Our Methodology
Transparency is core to vzimAI. Here is exactly how our recommendations work — no black boxes, no AI guesswork on product facts.
Recommendation Principles
- Deterministic first: Product scoring uses fixed rules, not AI inference. Given the same inputs, you get the same outputs every time.
- Structured data owns truth: All product facts live in structured data files. AI is only used to phrase explanations in natural language — never to invent product capabilities.
- Inspectable scores: Every recommendation shows which dimensions contributed to the ranking so you can understand why a product was chosen.
- Hard exclusions first: Products that clearly don't fit your constraints (too large, too complex) are removed before scoring begins.
Scoring Dimensions
Each product is scored across these dimensions, with weights reflecting what matters most for a good recommendation:
Hard Exclusions
These are non-negotiable filters. If a product fails any of these, it is not recommended regardless of its score on other dimensions:
space too small
Not suitable for very limited spaces.
setup too complex
Setup complexity exceeds your stated tolerance.
maintenance too high
Maintenance requirements exceed your stated tolerance.
too advanced for beginner
May be too advanced for a beginner-focused buyer.
Data Freshness
Every product in our catalog has a last_reviewed_at date. We regularly review product data for accuracy. If you notice outdated information, please contact us.
Affiliate Disclosure
Some links on vzimAI are affiliate links. If you click through and make a purchase, we may earn a commission at no extra cost to you. This does not influence our recommendations — our scoring is deterministic and independent of merchant relationships. Full disclosure.