# AI and People Intelligence Glossary | Findem Findem publishes a public search API over its indexed pages; the guide is at https://www.findem.ai/lazyweb/agent-access. Source: https://www.findem.ai/glossary Source observed: 2026-11-02T22:04:39.014Z Last checked: 2026-11-02T22:04:39.014+00:00 Indexed: 2026-11-02T22:13:08.505+00:00 --- # AI and people intelligence terms to know We explain the AI, data, and people-intelligence concepts that shape modern people-centric work, including foundational definitions, workflow applications, and the signals and platform components that power Findem. ![Smiling woman with short hair sitting indoors near a plant and yellow graphic lines on the right.](https://cdn.ploy.ai/6b6bb914-a578-4dfa-aefb-ba937ed4abcf/user/462bd6a7-glossary-original-hero-lossless.webp) ## Glossary categories The glossary includes **AI & Data Foundations**, **Talent Workflows & Use Cases**, and **Findem Platform & Agentic AI**. The supplied content provides the AI & Data Foundations terms below. ## AI & Data Foundations ### Artificial Intelligence (AI) AI refers to systems that perform tasks requiring human-like reasoning, pattern recognition, or decision-making. In talent workflows, AI interprets data at scale to support sourcing, screening, analytics, and communication. ### Machine learning (ML) ML is a subset of AI that uses algorithms to identify patterns and make predictions from data. It powers tasks such as candidate filtering, ranking, and classification. ### Deep learning Deep learning is a type of ML that uses neural networks to process unstructured or high-volume datasets, including resumes, profiles, and company information. ### Generative AI (GenAI) GenAI creates new content—such as outreach messages, summaries, and job descriptions—by learning from large datasets of text and code. ### Large language models (LLMs) LLMs are AI models trained to understand and generate human language. In talent workflows, they help interpret recruiter intent, answer questions, and power conversational interfaces. ### Hyperautomation Hyperautomation combines AI, ML, and automation tools to streamline multi-step workflows. In talent acquisition, it reduces manual work across sourcing and engagement. ### Business intelligence (BI) BI tools gather, store, and analyze data for reporting and decision support. BI complements AI by surfacing funnel metrics, performance signals, and workforce trends in readable form. ### Person data sources A person’s professional footprint—roles, achievements, contributions, certifications, and digital profiles—is used to generate structured talent data. ### Company data sources A company’s digital footprint—funding events, team growth, markets served, and leadership structure—provides context for interpreting a person’s experience. ### 3D People Graph Our 3D People Graph is a structured intelligence foundation containing trillions of expert-labeled data points across individuals, companies, and time. It connects who someone is, where they worked, how their career evolved, and how they are connected to others, giving AI context to reason over people data rather than simply search it. ### 3D data (Person × Company × Time) 3D data is the model underlying the 3D People Graph. It structures career history across three intersecting dimensions—person, company, and time—so the system understands experience in context, not only the titles a candidate held. ### 3D candidate profiles These profiles are generated from 3D data and provide an integrated view of a person’s background, achievements, and career trajectory. ### Attributes Attributes are verifiable facts derived from 3D data, such as scope increases, technical depth, tenure at a particular company stage, and industry specialization. We use them to assess fit with precision. ### Findem Magics Findem Magics are our library of **300,000+** proprietary, searchable attributes. They translate difficult-to-articulate career qualities—such as impact, leadership style, strategic thinking, and retention risk—into concrete, data-backed search criteria. Magics connect what a recruiter knows they want with what a keyword search can find. ### AI Labeling Engine Our AI Labeling Engine transforms raw person and company data into structured 3D data, identifies attributes, and produces Success Signals and Relationship Signals. It combines machine-scale processing with expert human review, so the intelligence behind Findem recommendations is labeled, structured, and verifiable rather than generated at the moment of use. ### Success Signals Success Signals are verified, expert-labeled patterns indicating which experiences and career markers predict success for a role, team, or environment. They are structured and explainable rather than black-box scores. We attach roughly **75–100 Success Signals** to a given candidate profile. ### Relationship Signals Relationship Signals represent how people are connected inside and outside an organization through shared work history, alumni networks, referral paths, employee connections, and permissioned network relationships. Warm paths identified by Relationship Signals convert **2–8×** more than cold outreach. ### Domain-specific AI for talent Domain-specific AI for talent uses talent-specific data and expert-labeled signals rather than only general-purpose language models. Because these models are trained on role expectations, career patterns, and organizational context, their recommendations are grounded in how hiring works, not only in how language works. ### People Intelligence Platform A People Intelligence Platform turns fragmented people data into context that teams and AI can reason over and act on across hiring, executive search, internal mobility, learning and development, and workforce planning. Our intelligence is labeled, structured, and verifiable rather than generated from general-purpose models. ### Insights powered by Findem These insights are analyses derived from 3D data, Success Signals, and Relationship Signals. They support workforce planning, hiring decisions, internal mobility, and talent strategy, using structured and labeled data rather than raw search output. ## The Takeaway Most AI in talent tools is general-purpose: it is trained on language rather than careers. It can generate text and move tasks forward, but it does not understand what good looks like for a specific role, team, or company stage. Since 2025, almost every vendor has opened an MCP, making access to people data a baseline capability. We are built differently. Our 3D People Graph contains trillions of expert-labeled data points across people, companies, and time. Success Signals and Relationship Signals provide a structured view of how careers unfold and which patterns predict performance in a given context. Findem Studio brings this together with a named expert’s methodology and verification before anyone acts, producing finished work that teams can defend. That is how we distinguish access from outcomes and task automation from decisions teams can act on with confidence. ## Related Findem resources - [Our Platform](https://www.findem.ai/platform) - [Findem Studio](https://www.findem.ai/studio) - [Embedded AI](https://www.findem.ai/embedded-ai) - [Findem ReadySlate](https://www.findem.ai/platform/readyslate) - [Labeling Engine](https://www.findem.ai/platform) - [Why Findem](https://www.findem.ai/why-findem) - [Success Signals](https://www.findem.ai/why-findem/success-signals) - [Relationship Signals](https://www.findem.ai/why-findem/relationship-signals) - [3D Data](https://www.findem.ai/why-findem/3d-data) - [Responsible AI](https://www.findem.ai/why-findem/responsible-ai) - [Integrations](https://www.findem.ai/why-findem/integrations) - [Partnerships](https://www.findem.ai/partnerships) - [Blog](https://www.findem.ai/blog) - [Webinars](https://www.findem.ai/webinars) - [Guides](https://www.findem.ai/guides-and-infographics) - [Support Center](https://findem.zendesk.com/hc/en-us) - [About Us](https://www.findem.ai/about-us) - [Careers](https://www.findem.ai/careers) - [Customer Stories](https://www.findem.ai/customer-stories) - [Contact Us](https://www.findem.ai/contact-us) ## Contact and subscription - [Sign in](https://matches.findem.ai/auth/login) - [Request a demo](https://www.findem.ai/request-demo) - Subscribe to the shortlist with your email address. - Findem on LinkedIn © 2026, Findem. 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