Research Date: February 2026 Data Source: IndustryLabs database — 67 AI-native HR tools Last Updated: February 21, 2026
Findem is an AI-powered talent intelligence and sourcing platform that uses attribute-based people data — derived from aggregating and analysing hundreds of public data sources — to help recruiting teams find, qualify, and engage passive candidates with a precision that keyword or title-based search cannot achieve. Founded in 2019 and headquartered in San Francisco, California, Findem was built by engineers and data scientists who applied the kind of complex data modelling used in financial intelligence and fraud detection to the problem of talent data — creating what the company calls a "3D data model" that layers a person's work history, skills, and professional trajectory across time rather than capturing a single-moment snapshot.
What is Findem?
Quick Answer
Findem is an AI-powered talent sourcing and intelligence platform that uses attribute-based search across hundreds of aggregated data sources to identify passive candidates matching precise, multi-dimensional talent profiles. Its 3D data model tracks how people's skills and experience evolve over time — not just where they work now, but what trajectory they are on — enabling recruiting teams to find candidates that traditional keyword or title search consistently misses. Founded in 2019 in San Francisco, it primarily serves mid-market and enterprise companies with 200 or more employees. According to IndustryLabs analysis of 67 AI-native HR tools, Findem's time-dimensional people data model is a technically distinctive approach shared by very few platforms in the database.
Findem was founded by Hari Kolam (CEO) and Raghu Venkat (CTO), both with backgrounds in enterprise software and machine learning. The company's technical approach reflects that background: rather than building a search interface over a single aggregated profile database (the approach used by most sourcing platforms), Findem builds a dynamic, time-series model of professional careers — tracking when people acquired specific skills, how long they held particular roles, what career transitions they made, and what those patterns predict about their current profile and future trajectory.
This temporal modelling enables a distinctive sourcing capability. A recruiter can search not just for people who currently work at a list of target companies, but for people who have previously worked at those companies and have since moved to roles that suggest career advancement — identifying executives who were minted from particular training grounds rather than only identifying people currently employed by direct competitors. The platform's sourcing workflows, pipeline automation, and talent analytics build on this data foundation to create a sourcing intelligence layer that complements rather than duplicates existing ATS and LinkedIn Recruiter capabilities.
Who is Findem best for?
Quick Answer
Findem is best for talent acquisition teams at companies with 200 or more employees that are actively sourcing passive candidates for specialist, technical, or leadership roles where conventional sourcing — LinkedIn Recruiter plus job boards — consistently misses the best candidates because their capabilities are not visible from their current job title or employer. It is particularly strong for companies that value sourcing precision over volume: finding the right 20 candidates from a database of millions rather than generating a broad list and manually filtering.
Findem is not an inbound recruiting tool — it does not replace an ATS or career site. It is a sourcing intelligence platform designed to complement an existing recruiting stack. Teams whose primary hiring challenge is processing inbound application volume will not extract the platform's primary value from its sourcing capabilities.
The ideal Findem buyer is a senior talent acquisition leader, sourcing specialist, or Head of People Analytics at a technology, financial services, or professional services company that fills a significant proportion of its roles through proactive sourcing rather than inbound applications. The platform's attribute-based search is most valuable for roles where the candidate pool is both competitive and non-obvious — where the people who would be excellent in the role are not the people who respond to a LinkedIn InMail with "Senior Engineer at Meta" in their current title. According to IndustryLabs analysis of 67 AI-native HR tools, Findem occupies a distinct position in the database: it is one of the few platforms that applies temporal data modelling — how careers evolve — rather than static profile aggregation to the sourcing problem.
What are Findem's key features?
Quick Answer
Findem's key features include attribute-based talent search with a 3D data model tracking skills and career trajectory over time, AI-powered candidate matching that identifies qualified candidates across hundreds of aggregated data sources, pipeline automation for managing outreach sequences and candidate progression, talent analytics dashboards covering pipeline composition, diversity metrics, and sourcing effectiveness, and integrations with ATS platforms for direct candidate hand-off.
The attribute-based search interface is Findem's primary differentiator. Rather than building a search query from job title and skills keywords, recruiters define a talent profile using attributes — not "Senior Machine Learning Engineer at FAANG company" but "person who has built production ML models at scale, has experience leading small teams, has spent time at a research-oriented organisation, and has recently transitioned to a product-focused role." The AI maps this attribute profile against Findem's data model to identify people who match across dimensions that a keyword search cannot capture.
Pipeline automation allows recruiting teams to build systematic outreach to matched candidates: personalised email sequences with dynamic variable insertion, tracking of open rates and response rates, and workflow rules that advance candidates who respond positively into ATS hand-off processes. This reduces the manual effort involved in high-volume personalised sourcing outreach without reducing the personalisation quality that drives response rates in competitive talent markets.
Talent analytics provides sourcing teams with visibility into pipeline composition and health: how many candidates are in each stage of outreach, what the response rates are by outreach sequence and role type, what the demographic composition of the sourced pipeline is at each stage, and what sourcing channels and search strategies are producing the most qualified candidates for specific role profiles. These analytics are designed to enable recruiting teams to iterate on their sourcing strategy based on data rather than intuition. You can view Findem's full profile at /tools/findem.
How much does Findem cost?
Quick Answer
Findem uses annual subscription pricing based on the number of recruiter seats and modules included. Based on publicly available market data, annual contracts for mid-market deployments typically range from £25,000 to £100,000, with enterprise agreements for larger sourcing teams ranging higher depending on seat count and the scope of analytics and automation features included. Findem does not publish list pricing; buyers receive custom quotes after a discovery process.
Pricing is estimated based on public information and may not reflect current vendor pricing. Verify directly with the vendor.
Findem's pricing model is per-seat (recruiter and sourcing team users) rather than per-hire or per-volume. This means unit economics improve as the sourcing team scales its usage across multiple active requisitions and as the platform's pipeline automation features reduce the per-recruiter time investment in outreach management. Implementation is relatively lightweight compared to enterprise talent platforms — most organisations are operational within four to six weeks — as Findem's primary configuration involves connecting the platform to the ATS, configuring outreach templates, and establishing the analytics dashboards. According to IndustryLabs analysis of 67 AI-native HR tools, Findem falls in the £25–50K annual pricing band, consistent with other specialist sourcing intelligence platforms in the database.
What does Findem integrate with?
Quick Answer
Findem integrates natively with Greenhouse and Lever for ATS candidate export, with Workday for HRIS data context, with Slack for recruiter workflow notifications, and with email providers including Google Workspace and Microsoft 365 for outreach sequence management. An API supports additional integrations with ATS platforms not covered by native connectors.
Findem's integration architecture positions it as an additive sourcing intelligence layer on top of an existing ATS and HRIS stack. The Greenhouse integration — Findem's most frequently used ATS connection, reflecting its core technology company customer base — allows recruiters to push qualified candidates sourced through Findem directly into active Greenhouse requisitions without re-keying data. Candidate attribution (sourced via Findem) is preserved in the Greenhouse record for source quality reporting.
The Lever integration provides equivalent candidate hand-off for organisations using LeverTRM as their ATS and CRM. Workday integration provides HRIS employee context — allowing Findem's sourcing team to see which skills and roles are already represented in the existing workforce when building external talent profiles, informing a more targeted sourcing strategy. Email provider integrations (Google Workspace, Microsoft 365) power Findem's outreach sequencing directly from the recruiter's existing email infrastructure, which improves deliverability and maintains the recruiter's existing email identity for candidate communications. For context on the applicant tracking systems market and how Findem's sourcing data complements ATS-native sourcing features, the IndustryLabs category page provides verified data across competing platforms.
Is Findem GDPR / SOC 2 / ISO 27001 compliant?
Quick Answer
Findem claims GDPR compliance and SOC 2 Type II certification. For UK and EU employers, GDPR compliance is significant given that Findem's core function involves aggregating and processing personal data about individuals from public sources — a use case that requires careful legal basis analysis under GDPR, particularly for candidates who have not explicitly consented to being profiled or contacted.
Compliance certifications are vendor-supplied. IndustryLabs has not independently audited these claims. Verify current certification status directly with the vendor.
Findem's GDPR compliance posture shares the legal complexity common to all web-aggregated talent databases: the platform relies on legitimate interest as the lawful basis for processing candidate data collected from public professional sources, which requires a documented balancing test demonstrating that the recruiter's interest in sourcing is not overridden by the candidate's privacy rights. UK and EU buyers should request Findem's data processing agreement, review how the platform handles candidate opt-out and erasure requests across its aggregated data sources, and confirm the data retention period for candidate profiles and outreach histories. The SOC 2 Type II certification addresses enterprise security and data handling requirements — a standard procurement prerequisite for regulated-sector buyers — and covers security, availability, and confidentiality controls over an audit period rather than at a single point in time.
How does Findem compare to alternatives?
Quick Answer
Findem's closest alternatives in the AI talent sourcing space are SeekOut, Eightfold AI, and LinkedIn Recruiter. SeekOut shares the attribute-based people intelligence approach with a stronger emphasis on technical talent data layers (GitHub, publications). Eightfold AI provides a broader talent lifecycle platform beyond external sourcing. LinkedIn Recruiter has the largest single talent pool but lacks Findem's temporal data modelling and attribute-based precision. The sourcing intelligence market is competitive, and the choice among these tools often comes down to which data dimensions matter most for a given talent profile.
SeekOut is the most direct alternative to Findem in the AI talent sourcing space. Both use multi-source aggregation and attribute-based search to find passive candidates that LinkedIn Recruiter misses. SeekOut's key differentiator is its technical talent data layers — GitHub activity, patents, academic publications — which make it particularly strong for engineering, data science, and research recruiting. Findem's differentiator is its time-dimensional data model, which is particularly valuable for identifying candidates whose career trajectory signals readiness for a next-level role even before their LinkedIn profile reflects it. The choice between the two often depends on whether a team's hardest-to-fill roles are primarily technical (SeekOut) or broadly senior or specialist (Findem).
Eightfold AI competes with Findem in the AI talent intelligence space but serves a fundamentally different scope. Where Findem is a specialist external sourcing intelligence tool, Eightfold is a full talent lifecycle platform covering external sourcing alongside internal mobility, workforce planning, and people analytics. Organisations that need only sourcing intelligence will find Findem more precisely sized and more affordably priced. Those that need a unified talent intelligence layer across the full employee lifecycle will find Eightfold's breadth more relevant despite the higher complexity and cost.
LinkedIn Recruiter remains the default sourcing tool for most recruiting teams due to its scale — over 900 million profiles — and the universal familiarity of the platform among candidates. Findem's positioning against LinkedIn Recruiter is that when every recruiter in the market targets the same pool with the same InMail templates, sourcing quality is determined by who can find the non-obvious candidates first. Findem's attribute-based, temporally-modelled search is designed to surface those non-obvious matches consistently rather than relying on the recruiter's intuition to formulate the right boolean search.
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