AI recommendation optimization
Know whether AI recommends you. Improve the evidence. Measure what changed.
AI recommendation optimization is the process of testing how AI assistants respond to real buyer questions, improving the public information influencing those answers, and repeating the same tests to measure movement.
The business problem
When a buyer asks an AI assistant who to hire or compare, the assistant may answer before the buyer visits any provider’s website. A business can lose that decision because its category fit is unclear, its proof is weak, its facts conflict, or stronger third-party sources support a competitor.
What Good Productivity does
- Benchmark: freeze realistic buyer questions and record the starting position.
- Diagnose: identify who is recommended, what sources are used, and where the evidence breaks down.
- Improve: prepare or implement the highest-value website, technical, and public-proof changes allowed by the selected plan.
- Rerun: ask the same questions again and report durable improvement, volatility, or no movement.
What you receive
- A recommendation benchmark and founder-readable scorecard.
- Competitor, citation, and factual-accuracy evidence.
- A prioritized action plan tied to the tested buyer questions.
- Implementation support according to the selected plan.
- Repeated measurement showing whether the recommendation position changed.
The claim
Good Productivity shows where a business stands in tested AI recommendations, why competitors may be winning, and what is most worth improving first.
The promise—and the boundary
We promise a documented starting point, prioritized work, transparent evidence, and a before-and-after measurement process. We do not promise that any AI assistant will place a business first or that a score change will create a specific amount of traffic, leads, or revenue.
Is this the same as making a website agent-ready?
No. Agent readiness focuses on whether an AI agent can crawl, understand, and interact with a website. Recommendation optimization includes those technical foundations, but it also measures whether the business is actually selected in buyer decisions and whether changes improve that position. Read the practical guide to getting recommended by AI.
Who should consider it
The strongest initial fit is a service business or ecommerce company with meaningful customer value, a public website, identifiable competitors, and enough authority to implement the recommended changes. It is a weak fit for a business expecting guaranteed rankings or unwilling to publish verifiable proof.