Recommendation Intelligence & Engineering

When AI Decides
Who to Recommend,
Will It Choose You?

ChatGPT, Gemini, and AI-powered search are becoming a new layer in how customers discover, compare, and choose businesses.

Haenisch helps companies understand which customer decisions they are winning, which are going to competitors instead, and which opportunities are actually worth changing.

Request an AI Recommendation Analysis

Decision → Candidates → Beliefs / Evidence → Recommendation → Opportunity

The shift 01

Your Customers Aren't Searching the Way They Used To.

For years, businesses competed for visibility in Google, social media, paid advertising, directories, and marketplaces.

Now another decision layer is emerging.

A prospective customer can ask an AI system:

“Who would you recommend?”

“What's the best option for someone like me?”

“Which company is best for this specific problem?”

And instead of receiving a page of links, that customer may receive a handful of recommendations.

That changes the question.

It is no longer only:

Can customers find you?

It is increasingly:

Does AI choose you?

Recommendation intelligence 02

Being Visible to AI Is Not the Same as Being Recommended.

A business can have a strong website, excellent reviews, meaningful credentials, years of experience and a great product—and still lose a particular customer decision to a competitor.

And there is rarely one universal “AI ranking.”

Your business may be strongly recommended for one type of customer and barely considered for another.

ChatGPT may treat you differently from Gemini.

Identical questions can even produce different recommendation sets across repeated tests.

That's why a screenshot isn't enough.

We measure the recommendation behavior behind the answer.

What Haenisch does 03

Understand the Decisions AI Makes About Your Business.

Haenisch combines proprietary technology, repeated experimentation and strategic analysis to study how AI systems make commercially important recommendations.

Where You Win

Which customer decisions consistently lead AI systems toward your business.

Where You Lose

Which valuable customers are being directed toward competitors instead.

Who Wins Instead

Which competitors repeatedly enter the recommendation set—and under which buying situations.

How Reliable the Signal Is

We repeat recommendation tests rather than treating one AI response as a permanent ranking.

What Actually Matters

We separate meaningful business opportunities from noise, volatility, poor-fit customers and gaps that simply aren't worth pursuing.

Because the objective isn't to appear everywhere.

It's to win the customer decisions worth winning.

How it works 04

From Recommendation Intelligence to Measured Action

Measure → Diagnose → Prioritize → Test → Learn.

Define the Buyers That Matter

We start with your business.

Which customers do you most want?

Which products or services matter most?

Where do you have capacity?

Which opportunities have the greatest strategic value?

That determines what is worth measuring.

Measure the Recommendation Environment

We test realistic customer buying situations across leading AI systems.

We measure recommendation inclusion, primary recommendations, competitive position, consistency and differences between AI environments.

And because AI recommendations can vary, we use repeated observations where necessary rather than pretending one response is definitive.

Map Your Recommendation Territory

We identify where your business is:

Winning.
Losing.
Provider-dependent.
Volatile.
Or facing a potentially valuable recommendation gap.

Determine What Is Worth Changing

Not every loss is a problem.

Sometimes another business genuinely is a better fit.

Sometimes the customer isn't valuable to you.

Sometimes the signal isn't reliable enough to act on.

We focus deeper analysis where four things intersect:

Business value.
Genuine fit.
A meaningful recommendation gap.
A plausible ability to influence the outcome.

Diagnose, Engineer & Measure

Where an opportunity deserves deeper investigation, Haenisch can go beyond measurement.

We examine the beliefs and evidence surrounding the recommendation decision, identify potentially controllable variables, and develop a defined intervention hypothesis.

Where appropriate, we can then help engineer the intervention and remeasure recommendation behavior afterward.

The difference 05

This Isn't Another AI Visibility Dashboard.

Knowing that your business appeared in 37% of AI prompts can be useful.

But it doesn't answer the most important questions.

Which customers were behind that 37%?
Which customers did you lose?
Who won instead?
Was the pattern consistent—or was it noise?
Does that customer actually matter to your business?
Are you genuinely a strong fit?
What appears to influence the decision?
Is any of it controllable?

And ultimately:

What would have to become true for the decision to change?

That's the problem Haenisch is built to investigate.

Foundational evidence 06

A Clearer Way to Study AI Recommendations.

These foundational pages define the problem, the method, and the limits of what an observation can establish.

What Is Recommendation Intelligence?The decision-analysis layer beneath GEO and AI visibility.

MethodologyHow Haenisch measures, diagnoses, tests, and learns.

AI Visibility vs AI RecommendationWhy being mentioned is not the same as being selected.

Why One ChatGPT Answer Is Not a RankingWhy repeated, controlled observations matter.

Haenisch ResearchThe publication standards for future studies.

Our model 06

Technology Does the Heavy Lifting. Judgment Determines What Matters.

AI can process enormous amounts of information.

Software can run hundreds or thousands of observations.

Neither eliminates the need for judgment.

Haenisch combines:

Proprietary technology to collect, structure and measure recommendation behavior.

AI-driven analysis and execution to perform work that once required teams of analysts and marketers.

Experimental methodology to distinguish plausible explanations from what the evidence actually supports.

Human strategic judgment to determine what matters commercially, what is genuinely controllable and what should happen next.

The result isn't another dashboard your team has to learn how to interpret.
We run the intelligence system and surface the decisions worth making.

Experience 07

New Technology. A Decade of Customer-Acquisition Experience Behind It.

AI-mediated recommendation is new.

Understanding why customers choose one business over another is not.

Haenisch is built on approximately a decade of hands-on experience building and optimizing customer-acquisition systems—including funnels, paid advertising campaigns, launches, positioning, messaging and conversion strategy.

That experience spans local healthcare, online coaching and consulting, digital education and other service businesses, and has contributed to millions of dollars in client revenue.

Selected engagements have included:

Supporting Yes to Coaching’s growth to approximately $1.5 million in revenue.
Contributing to the growth of a Netherlands-based business that reached approximately $500,000 in monthly revenue.
Supporting Salesgirls challenge-based launches that generated approximately $300,000 to $500,000 in revenue.
Building funnels, launches, advertising campaigns, positioning, messaging and broader customer-acquisition systems for entrepreneurs, established businesses and local practices.

But the fundamental business question has remained the same:

Why does one customer choose one business instead of another?

AI is now becoming part of that decision.

Haenisch exists to understand what happens when it does.

Built differently 08

Intelligence First. Implementation When It Matters.

Haenisch is not designed as a traditional marketing agency.

We don't begin with a predetermined solution.

We don't assume every business needs more content, more SEO, more reviews or another marketing retainer.

We begin with the decision.

What is happening?

Does it matter?

Why might it be happening?

What is actually controllable?

What should be tested?

When implementation is justified, Haenisch can help engineer a defined intervention using technology, AI, structured systems and specialized execution.

Then we measure again.

The objective isn't more marketing activity.

It's better decisions—and measurable learning about what actually changes AI recommendation behavior.

Founding program 09

We're Opening Haenisch to a Limited Number of Founding Businesses.

We are currently commercializing our Recommendation Intelligence methodology with selected businesses in high-value markets.

A founding analysis begins with the customers you actually want to acquire.

We then examine how leading AI systems treat your business across those buying decisions, including:

Where you're being recommended.
Where competitors are winning instead.
How consistent those recommendations actually are.
Which competitors repeatedly appear.
Where a meaningful opportunity may exist.

And—equally important—

Which apparent gaps aren't worth your attention.

Request a Founding AI Recommendation Analysis ↘

Final CTA 10

Which Customers Is AI Sending You?

And which ones is it sending to your competitors?

Understand how AI recommendation systems treat your business—and identify the decisions actually worth investigating.

Limited founding-business availability.

Request an Analysis