Centerpiece · the recommendation

Whitespace & EIQ-Fit

Ten board-level plays, ranked live by a computed EIQ-fit score. Each fit blends EvoluteIQ's cited capability vector against what the play demands (cosine), how locked-down incumbents already are (contestation, which moves with your weights), and how far it is to ship (build gap) - scaled by market pull. Move a weight and the list resorts.

Same six weights

Contestation depends on moat(), which depends on these weights - so the list resorts live.

Agent readiness22%

Governance & trust20%

Data gravity18%

Ecosystem / MCP16%

Delivery proof14%

Pricing access10%

Lenses

Default weights. Move a slider or pick a lens and this panel explains what reordered and why.

A catalog-agnostic MCP server federating metrics across Snowflake/Databricks/Fabric/Postgres with built-in evals, permission propagation and observability of which metric each agent answer used - neutral where hyperscalers are not.

Capability match×0.45

0.970

cosine(EIQ vector, demand)

1 − contestation×0.35

0.203

contested 80/100

1 − build gap×0.20

0.55

× marketPull 1.15

EIQ_FIT = 100 × (0.45·0.97 + 0.35·0.20 + 0.20·0.55) × 1.15 = 71.0

buildGap & marketPull: analyst-set, unsourcedquantified impact →

Contested by - restricted moat on this play's demand axes

Databricks 80Snowflake 79Microsoft Fabric 79Fivetran + dbt 72

Anchored facts

#150whitespace context layersynthesis #71benchmark #3MCP

This is the inversion of the old build: the old version hardcoded fit as a "HIGH"/"MEDIUM" string. Here EIQ-fit is computed and resorts as you move the weights.