AI chatbot for technical recruiters: routes candidates and clients by stack
Stop spending mornings triaging candidate emails and weak client briefs. SleekAI splits the two on the first message and routes by stack, using OpenAI, Anthropic, Google, or OpenRouter with your own API key.
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Technical recruiting is stack matching at scale
The whole job of a technical recruiter is mapping people to roles that fit their stack, level, and timing. The website usually does none of that work. A senior Go engineer looking for a remote role and a CTO looking to hire ten platform engineers land on the same contact form. SleekAI runs both conversations natively, asks the right questions, and routes each to the right pipeline.
The bot reads your active roles, your practice focus, your typical placement fees, and your candidate intake structure from WordPress (post types, ACF fields, taxonomies, the data-source wizard handles each). When a candidate asks "any senior Go roles, remote, EU time zone?" the bot checks the active job custom post type for matching tags and surfaces the two or three roles that fit. When a CTO asks about a platform engineering hire, the bot quotes the fee model (percentage of base or fixed-fee retained), confirms stack focus, and books a scoping call.
BYO API key means OpenAI, Anthropic, Google, or OpenRouter and you pay only your provider. Conversation logs stay in WordPress with model name, token usage, and page URL. The bot is the qualifying conversation a senior associate would run if they had unlimited time, which is to say the conversation no one in your firm currently has time to run consistently.
Workflow
How SleekAI handles technical recruiter inquiries
Split candidate from client
Ground in active roles and stack focus
Run the right qualifying conversation
Route to ATS and CRM
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A typical technical recruiter conversation
Comparison
Generic chatbot vs SleekAI for technical recruiters
Generic chatbot
- Can't tell a candidate from a hiring client
- Doesn't know your active roles or stack focus
- Misses the difference between contract, retained, and embedded
- Books client calls without confirming stack or seniority
- Skips candidate qualification questions like comp and notice period
SleekAI chatbot
- Splits candidate and client conversations on first message
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Reads active roles from
wp_postsfiltered by stack tag - Quotes fee structure (percentage of base or retained fixed-fee)
- Qualifies stack, seniority, location, and timing
- Routes candidates to ATS and clients to CRM via webhook
Features
What SleekAI gives you for Technical recruiters
Stack matching
Active roles are tagged by stack (Go, Rust, Python, .NET) and the bot filters by stack on candidate inquiries. A Go engineer sees Go roles, not the full job board.
Intent routing
First-message intent splits candidate from client. Candidates get matched against active roles; clients get qualified on stack focus, seniority, and timing before any partner time.
Candidate intake
Captures CV, LinkedIn, notice period, comp band, and remote preferences cleanly. The candidate record lands in the ATS with the conversation context, not a one-line form submission.
Use cases
Where technical recruiters use SleekAI
Role discovery
Engineers find roles that match their stack, level, and location in one conversation instead of scrolling through a 200-role job board sorted by date posted.
Client brief qualification
CTOs and hiring managers describe stack, level, and timing in chat. The scoping call walks in with the picture already drawn instead of starting from "so tell me about the role."
Candidate intake
Active candidates submit profiles cleanly: CV, notice, comp, remote preferences, stack. The record lands in the ATS with the full transcript so the recruiter has context before the first call.
The bigger picture
Why technical recruiting needs structured intent routing
Technical recruiting is one of the most stack-sensitive lines of work in B2B services. The fit between a candidate and a role hinges on languages, frameworks, infrastructure, level, location preferences, and timing. The fit between a firm and a hiring client hinges on stack focus, level focus, and fee model.
A generic chatbot, with no view of either side of the marketplace, can do nothing useful in this domain. It can answer "what are your office hours?" and route to a contact form, which is what every site already does. The whole opportunity is in the structured matching.
A candidate who asks about senior Go remote roles should see senior Go remote roles, surfaced from the active job custom post type filtered by tag, not a generic "please send us your CV" reply. A CTO who asks about a platform engineering hire should be qualified on stack and level before any partner time is committed. Both behaviours are completely standard in a senior associate's first conversation.
They just don't happen on the website because the website is static. Deploying a bot that runs those conversations natively closes the gap between what the firm does in person and what the website does, which is the most important thing the website can do. The intent split is the cornerstone.
Without it, the bot fails immediately, because the questions a candidate has and the questions a hiring client has barely overlap. With it, both sides get a meaningful conversation, the partner sees only the qualified leads, and the candidate record lands in the ATS as a shaped profile rather than a one-line form submission. The ATS integration is the multiplier.
Conversation logs in WordPress, pushed to Greenhouse or Lever via webhook, mean the candidate's intake conversation is preserved as context for every recruiter who picks up the file later, which is exactly the kind of historical context that disappears when intake happens in scattered emails.
Questions
Common questions about SleekAI for Technical recruiters
Yes, that's the whole point. Active roles live as a custom post type tagged by stack, level, location, and contract type. SleekAI filters by tag, so a senior Go engineer asking about remote roles sees the matching subset of the job board surfaced in chat, with comp range and stack details. When a role gets closed or a new one posts, the next conversation reflects it without any retraining.
Client briefs run a separate track. The bot asks about stack, seniority, team size, timing, and budget, then confirms practice-area fit before booking anything. Below-floor briefs (single junior hire when the firm only does staff-plus engineering, or stacks outside the firm's focus) get a polite redirect to a peer firm or a self-serve resource rather than booking a wasted scoping call.
SleekAI logs every conversation in WordPress, and tech recruiting firms typically use Zapier, webhook, or direct API integration to push candidate profiles into Greenhouse, Lever, Workable, or Recruit CRM. The conversation transcript plus the structured intake fields (stack, comp, notice, location) become the candidate record so the recruiter has the picture before the first call rather than running intake from scratch.
Yes, when the firm wants it to. Fee structure (percentage of base, retained fixed-fee, contract markup) lives on a services page or in ACF fields, and the bot quotes the structure plus the typical placement timeline. Most firms keep specific percentages off the public bot and quote ranges ("typical placement is 20-25% of first-year base") with specifics confirmed on the scoping call.
Yes. The system prompt can be configured for multiple languages, and the underlying models (OpenAI, Anthropic, Google) handle most major European and Asian languages natively. For sites running WPML or Polylang, each language version can have its own bot instance with the active-roles feed translated to match the site language.
Confidential mandates stay off the public bot. For confidential roles you can choose to surface the existence of the role without disclosing the client ("we have an active staff backend role at a Series C fintech; the recruiter can share more after a quick call") or keep them entirely private until a recruiter confirms fit. The behaviour is set in the system prompt and respects whatever confidentiality posture the firm prefers.
BYO API key means you pick: OpenAI, Anthropic, Google, or OpenRouter. For most tech recruiting sites a mid-tier model handles candidate matching and client qualification easily. If the volume is heavy, OpenRouter lets you fall back to cheaper models on simple intent routing and reserve the stronger model for the actual qualifying conversation. Token usage is logged per conversation so the cost picture is visible from day one.
No, and it shouldn't. The bot is configured to quote comp bands as published on the role and capture the candidate's expected range, but specific negotiations sit with a human recruiter. That discipline is what keeps the bot from creating mismatched expectations or saying something on the firm's behalf that the candidate later quotes back during a real negotiation. Conversation logs preserve the band the bot quoted, which is useful context for the recruiter.
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