial language, not to invent the numbers.
Commercial prioritisation
Findings ranked by likely commercial impact and effort, so teams know what to fix first.
The framework
15 audit categories, 150 checks.
The framework is defined. The production service that runs it against connected accounts is in development.
- 01Account configuration and settings
- 02Conversion tracking and measurement
- 03Bidding strategies
- 04Budgets and pacing
- 05Campaign structure
- 06Keywords and match types
- 07Search terms and negative keywords
- 08Ads and assets
- 09Audiences and targeting
- 10Performance Max
- 11Shopping and product feeds
- 12Landing-page experience
- 13Revenue measurement
- 14Profitability and customer economics
- 15Commercial prioritisation
Availability
Where things stand today.
150-point audit framework and category structure.
Google account connection, audit engine and reporting.
Customer dashboard, access and disconnection controls.