Pick who’s asking. The Graph is the same underneath, but the questions that matter — and what a good answer needs to prove — are different for every team.
Connected Workerbee to Claude, ChatGPT or Gemini? Start with the connector flow — six prompts, in order.
You’re accountable for talent outcomes across the whole company — which means every team needs to be working from the same standard, not five different ones.
“What’s the one company-wide standard we’re evaluating this role against?”
The Success Profile behind the role — the same one every candidate and every recruiter is held to, with its provenance.
Grounded, not retrieved — one versioned standard, applied the same way to everyone.
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“How many people did this shortlist start from, and why did these five survive?”
The full evaluated population plus the capability-level evidence for who made the cut, not just the five names.
Grounded, not retrieved — you see who was actually considered, not a shortlist that just looks right.
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“Pull the decision audit for this role — what would I hand to compliance?”
A retrievable record of what was asked, what was answered, and what evidence was used — built for scrutiny, not assembled after the fact.
Everything is logged — as it happens, so nothing needs reconstructing when compliance asks.
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“Where is our workforce underinvested in the skills we’ll need next year?”
A gap analysis across the org against a future-facing Success Profile, so development spend goes where it’s actually short.
Works at org scale — this reads the whole workforce, not a sample of it.
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“Hiring managers say what this role needs has changed — what would that change in the Success Profile, and should we accept it?”
A proposed update showing exactly what the new input would change. Nothing enters the standard until someone approves it, and the change is recorded with who, when, and why.
Everything is logged — every change to the standard is reviewed and recorded, never silently absorbed.
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“What would it take for this person to qualify for that role?”
The capability gaps between their evidence and the target role’s Success Profile, so development goes at what’s actually missing.
One framework, every decision — development is measured against the same standard used to hire for the role.
More applicants doesn’t make hiring easier. You need every candidate evaluated against the same bar, and a reason for every yes and no that survives a hiring manager pushing back.
“Rank every applicant for this role against the same standard, and show your work.”
A full-pool ranking with capability-level evidence per candidate — never a black-box score.
One framework, every decision — the same standard behind hiring, mobility, and workforce planning.
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“Why is candidate A ranked above candidate C?”
A side-by-side comparison against the Success Profile, so a hiring manager’s pushback gets a real answer, not “trust the algorithm.”
Grounded, not retrieved — evidence against the standard, not a similarity score.
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“This role’s requirements just changed — re-rank the pool without starting over.”
Update the Success Profile and every ranking updates with it; the change itself is recorded.
Everything is logged — every change recorded, so you can trace why the pool looks different today.
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“Pull the full evidence trail for the candidate we’re about to reject.”
The exact evidence and rationale behind that ranking, ready before anyone asks why they didn’t advance.
Everything is logged — captured the moment the ranking ran, not reconstructed after the fact.
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“I want two more like our best performer in this role — what actually makes them successful here?”
The capabilities behind their success, captured in the role’s Success Profile so every recruiter screens for them — not a lookalike search on one person’s résumé.
Grounded, not retrieved — what success looks like at your company, written down once and applied to everyone.
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“Weight this search toward the skills we’re moving into, not the ones the team has today.”
Adjusted evaluation weights, a re-ranked pool, and the change on record — so the shift in direction is a documented decision, not a recruiter’s hunch.
Everything is logged — every weight change recorded, so the ranking can always be explained.
Your systems hold the data; none of them understand the work. You need that understanding built once, alongside what you already run, and available to people, applications, and AI alike.
“Map this job description onto our existing capability taxonomy, not a fresh one.”
Structuring against the same Graph every other role in the company already uses — no per-project taxonomy to reconcile later.
Buy it, don’t build it — one taxonomy the whole org reuses, not one your team maintains.
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“What does the “Cloud Automation Engineer” standard actually require at our company, and where else does it apply?”
One company-specific Success Profile for the occupation, reusable by every job posting mapped to it — plus whether any similarly-titled role should be mapped to it too, instead of drifting into its own standard.
One framework, every decision — every job mapped to the occupation reads the same standard.
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“Give me this understanding back through the API, not just a screen.”
The same primitives the Work Intelligence Console (WIC) and connectors call, available directly — see API reference and MCP Server.
Buy it, don’t build it — the same primitives the product runs on, without standing up your own graph.
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“How much of our workforce data is actually structured well enough to reason over?”
Evidence coverage per person and per role, so you know where the Graph is confident and where it’s still thin.
Works at org scale — coverage across the whole workforce, not a spot-check.
Why some reps hit, and what to do about the ones who don’t
Your CRM tells you who hit their number. It can’t tell you why. Put performance next to what each rep has actually demonstrated, and you can answer the question that actually moves revenue: why do some people hit, and what do you do about the ones who don’t?
“Why do some of our reps hit quota and others don’t?”
The capabilities that separate reps who hit from reps who miss, drawn from CRM results joined to each rep’s evidence — not a guess from tenure or title.
Grounded, not retrieved — performance tied to demonstrated skills, not correlated with a job title.
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“For the reps who are missing, what specifically is missing — and what should we train?”
Per-rep capability gaps against the Success Profile your top performers define, so training goes at the gaps that actually track with revenue.
One framework, every decision — the same standard behind hiring, coaching, and development.
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“How long are new hires taking to reach competency, and what’s slowing the slow ones down?”
Ramp to the Success Profile by cohort, with the capabilities that fast ramps have early and slow ramps are still missing.
Works at org scale — every rep and every cohort, not the handful a manager can track by hand.
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“We want two more like our top closers — what should recruiting screen for?”
The Success Profile for the role, written from what top performers demonstrably do, applied to every applicant.
Grounded, not retrieved — what success looks like at your company, written down once and applied to everyone.
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Bringing your own data onto the Graph
CRM performance data isn’t covered by a standard integration, but you can still add it to your Graph as your own data. Your engineers can build the pipeline with the REST API and SDK, or Workerbee can build it for you. This team had no engineers free, so they took the Engineering services path: after a 30-minute discovery call, Workerbee engineers built an automated workflow that pulls revenue and quota attainment by rep from the CRM, with sales call transcripts as supporting evidence. It adds them to the customer’s Graph as custom dimensions linked to each rep and to the Success Profile for the sales role.
Once performance sits on the Graph next to the evidence, the answers above feed two outcomes:
Accelerated time-to-competency. New reps are coached toward the capabilities top performers actually show, rather than waiting out a generic ramp.
Evidence-based training spend. Budget goes to the gaps that separate reps who hit from reps who don’t, not to training everyone on everything.
FOR HR TECH PLATFORMS & PARTNERS
Build on it, and keep the customer
You own the product, the customer relationship, and the last mile. Workerbee is the managed layer underneath: it structures your customers’ data once and answers questions over it, so your team doesn’t have to build or maintain it.
100%
What every customer’s Graph comes with
Each of your customers gets its own Graph, kept separate from every other customer’s. It grows with what’s specific to that company — its roles, its people, and what success looks like there — as new data arrives and new signals are approved.
Every one comes with:
A shared skills taxonomy, built from hundreds of thousands of real job postings, covering tens of thousands of occupations and hundreds of thousands of skills. Every customer’s roles map onto it, so different job titles for the same work read as the same thing.
Versioned Success Profiles, where new signals only change the standard once a person approves them.
A decision audit for every ranking — who was considered, and why.
Rankings that live in the Graph, not a prompt, so they don’t shift when a model is swapped or upgraded.
Across your customer base, that value compounds. Every customer’s Graph starts from the same taxonomy — already built from hundreds of thousands of job postings — so your product works the same way for each of them from day one, while each Graph keeps getting sharper about its own company.
How it fits
Already have a conversational agent? Connect it through the MCP Server.
Building your own agents and need tighter control? Call the same primitives through the API and SDK.
Getting your customers’ data in. Connect their ATS, HRIS, or LMS through Integrations, or send files by upload or API. Workerbee sits alongside those systems — it doesn’t replace any of them.
Presenting the answers. Show them however you like. Workerbee doesn’t need to appear in your UI.
The questions in the five sections above are the ones your customers can ask through your product. The answers and evidence are the same whichever surface they come from.
IN CLAUDE, CHATGPT OR GEMINI
The connector flow — 6 questions, start to finish
Once Workerbee is connected in your AI assistant (see LLM Connectors), ask these in order: each step builds on the last. Click a card to copy its prompt, then paste it into the assistant.
“Hi Workerbee, I need to hire a Senior Cloud DevOps Engineer. Here's the job description: …”
1. Create a role. Workerbee structures the JD into a Success Profile — the reusable standard for what good looks like: core capabilities vs nice-to-haves, with provenance. Answer the couple of follow-up questions it asks; they sharpen the standard.
Copies the full prompt, including a sample job description for a Senior Cloud DevOps Engineer.
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“Rank the Workerbee Network candidates for this role. Who should I talk to first?”
2. Rank the market. You get a scored shortlist from Workerbee’s pre-vetted talent network — top matches typically 90%+. Candidates appear as initials: identities stay anonymized until a candidate consents to meet you. That’s a product guarantee, not a demo limitation.
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“Why is the #1 candidate ranked above #3? Compare them side by side.”
3. Ask why. Every ranking comes with receipts — capability-level evidence against the same standard, never a black-box score.
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“Kubernetes matters more to us than on-prem experience — update the profile and re-rank.”
4. Teach it. Your feedback changes the standard itself, not just this list — and the change goes on the record. Try your own priorities: credentials vs shipped work, seniority, anything.
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“Invite the top candidate to this role.”
5. Invite — safely. In the demo the full invite flow runs, but no email is sent — nobody is contacted. On a full account this is the real thing: the candidate receives a consent-based invitation to your role, and only when they accept do you see who they are.
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“Show me the decision audit for this role.”
6. Pull the audit trail. Every scoring run is on record: when it ran, which talent pools, how many candidates were evaluated. This is what you’d hand to compliance.
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For visualizations — your company graph, capabilities, Success Profiles and who could move into which role — use the Work Intelligence Console, not the chat.
In a free demo/trial account, candidate identities are anonymized and invitations are simulated. On a full account you see your real data, and invites go to real candidates.
More examples
Cross-cutting walkthroughs, not tied to one team:
Backfill and succession from your MCP client
ask_talent_intelligence → find_candidate_rank → get_decision_audit, chained together in Cursor or Claude Code.