Staffility — Admin

Admin login

Heads-up on security: this is client-side gatekeeping only. Anyone who views the page source can see the password. It keeps casual visitors out; it is NOT real authentication.

Admin assumptions

These values drive every recommendation and ROI figure the tool generates. Edit, then Save (for this browser) or Export tiers.js (for everyone).

AI Assists model

Sizing formula: Raw Assists/yr = (employees × per-employee) + (jobs × per-job) + (hires × per-hire) + (bids × per-bid) + (resumes × per-resume).
Resume volume is derived from team size: employees × candidates per employee × parses per candidate. Captures marketing to your candidate list and employee referrals where the candidate signs up and a resume is parsed.
Required Assists = Raw × (1 + reserve %). The reserve buffer keeps customers off the redline for spikes and surges.

AI Assists per license (annual) Each license grants this many AI Assists per year.

AI Assists per employee Assist load per employee on the roster.

AI Assists per job post Assists consumed by each posting.

AI Assists per hire Assist load per filled hire.

AI Assists per bid Assist load per submitted bid.

AI Assists per resume parse Assist cost to parse one resume.

Candidates per employee / yr Drives derived resume volume (marketing + referrals).

Resume parses per candidate / yr How often each candidate’s resume gets parsed.

Reserve buffer (%) Headroom added on top of Raw Assists (default 30%).

Retention model (GovCon)

Baseline retention rate (%) % of hires retained past Year 1 without Staffility.

Retention lift with Staffility (%) Relative lift (70% × 1.30 = 91%).

Days to replace / fill a vacancy Used to compute lost revenue during vacancy.

Recruiting costs

Cost per hire WITHOUT Staffility ($) Traditional recruiting cost.

Cost per hire WITH Staffility ($) Paid to the referrer — Staffility does not collect this.

Annual revenue per employee ($) Drives lost-revenue-during-vacancy math.

Growth (pipeline lift from the Staffility network)

Staffility lift to PWIN (%) Relative lift to win probability on bids.

Staffility lift to # of bids (%) Relative lift to bids submitted per year.

Sizing guardrails

Minimum hire assumption If a customer enters fewer hires than this, the ROI math uses this minimum instead (with a visible transparency note).

Projection horizon (years) How far forward to project the year-by-year table.

Believability controls (realization & gross profit)

The underlying GovCon math is defensible, but two things keep the headline from drifting into fantasyland: (1) revenue-based dollars are discounted to their gross profit slice so we don't claim full top-line revenue as ROI, and (2) realization factors account for year-one adoption, change management, and phased rollouts. Together these convert "mathematical max" into a planning estimate that survives a CFO gut-check.

Gross profit on revenue (%) Default 35%. Applied to lost-revenue-during-vacancy and to incremental pipeline revenue. Cash recruiting savings are NOT discounted by this factor.

Realization on retention & referral savings (%) Default 60%. Applied to Referral & Turnover returns.

Realization on pipeline upside (%) Default 30%. Heavier discount because growth lift is less directly anchored than retention benchmarks.

Pricing

Per-extra-license cost / month ($) Charged for each license beyond the tier base.

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