A UK recruitment agency specialising in supply and permanent placements for schools needed a faster, more structured way to handle incoming vacancy enquiries. Working with Stitch, the WhatsApp business communication platform, the agency deployed a keyword-triggered chatbot flow with a pre-filled WhatsApp URL that captured vacancy details upfront, pre-qualified candidates and schools before human contact, and routed each enquiry to the correct internal team. The build used a WhatsApp link that could be customised to open a chat with a specific starting message, such as an interest in filling a school vacancy, so the automation began the moment the conversation started rather than after a human replied.
School recruitment runs on volume and urgency: schools need cover fast, and agencies fielding those enquiries by phone or unstructured messaging risk losing time to manual triage. Before automation, every enquiry landed as an open-ended conversation, with no consistent way to capture the type of vacancy, the school name, contact details, or start date before a recruiter got involved. That meant staff time was spent asking the same qualifying questions on every single enquiry, rather than acting on information that had already been captured.
The agency needed a way to pre-qualify enquiries at the point of contact, before assigning them to a recruiter, without losing the personal, responsive feel that WhatsApp as a channel is known for. It also needed to set the right expectation with the person messaging in: that they were speaking to an automated flow first, and that a human team member would follow up as soon as possible.
Stitch worked with the agency to design a chatbot flow built around a pre-filled WhatsApp URL. The link could be customised to include a specific opening message, for example expressing interest in filling school vacancies, so the chatbot immediately understood the intent behind the conversation as soon as it started. This meant the automation didn't need to guess at context. It knew, from the very first message, what the enquiry was about.
From there, the flow used keyword triggers to fire predefined responses and actions, moving the conversation through a structured sequence rather than leaving it open-ended. Certain questions in the flow were configured to require a reply before the conversation could progress, which ensured the data being captured was complete rather than partial, and confirmed the candidate or school was genuinely engaged rather than dropping out mid-conversation.
A key design decision was transparency: the flow was built to explicitly set expectations that the respondent was interacting with a chatbot, not a person, and that a team member would follow up as soon as possible. This mattered for trust as much as for how UK GDPR and data residency rules apply to WhatsApp business messaging and how the agency wanted to represent itself to schools and candidates.
The chatbot flow was structured around a specific set of qualifying questions designed to capture the information a recruiter would otherwise have had to ask manually on every call or message. These included:
Each of these data points was captured automatically, before the enquiry reached a human, using the keyword-triggered flow logic. Because certain questions required a reply to proceed, the agency could be confident that by the time an enquiry reached the assignment stage, the core qualifying information was already on record rather than needing to be chased.
Once qualification was complete, the flow assigned the conversation to the appropriate internal team, rather than routing every enquiry to a single generic inbox. This meant conversations arrived with the recruiter or team best placed to act on them, already carrying the context needed to respond meaningfully on first contact.
The flow itself was not fixed. It was built to be customised and edited as requirements changed, allowing the agency to adjust the qualifying questions or the routing logic as its own processes evolved. This gave the agency ownership of the flow's structure rather than a rigid, one-off build that would need a developer to modify.
The core outcome of the deployment was a shift from manual, conversation-by-conversation qualification to a structured, automated front end that did the qualifying work before a recruiter ever engaged. By capturing vacancy type, school name, contact details and start date automatically, the agency's team could pick up conversations already knowing what the enquiry was about, rather than starting from zero on every interaction.
The keyword-triggered, reply-required design meant that data capture was consistent rather than dependent on how thoroughly a given caller or recruiter probed for details on a given day. Because the flow explicitly told respondents that a chatbot was handling the initial exchange and that a team member would follow up promptly, the agency was able to automate the first stage of contact without misrepresenting who, or what, candidates and schools were speaking to.
The pre-filled URL mechanism also meant the agency could direct enquiries into the flow with intent already attached, since the opening message could be set to reflect exactly what the enquiry was about, such as interest in filling a school vacancy. That removed a layer of ambiguity that would otherwise have required a recruiter to establish manually at the start of every conversation.
Because the flow assigned qualified enquiries to the correct team automatically, the agency was able to route work based on the content of the enquiry rather than on whichever recruiter happened to be free to answer first.
For recruitment operations leads managing high volumes of candidate and client enquiries, this deployment demonstrates a few transferable principles:
For recruitment agencies handling the same kind of high-volume, time-sensitive enquiries, WhatsApp automation built this way turns the first stage of every conversation into a data capture and qualification step, so that by the time a recruiter is involved, the groundwork is already done.
