Methodology
No black box. Every score shows its work.
Recommendation engines in this industry are usually pay-to-rank. Ours is a deterministic scoring model: the same answers always produce the same scores, every number traces back to specific answers you gave, and no vendor pays for placement. AI writes narrative — it never computes a score.
System fit scoring
Each system is scored 0–100 against your profile, weighted like this:
Industry match
Is your industry one this system demonstrably serves well?
Company size
Does your revenue band sit inside the system's proven sweet spot? Being far outside it overrides everything else — a system that's too small stays 'likely too small' no matter how well it scores elsewhere.
Complexity alignment
Your operational complexity (entities, currencies, operations) vs. the complexity range the system is built for — too much system is a real failure mode, not a safe default.
Capability coverage
Of the capabilities you actually need (from your function checklist and answers), how many does the system cover natively? Gaps are shown, not hidden.
Your priorities
Stated preferences — cost sensitivity, Microsoft-stack affinity, industry depth, scalability headroom — nudge but never dominate.
Why you may see fewer questions than someone else
Not every question applies to every company, so the questionnaire prunes itself. Each conditional question carries a versioned relevance rule that decides, from your earlier answers, whether it gets asked at all. A pure services firm never sees the warehouse management question, because that rule looks for inventory signals: an inventory or manufacturing selection on the needs checklist, a physical-goods industry, or a WMS in your current systems. A distributor sees the full physical-goods path.
Skipped questions are not guessed at. They are scored exactly as if you had answered no, and your report lists every question we skipped along with the rule that skipped it. The rules are data, not judgment calls made at scoring time. Same answers, same questions, same scores.
Readiness scoring
The readiness track scores the factors that decide whether implementations succeed — executive sponsorship, documented requirements, budget clarity, data quality, and change risk — alongside implementation complexity, data migration risk, and integration scope. Each is a 0–100 score built from explicit rules, so you can see exactly what would move it.
Who maintains this
The scoring model, system research, and pricing anchors are built and maintained by Brady Justice, an independent ERP consultant. The site is funded by his advisory practice, not by vendors, and the about page spells out the business model and how vendors can dispute a factual claim.
What we don't do
- ▪No vendor pays to appear, rank, or be recommended.
- ▪No single "the answer" — output is always a tiered shortlist with reasoning.
- ▪No score is generated by an LLM — AI only narrates results the scoring engine computed.
See it on your own company:
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