Findem Studio’s talent graph is the data layer underneath the agents the platform is built to run. It stores people, companies and time as connected, labeled records rather than as flat profiles scraped off the web, which means an agent reasoning about a candidate is working from information that has been resolved and checked rather […]
Findem Studio is people intelligence, built for AI. It is designed to turn that intelligence into finished people work you can trust: a succession plan, a role calibration, a completed hiring manager intake, produced and evidence-backed rather than handed to you as raw material to assemble yourself. For staffing firms and RPOs, that distinction matters […]
An AI recruiting agent is given a task and carries it through to finished work. AI recruiting software gives you better tools to produce that work yourself. That distinction, finished work against better tools, is the real line between the two categories, and it matters more than most vendor pages let on. Recruiting technology has […]
Findem Studio is people intelligence, built for AI, from Findem, the parent company behind Glider. If you are a recruiter, a staffing leader or a hiring manager wondering what Studio actually is, whether it touches the Glider tools you already use, and what you can realistically expect to use now, this FAQ answers all of […]
Yes. Your recruiting tools can increasingly talk to each other through MCP, short for Model Context Protocol, and in practice that means you can ask an AI assistant you already use to pull finished work out of a recruiting tool without opening that tool’s own screen. Instead of logging into three or four systems to […]
You can trust AI to help make hiring decisions, but only the way you would trust a very fast, very well-read colleague who has never met the candidate. The AI can read a resume, an interview transcript or a coding submission and produce a confident, well-organised answer in seconds. What it cannot do on its […]
Build vs buy AI recruiting tools comes down to one tradeoff. Building your own gives you control over exactly how a task gets done, but you still have to solve the intelligence underneath it and the checks on top of it yourself, and that work is usually harder and slower than the automation itself. Buying […]
AI candidate scoring accuracy depends far less on which model a vendor uses and far more on how the people data behind that score was labeled in the first place. A model can only reason about what it can actually understand, and resumes, assessment results and interview notes rarely arrive in a form a model […]
Before you trust an AI recruiting tool with work that reaches a hiring decision, check three things: whether it has the right intelligence before it starts, whether it follows the right method while it works, and whether it runs the right checks before anyone acts. A tool that fails any one of those can still […]
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 […]
AI agents in recruiting are systems that complete a piece of hiring work end to end and hand back the finished artifact, a sourced slate, a completed intake, a succession plan, rather than returning data a recruiter still has to interpret and assemble. That is the practical shift underway now, and it changes what a […]
