The Candidate Was Already There

A recruiter opens a database and types a name. There is the candidate: old title, old company, perhaps an email address that has since gone to the graveyard where email addresses go. The person is still alive, of course. They may have learned a new skill, moved to another city, become a hiring manager or grown into precisely the candidate the recruiter needs. The database has preserved the acquaintance while losing the person.

Woo.io has built a business around that small, expensive embarrassment. Its software connects to the systems staffing firms already use, refreshes candidate and client records, tracks career changes and suggests when an old relationship deserves a new conversation. The pitch is almost impolite in its simplicity: before buying access to more strangers, find out what happened to the people you already know.

The three-month disappearing act

Kotzer and Dudu did not arrive at recruiting as tourists. The pair had worked together in technology and founded SeeV, a tech recruiting firm. Kotzer later described a familiar pattern from that work: agencies lost touch with candidates roughly three months after a person joined. A recruiter would invest in a conversation, learn what someone wanted, perhaps even place them, and then let the relationship cool. The next assignment would start with a fresh hunt.

Woo, founded in 2015, first addressed the problem from the candidate's side. It offered tech workers a discreet way to describe the job they might accept without publicly announcing a search. The wish list could include salary, location, company type and work-life balance. An employer interested in that person could meet someone already willing to consider a change. That was a sensible remedy for the awkwardness of being employed while curious about elsewhere.

The founders called the resulting virtual headhunter Helena. In 2017, Woo announced Helena alongside a $7 million Series A round. Its design was two-sided: act as an agent for candidates and a headhunter for companies. Woo reported that 52% of candidates Helena sourced reached interviews in its early results. That figure was Woo's own account of its test, rather than a guarantee of what another recruiter would see.

The unusual part of the story is what happened before the software looked clever. Kotzer said Woo started with 20 recruiters doing the job manually while engineers studied them. Dudu said the human operation let the company validate the process and gather the data needed for the product. Building Helena took nearly three years. The cost visible in public was not merely venture capital; it was a sizeable working model of the thing the software was supposed to automate.

That method is more portable than the bot's name. When an expert task is full of tacit judgment, make people do it in view of the builders. Capture the questions they ask, the evidence they distrust and the moments they change their mind. Only then decide which parts a machine should handle.

The archive becomes the product

Today's Woo is best understood through the staffing firm's own archive. Its AI Career Tracker monitors job moves, promotions and relocations. Its data enrichment service updates candidate and client records in an existing system. A profile alert can recognize that a person being viewed elsewhere already belongs to the team's network. AI Sourcer ranks people for an open role and, according to Woo's product description, shows the reasoning behind a recommendation. A recruiter still has to judge whether that person wants the role and whether the moment is right.

This matters because a staffing agency's ATS is both a memory and a work surface. Search it with stale information and a good candidate can appear to be a bad match. Send automated outreach from that record and the error becomes a message to a real person. Woo's distinction is its place in that chain. Bullhorn, Greenhouse, Lever, Salesforce and HubSpot are among the systems Woo says it works with; the company sells an added layer of current data and signals around the systems that teams already operate.

Its legal terms describe an order-form subscription for the platform. Woo's site asks prospective buyers to book a demo. That fits the market: a staffing team needs to know how its particular database will be matched, updated and used. Public materials do not establish a single price per seat or record. More important to a buyer is the operational question: how much old data is usable after the service runs, and how much recruiter time does that actually save?

A year-old candidate, found in a fortnight

Spinks offers a more tangible example than a promise about AI. The tech recruitment firm had a large pool of candidates with whom it had already spent time. In Woo's 2022 case study, Spinks described using granular search, profile updates and a Bullhorn integration to revisit those relationships. One recruiter placed a candidate who had sat in the database for more than a year within less than two weeks of renewed attention.

That is a customer story, not a randomized experiment. It still captures the business model neatly. A successful placement did not require a new name to be found on the open market. It required an old record to become trustworthy enough to use and visible enough to act on.

The first two figures come from Woo's founder interview; the placement is from its Spinks case study.

Woo also quotes TechNET IT leadership saying 80-85% of the firm's placements now come from its own CRM. That is a striking testimonial, though it describes TechNET's operation and cannot by itself isolate Woo's contribution. The useful principle is smaller and sounder: a database only earns its keep when recruiters believe the people in it are still there.

The quiet competitor is inertia

Woo competes with other sourcing and enrichment tools, as well as features inside an agency's existing software. Yet the toughest alternative may be the office habit of searching externally because an internal result has disappointed too often. A stale title can send a recruiter to a job board. A bounced email can end a search. Each small failure teaches the team to distrust its own records.

The company's change in emphasis makes sense in that light. The early product promised a better introduction between strangers. The current one promises continuity after the introduction. Candidates change jobs; clients change companies; a person once placed can become the person hiring. Woo's present language calls this relationship intelligence. It is a claim about time, not merely about matching.

The practical playbook is available even to a team that never buys Woo. Choose a narrow group of past candidates or clients. Check how many titles, companies and addresses are wrong. Refresh those records, then compare outreach response and placement opportunities against a similar untouched group. Give recruiters a reason to revisit old contacts at a specific moment, such as a promotion or move. The experiment works best where a team already has a substantial network, permission to use its data and people willing to act on the signals. Without those ingredients, a cleaner database is simply a tidier cupboard.

Woo's story began with the dream of an agent that could search on everyone's behalf. Its more persuasive idea is less glamorous: keep the acquaintance alive. In recruiting, the person you need may already have said hello. The trick is remembering who they became.