EEO / Headcount & Diversity Report Builder: Accurate Counts, On Time, Every Cycle
Turn your messy roster into clean, category-mapped EEO-style headcount and diversity reports — validated for completeness, reviewed and approved by HR before anything is finalized or filed.
A web tool where you import your roster as of a snapshot date, AI maps each person to the right report categories and computes the aggregated counts, the tool flags data-quality gaps and small-cell privacy risks, HR reviews and approves, and it exports a report CSV in the exact layout your filing or internal requirement expects.
Before you start
- A Supabase account (free)
- A Vercel account (free)
- A Resend account (free)
- A roster export with job, location, status, and demographic fields
- Your report's category definitions (job-category and grouping rules)
- Claude Code or any AI coding agent
The problem this kills
Every reporting cycle, someone in HR becomes a pivot-table archaeologist. They pull a roster export, hand-map each job title to the right report job category, slice everyone by location and demographic grouping, build the count tables in a spreadsheet, and then second-guess whether the snapshot date was consistent, whether anyone got double-counted, and whether a tiny cell of one or two people just exposed an individual's demographics. Then they re-key it all into a filing template under a deadline.
It's slow, it's error-prone, and the stakes are real: a job mapped to the wrong category, a terminated employee who should have dropped off the snapshot, a "prefer not to say" treated as blank, or a count that simply doesn't reconcile to total headcount. You don't need to keep rebuilding this fragile spreadsheet by hand, and you don't need to be a developer to replace it.
What you'll build
A simple internal web tool. You import your roster as of a snapshot date with the fields your report needs — job, location, employment status, and the demographic data your reporting requirement uses. The tool dedupes people by employee ID at the snapshot date, maps each person's job to the correct report job category using your own rules, and aggregates the counts by category, location, and demographic grouping. It runs completeness and validation checks — does the breakdown reconcile to total active headcount? Are there unmapped jobs? Unknown locations? Missing or undisclosed demographic values handled consistently? It flags small cells that risk identifying an individual. HR opens a clean report view, resolves the data-quality gaps, and clicks Approve. Only then does the tool generate the finalized report and export a CSV in the exact layout your filing channel or internal template expects.
What's inside the Implementation Plan
The downloadable plan is a step-by-step file you paste into an AI coding agent. It opens by interviewing you about your business — which HRIS you export from, exactly what your roster columns are named, which report you're producing and its category definitions, how your jobs map to job categories, how you treat voluntary or undisclosed demographics, your snapshot-date rule, your typical and peak headcount, and your messy edge cases — and then it tailors the data model, the category mapping, the validations, and every later step to your answers. This is not a generic template; the agent reflects a short spec back to you and waits for your thumbs-up before it builds anything. From there it walks the agent through the roster import, the job-to-category mapping, the aggregation logic, the completeness and small-cell checks, the HR review-and-approve screen, and the report-and-CSV export — each step with a ready-to-copy prompt. There's also a fallback so you can build the whole thing today even with no API to your HRIS.
The governance it includes (this is the point)
This is sensitive HR and compliance tooling, so it ships with the controls a serious team needs: login so only your HR team can use it, row-level security so you only ever see your own organization's roster and reports, a complete audit trail of who imported, mapped, reviewed, and approved which report and when, a hard human-approval gate so no report is finalized or exported until HR signs off, and duplicate guards keyed on employee ID at the snapshot date so the same person can't be counted twice. Data-quality gaps — unmapped jobs, missing demographics, counts that don't reconcile — must be resolved before approval, and small cells that could expose an individual are flagged for handling.
Who it's for
HR generalists, people analysts, and compliance leads who assemble EEO-style or DEI headcount reports by hand each cycle and dread the reconciliation. If you can describe how your organization maps jobs to categories and what your report needs to show, you can build this.
You've got this — start with the plan, paste the first prompt, answer the interview, and you'll see your category-mapped headcount tables take shape the same afternoon.