MCP for recruiting is the use of the Model Context Protocol, an open standard published at modelcontextprotocol.io, to let an AI assistant call your recruiting tools directly and get a structured answer back. Instead of exporting a list from your ATS and pasting it into a chat window, the assistant asks the tool and the […]
You can trust AI to help make hiring decisions when the result it produces is reviewable: you can see the evidence behind it, trace which inputs drove it, challenge any part of it, and produce a record of all three six months later. Trust is a property of what the model hands to the person […]
Build vs buy AI recruiting tools comes down to one question: is the thing you want specific to your business, or is it a general recruiting workflow that someone already sells? Build when the method is yours and you can staff it for years. Buy when the method is common and the data is the […]
AI candidate scoring accuracy is set by how candidate data was labeled before the model ever saw it. Send the same resume through the same model twice, once as raw text with a job title and once with labeled scope, progression and company context, and the two runs disagree about whether the person can do […]
You can trust an AI recruiting tool when you can inspect three things: the data it held before it started, the method it followed while it worked, and the checks it ran before it handed you a result. Everything else in a vendor demo is decoration. This page gives you the three checks, the literal […]
Candidate data is a record. People intelligence is labeled, connected understanding of a person. A resume, a test score and an interview transcript can all be true and still leave you no closer to knowing whether a candidate is right for the role, because none of them, on their own, tells you how those facts […]
MCP, short for Model Context Protocol, is an open standard that lets an AI tool connect to outside data and software, the same way a library card lets a person into a library. It gets the AI in the door. It does not tell you what is on the shelves or which book answers your […]
An AI agent in recruiting takes a goal, works out its own steps, and hands a recruiter finished work: a calibrated role definition, a completed phone screen, a booked interview, a verified identity. It differs from automation because nobody wrote the steps in advance, and from a chat assistant because it acts inside your systems […]
Global hiring fraud hotspots are the countries and regions where documented candidate fraud and identity verification risk run highest, based on named industry indices rather than assumption. In 2026, that data points consistently in one direction: fraud vulnerability is concentrated in markets with weaker centralized identity infrastructure and fast growing digital hiring volume, led by […]
Second device cheating is when a candidate uses a phone, tablet, or second monitor outside the webcam’s view during a proctored online assessment to look up answers, message someone for help, or mirror their screen to a helper. It is one of the hardest forms of assessment fraud to stop with software alone, because the […]
A synthetic candidate is a job applicant who does not exist as a single real person at all. The persona is assembled, usually by combining stolen or purchased personally identifiable information (PII) with an AI generated resume, an AI generated or composite photo, and an invented work history, into a package that looks, on paper […]
Take home assignment outsourcing happens when a candidate has someone else, a freelancer, a friend, or a paid ghost writing service, complete their take home coding, design, or writing test for them. Because the assignment is done alone, on the candidate’s own schedule, and away from any interviewer or proctor, nothing about the format itself […]
