SHEKARI · SERVICES
AI recommendation analysis for buyer decision questions
A buyer asking which option fits their needs is not simply requesting a list of brands. This service examines comparisons based on constraints such as budget, expertise and scope, then checks how accurately a brand is described. The goal is better decision-grade public information, not forcing an assistant to recommend a provider.
When is this service relevant?
Typical situations worth investigating before choosing the next step.
Responsibilities overlap with adjacent services. LLM Visibility tracks whether the brand appears at all; AI Recommendations examines selection-oriented responses.
The execution decision can be based on insufficient evidence. Review real decision criteria, neutral prompts, verifiable provider facts and observed comparison answers.
Findings may be overstated beyond the conditions of observation. Recommendations cannot be bought or guaranteed, and competitors are not invented for convenient comparisons.
Scope and potential deliverables
The final scope is agreed after reviewing the website; these are typical work areas.
A small buyer-scenario matrix with actual selection criteria, constraints and repeatable questions.
Dated observations distinguishing a listed brand from an explicit contextual recommendation.
An evidence register for stated reasons, exclusions and uncertain model claims.
A service-page messaging and proof improvement plan, with no promise that a model will recommend the brand.
How the engagement works
The steps are adjusted to the scope we agree on.
- 01
Confirm the audience and scope: Providers whose suitability depends on project scope, expertise, service model or buyer constraints.
- 02
Gather evidence and establish the baseline: Review real decision criteria, neutral prompts, verifiable provider facts and observed comparison answers.
- 03
Separate the assignment from neighboring services: LLM Visibility tracks whether the brand appears at all; AI Recommendations examines selection-oriented responses.
- 04
Prepare implementation tasks and deliverables: A buyer-scenario matrix, recommendation labels, factual issues and an evidence-improvement roadmap.
- 05
Review findings and monitor valid indicators: Accuracy of the stated strengths and limitations in fixed samples and relevant attributable enquiries, where available.
IN-DEPTH GUIDE
What is involved in AI recommendation audit?
Useful details to consider before deciding on this service.
Assess decision contexts, not purchase an AI recommendation. LLM Visibility tracks whether the brand appears at all; AI Recommendations examines selection-oriented responses. Recommendations cannot be bought or guaranteed, and competitors are not invented for convenient comparisons.
Use genuine buyer scenarios
Base prompts on real project size, region, expertise and delivery constraints. Mark leading prompts as a separate group.
Define the decision criteria
Specify scope, competence, engagement model and checkable evidence before reviewing answers; a list of names is not an objective ranking.
Differentiate listing and recommending
A model may name several vendors or explain suitability. Record explicit recommendations, neutral lists and omissions separately.
Verify model claims about providers
Check reported pricing, experience, certifications or capabilities against official evidence and correct documented errors.
Improve decision-ready service pages
Make scope, limitations, workflow and supported examples clear so buyers can evaluate the provider even without an AI answer.
Compare alternatives fairly
Use common criteria and verifiable public information. Fabricated criticism or misleading competitor claims are excluded.
Repeat under documented conditions
Keep tool, language, timing and prompts available for review. A small sample cannot support a permanent worldwide provider rank.
Deliver a buyer-evidence backlog
For each scenario, document the observed answer, factual gap, evidence to add and a realistic validation step.
START A CONVERSATION
Request an AI recommendation review
Choose realistic buyer scenarios to distinguish casual mentions from contextual recommendations.
FAQ
Frequently asked questions
A few useful details before we begin.
Which realistic buyer scenarios belong in an AI recommendation audit?
Base prompts on real project size, region, expertise and delivery constraints. Mark leading prompts as a separate group.
How are purchase criteria reflected in test questions?
Specify scope, competence, engagement model and checkable evidence before reviewing answers; a list of names is not an objective ranking.
Is appearing in an AI list equivalent to being recommended?
A model may name several vendors or explain suitability. Record explicit recommendations, neutral lists and omissions separately.
What happens if an AI answer invents a product feature or price?
Check reported pricing, experience, certifications or capabilities against official evidence and correct documented errors.
How do you keep competitor comparisons fair and factual?
Use common criteria and verifiable public information. Fabricated criticism or misleading competitor claims are excluded.
What actionable output comes from the AI recommendation review?
For each scenario, document the observed answer, factual gap, evidence to add and a realistic validation step.