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.
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.
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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