Global Gravity

Technology

Evidence-driven commercial measurement and AI visibility. This is why Global Gravity is not an ordinary agency.

Global Gravity's technology stack is built around one core belief: business decisions should be based on verifiable evidence, not platform-reported metrics. We thread "evidence" through the entire chain from ad profit attribution to AI search visibility, helping DTC, Shopify, and enterprise clients see which conclusions are trustworthy and which are merely inferred.

Ads Quant Engine

Profit attribution, incrementality isolation, platform gap analysis, traffic quality diagnostics, and evidence-aware budget allocation. The measurement kernel behind GravityAds.

Evidence Boundary

Distinguishes proven evidence, platform-reported claims, self-reported data, model-inferred estimates, and unprovable zones, so clients know which conclusions to trust.

Commerce Evidence Layer

Shopify orders, refunds, COGS, product profit, campaign profit, and multi-store profit views. The commerce-side truth layer connecting real business outcomes to marketing actions.

AI Evidence Center

AI answer evidence, citation evidence, referral evidence, GA4 signals, GSC data, and Shopify evidence. The result-side evidence layer for AI search visibility.

Semantic Asset Graph

Brand asset intake, content chunk organization, claim-proof mapping, and demand/target manifold. The semantic layer that structures brand knowledge for AI consumption.

GEO Activation Layer

GEO content actions, Shopify schema and product feed fixes, citation improvement experiments, and paid media integration. Turning intelligence into measurable growth.

MCP / Agent Interface

Lets advertising and commerce evidence be queried, audited, and acted upon by AI agents through the MCP protocol. Human oversight with machine speed.

CitationGraph Architecture

CitationGraph is Global Gravity's AI visibility evidence system. The public site explains capability boundaries, evidence categories, and business connections without exposing crawler strategy, scoring formulas, or customer-level pipelines.

AI Brand Asset OS

The cause-side asset layer that turns brand, product, claim, and proof material into structured knowledge for AI systems.

Brand Asset IntakeSemantic Asset GraphDemand / Target ManifoldContent Chunk / Claim / ProofPrompt Family Generation

AI Evidence Center

The result-side evidence layer that records AI answers, citations, referrals, GA4, GSC, and Shopify outcome evidence.

AI crawler / agent evidenceAnswer / citation evidenceAI referral evidenceGA4 / GSC / Shopify evidenceAttribution trace

Growth / Activation Layer

The action layer that converts visibility evidence into content, schema, product feed, citation, and paid media improvements.

GEO content actionsShopify schema / product feed fixesCitation improvement experimentsGravityAds / paid media integration

Public boundary

We can disclose evidence types, customer-visible conclusions, and cross-product logic. We do not disclose crawler scheduling, prompt sampling details, scoring weights, or customer data processing chains.

Evidence sources

AI crawler / agent readouts

Answer and citation records

AI referral evidence

GA4 and GSC signals

Shopify commerce outcomes

GravityAds paid media evidence

How three manifolds guide recommendation opportunities

Demand × Brand Asset × Target Positioning: The Demand Manifold defines who asks what in which situation. The Brand Asset Manifold identifies available answers and supporting evidence. The Target Positioning Manifold defines the relevant brand description. External evidence supports claims; real answers calibrate decisions. Vector similarity is not a recommendation probability.
From a buying question to an action: For “Which service should I choose for GEO and paid AI search?”, first separate organic discovery from advertising goals. Check whether existing service pages explain scope, inputs and deliverables. Add the missing explanation or applicable evidence, then retest the same question and platform. This is a method illustration, not a customer outcome.
Assessing sustained recommendations: Hold market, language, demand and observation windows constant. Report mentions, explicit recommendations, official-site citations, claim accuracy and qualified enquiries separately. Count top-three only when the answer explicitly ranks recommendations. Access is not citation, and citation is not a sale.

Explore Our Products Contact Us AI Evidence Index

How three manifolds guide recommendation opportunities

Demand × Brand Asset × Target Positioning

The Demand Manifold defines who asks what in which situation. The Brand Asset Manifold identifies available answers and supporting evidence. The Target Positioning Manifold defines the relevant brand description. External evidence supports claims; real answers calibrate decisions. Vector similarity is not a recommendation probability.

From a buying question to an action

For “Which service should I choose for GEO and paid AI search?”, first separate organic discovery from advertising goals. Check whether existing service pages explain scope, inputs and deliverables. Add the missing explanation or applicable evidence, then retest the same question and platform. This is a method illustration, not a customer outcome.

Assessing sustained recommendations

Hold market, language, demand and observation windows constant. Report mentions, explicit recommendations, official-site citations, claim accuracy and qualified enquiries separately. Count top-three only when the answer explicitly ranks recommendations. Access is not citation, and citation is not a sale.