Explainability
↑ higher betterAbility to surface why a model made a recommendation.
key · explainability
Supply Chain Planning · interactive
Dimension Explorer
Pick any two of 10 dimensions. Vendor position reflects evidence-weighted scoring; halo radius reflects confidence.
Scenario simulation
Set your buyer context. With simulation on, vendor positions shift in real time to reflect how scores read against your industry, region, and implementation profile.
Industry
Region
IT depth
Priority
X axis
Y axis
low · AI SophisticationAI Sophistication · high
Enterprise Readiness →
Legend & glossary
How to read the map
The Domain Map is not a quadrant verdict. Every position is the output of an evidence-weighted score on the two dimensions you choose. Use the keys below to read it like an analyst would.
The dot
Each dot is one vendor. Position = (X dimension score, Y dimension score). Amber dots are AI-native; blue dots are incumbents or AI-augmented vendors.
The halo
The faint halo is confidence. A tight halo means many corroborating evidence sources; a wide halo means the score is real but should be read as approximate.
The axes
Both axes run 0–100. Scores are normalised per dimension so a 70 on AI sophistication means the same percentile as a 70 on enterprise readiness. Quadrant labels (Leaders / Visionaries / etc.) are reference only — they shift every time you change axes.
01
Evidence ingestion
Public docs, customer references, product trials, filings, repo activity, hiring signals, and structured vendor submissions are continuously ingested into a per-vendor evidence graph.
02
Dimension scoring
An LLM scoring pipeline converts evidence into a 0–100 score per dimension, with a rationale and an evidence count. Higher evidence volume + agreement = higher confidence (0–100).
03
Buyer-context weighting
On the map, position is raw. In Buyer query and the Explain panel, scores are reweighted by your context (priority, IT depth, segment) so the same data answers your question.
Formula (weighted fit): Σ (score/100 × weight × confidence/100) ÷ Σ weight × 100. Click any vendor on the map to see this calculation expanded for that vendor.
AI-native pillar
Depth of native AI: model quality, explainability, automation.
3 dimensions
Automation Depth
↑ higher betterHow much of the planning loop runs without human intervention.
key · automation_depth
AI Sophistication
↑ higher betterDepth of ML/AI in forecasting, optimization, and autonomous decisioning.
key · ai_sophistication
Business pillar
Commercial maturity, ecosystem, momentum, pricing.
2 dimensions
Ecosystem Maturity
↑ higher betterPartner network, marketplace, integration breadth.
key · ecosystem_maturity
Innovation Velocity
↑ higher betterPace of meaningful product release and roadmap execution.
key · innovation_velocity
Operational pillar
Implementation, time-to-value, customer success, support.
3 dimensions
Cost Efficiency
↑ higher betterTCO relative to delivered value for mid-to-large enterprises.
key · cost_efficiency
Time to Value
↑ higher betterHow quickly an average enterprise gets a working deployment.
key · time_to_value
Implementation Ease
↑ higher betterInverse of implementation complexity and required internal expertise.
key · implementation_ease
Technology pillar
Product architecture, depth, and engineering quality.
2 dimensions
Architecture Quality
↑ higher betterModern composable architecture, API maturity, scalability.
key · architecture_quality
Enterprise Readiness
↑ higher betterSecurity, observability, governance, and operational maturity.
key · enterprise_readiness