Precision starts with context.
One operating graph per account, built from the ad platforms, analytics, and CRM it already runs on.
The context decides the answer long before anyone asks.
A site release breaks a GA4 conversion. A page that ranks in Search Console gets described wrong in ChatGPT. All of it renders fine in a report. All of it becomes fact the moment an agent reads it.
Most AI products are assembled from prompts, YAML workflows, MCP servers, and API orchestration. They move data between steps and forget it. None of them knows the Shopify customer and the HubSpot contact are the same person.
ACE resolves every business object into one typed knowledge graph. Next week a workflow reads the same node it read today.
One standard, from the ad account to the CRM
A launch that fails a check does not go live. Every override is logged.
Deterministic · AuditableAnswers no single system holds
A drop in Shopify revenue is read against Google Ads spend, HubSpot pipeline, Search Console demand, and brand citations in AI Overviews before anything is called a cause.
LLM-assisted · Reasoned over your dataBounded workflows, run against the operating graph
Agents trace a drop in qualified leads back to the tracking change behind it, and escalate what they cannot resolve.
Deterministic · Auditable · ReversibleTwenty systems, one graph.
- Paid platformsAds · Meta · TikTok
- Analytics & searchGA4 · GSC · GTM
- CRM & commerceHubSpot · Shopify
- WarehouseBigQuery · Looker
- AI answer surfacesChatGPT · Perplexity
- Validations
- Recommendations
- Reports
- AI agents
Seven modules, in the order they run.
- Campaign Blueprint
- Tracking
- QA
- Paid Media
- SEO/GEO
- Reporting
- AI Agents
Campaign Blueprint Engine
Tracking Readiness Engine
Pre-Launch QA
Paid Media Control Plane
SEO / GEO / AIO Intelligence
Client Reporting
AI Agents
Run a sample Adeqo audit.
A snapshot of your site’s search structure, GEO readiness, and tracking, from the checks Adeqo runs on managed accounts.