Guides
AI models organize the world using entities, not keywords. This pillar addresses how to clearly define your brand, product, and founders as distinct entities that AI can map. Guides cover 'How to build entity signals for AI search,' 'Using schema markup for entity recognition,' and 'Strengthening your brand's presence in the AI knowledge graph' as the foundation of search visibility in 2026.
This guide provides a technical blueprint for building a production-ready entity recognition pipeline. It covers data ingestion, model selection (comparing spaCy, NLTK, and transformer-based models like BERT), entity linking to knowledge bases like Wikidata, and outputting structured data for AI search engines. You'll learn to design for scalability and integrate with tools like LangChain and LlamaIndex for downstream RAG applications.
This guide teaches technical leaders how to identify and define the fundamental entities that represent their brand in the AI knowledge graph. It covers entity typology (Product, Person, Organization, Concept), creating canonical profiles with unique identifiers, and aligning definitions with public ontologies like Schema.org. This foundational step ensures AI systems can accurately map and relate your brand's core components.
This guide details the process of deduplicating and merging entity records from disparate internal and external sources. It explains deterministic and probabilistic matching algorithms, using tools like Dedupe.io or building custom resolvers with Python. The focus is on creating a single source of truth for each entity, which is critical for accurate analytics and reliable AI agent decisions.
This guide explains how to design and build a knowledge graph that serves as a reasoning backend for autonomous AI agents. It covers graph database selection (Neo4j vs. Amazon Neptune), defining relationship ontologies, and implementing a query layer using GraphQL or Cypher. You'll learn how to structure the graph to support multi-hop reasoning and context retrieval for agentic systems.
This guide demonstrates how to use JSON-LD, Microdata, and RDFa to embed machine-readable entity information directly into web content. It provides actionable code examples for implementing Schema.org markup for products, people, and organizations. This process directly signals entity properties and relationships to search engines and LLMs, building authoritative signals for AI search visibility.
This guide walks through automating the process of augmenting internal entity profiles with external data. It covers sourcing from APIs like Google Knowledge Graph, Wikidata, and Crunchbase, handling rate limits, and merging new attributes (e.g., funding rounds, executive changes) into your master entity record. This enriches context for AI systems without manual research.
This guide explains how to power personalized content feeds using a knowledge graph of user and content entities. It covers building user-entity affinity models, graph-based recommendation algorithms (e.g., personalized PageRank), and integrating the system into a web application. This moves beyond collaborative filtering to semantically-aware recommendations.
This guide addresses the operational challenge of entities changing over time. It provides a system design for monitoring key entity attributes, detecting statistically significant drift using tools like Evidently AI, and triggering automated or manual review workflows. This ensures your knowledge graph remains accurate and reliable for autonomous AI agents.
This guide teaches how to create vector representations of entities that capture their semantic meaning for use in similarity search. It compares techniques like TransE for knowledge graphs and sentence transformers for entity descriptions, with implementation examples using libraries like PyTorch and FAISS. These embeddings enable finding related entities beyond literal keyword matches.
This guide provides a deep dive into creating a system that matches messy, real-world text mentions (e.g., from news articles or social media) to canonical entities in your knowledge graph. It covers building training data, fine-tuning transformer models for entity linking, and evaluating precision/recall. This is a core component for building entity signals from unstructured text.
This guide outlines the policies and technical controls needed to manage entity data used by autonomous AI agents. It covers defining ownership, change approval workflows, access control at the entity level, and audit logging. This framework is critical for maintaining data quality and security in complex, agent-driven environments.
This guide details how to build a system that monitors where and how your brand entities are cited by AI search engines like ChatGPT and Gemini. It covers using search APIs, parsing LLM responses for entity mentions, flagging inaccuracies, and deploying corrective structured data. This proactive approach protects your brand's factual representation in AI-generated answers.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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