Entity and knowledge architecture
Audit how your organization, people, products and services are described. Build a source of truth and map the relationships that matter to customers.
AI systems × structured knowledge
Neuro-symbolic AI.
Marketing with a map.
Connect the flexibility of AI with a clear model of your brand, its expertise and the markets it serves.
Predrag Petrović
SEO · GEO · Entity strategy
Belgrade, Serbia
SEO · GEO · Entity strategy
Belgrade, Serbia
01 / The idea
What is neuro-symbolic AI?
Neuro-symbolic AI brings together two approaches: neural models that learn patterns from data, and symbolic methods that represent knowledge through concepts, relationships and rules. In research, those parts can be integrated in different ways to support learning and reasoning.
For marketing, the useful lesson is practical. AI can help interpret questions, language and content at scale; a deliberate knowledge model can define what is true about the business, how its entities relate and which claims require evidence. This page describes a marketing approach informed by neuro-symbolic thinking, not a claim that every campaign runs on a custom neuro-symbolic model.
01 / Neural
Patterns in language
Use AI-assisted analysis to surface audience questions, topic clusters, content gaps and variations across markets.
02 / Symbolic
Explicit brand knowledge
Document people, services, locations, proof, terminology and the relationships between them in a consistent structure.
A coherent digital presenceTurn the two inputs into useful pages, connected facts and measurable search experiences.
02 / Services
Neuro-Symbolic AI Marketing Services
A strategy and implementation engagement for organizations that need their expertise to be legible across search, AI-assisted discovery and multiple languages.
Content and answer design
Turn real customer questions into clear, evidence-backed pages. Connect each answer to the right service, expert, location and supporting source.
Technical and structured signals
Align site architecture, internal links, metadata and appropriate structured data with the visible content and the underlying entity model.
AI-assisted market research
Analyze language patterns, search demand and competitor themes, then check findings against first-party knowledge and human review.
Multilingual consistency
Adapt terminology and content to each market while keeping names, services, proof points and relationships consistent across languages.
Measurement and iteration
Track search visibility, qualified visits, inquiries and content coverage. Review AI answer appearances as directional evidence where measurement is available.
03 / The work
From fragmented facts to a useful system.
The first deliverable is a clear picture of what the business says, what its audience asks, and where those two fail to meet. From there, we build and test the smallest set of improvements that can make a measurable difference.
01
Discover
Review the site, search data, audience questions and existing brand facts.
02
Model
Define the core entities, relationships, claims and priority journeys.
03
Publish
Improve pages, navigation and structured signals, with editorial review.
04
Learn
Measure outcomes and refine the model and content as markets change.
Structured data and entity clarity help express information consistently; they do not guarantee rankings, inclusion in AI answers or citations.
04 / Work together