Haenisch helps businesses understand where AI systems recommend them, where competitors win instead, why those outcomes appear under tested conditions, and which decisions may be worth changing.
The decision beneath the answer
People increasingly ask ChatGPT, Gemini, and other AI systems to compare options and recommend businesses, products, and providers. These answers can influence who enters a buyer's consideration set and who is favored for a particular need.
Haenisch studies that decision process through a practical analytical chain:
- Decision
- Candidates
- Beliefs
- Evidence
- Recommendation
The goal is not to manufacture favorable answers. It is to build legitimate evidence, measure recommendation behavior carefully, identify commercially meaningful gaps, and test defined interventions only when the evidence justifies them.
Deeper than AI visibility
GEO, Generative Engine Optimization, AEO, and AI visibility are useful market terms. They help describe whether a brand is surfaced, mentioned, cited, or represented in AI-generated answers.
Haenisch connects to that market without stopping at the visibility layer. Recommendation Intelligence asks whether a business was actually selected or favored for the buyer decision, who won instead, how stable the pattern is, and what evidence may be decision-relevant. Recommendation Engineering is the disciplined work of testing a defined change and measuring again.
Commercial progression
AI Visibility → Recommendation Intelligence → Recommendation Engineering → Measured Recommendation Outcomes
Haenisch is not a traditional marketing agency and is not designed as another generic reporting dashboard. The company is being built around repeatable methodology, structured evidence, technology-assisted execution, conservative claims, and defined decision gates.
Founded by Ausi Kimmons
Ausi Kimmons founded the company after approximately a decade working in customer acquisition, including positioning, messaging, funnels, paid acquisition, launches, and conversion strategy across service businesses and digital markets.
Important evidence boundary: that prior customer-acquisition experience informs Haenisch's commercial perspective. Historical marketing or customer-acquisition outcomes are not Haenisch Recommendation Intelligence results and are not presented as validation of the methodology.
The company's present work is to systematize how commercially important AI recommendations are measured, diagnosed, prioritized, tested, and learned from.
How the company is being built
- Evidence before claims. Observations, hypotheses, and causal conclusions are kept distinct.
- Business value before activity. Not every visibility gap or recommendation loss is worth pursuing.
- Defined interventions. Implementation should test a specific hypothesis rather than become indefinite marketing activity.
- Remeasurement. A change is not assumed to work; recommendation behavior must be measured again.
- Systematized delivery. Methodology, automation, and structured judgment should reduce founder dependence and unnecessary client burden.
For company or research inquiries, contact ausi@haenischintelligence.com.