hireburn 0.1.1

hireburn — open-source AI HR hunter: record your AI avatar once, reach candidates at a human pace, let the avatar run first interviews, and see where your funnel leaks.

Three small tools that work together or alone:

• avatar — the record-once kit. A recording script and checklist, an interview plan of pre-recorded clips with at most one voice-AI follow-up per question, a candidate page that tells people they are talking to an AI avatar before they press Start, and a scoring rubric whose HIRE / MAYBE / NO label always ships with the warning that a label is not a measurement.
• outreach — LinkedIn outreach and follow-ups at a human pace. Personal drafts from a template and a hook you wrote after reading the profile, a queue with random pauses, hourly and daily caps and working hours, a STOP switch for the first warning or captcha, and a reply tracker. A person presses "send". hireburn never does.
• funnel — where the hiring funnel leaks: losses per stage, show-up rate, completion rate, days to first real work, AI label against who is still working on day N, and a disclosure checklist measured against NYC Local Law 144, the Illinois AI Video Interview Act, Illinois HB 3773 and the EU AI Act.

Python 3.10+, standard library only, CSV / YAML / JSON in, Markdown and JSON out. Nothing leaves your machine: hireburn makes no network calls.

Why

• People put off recording an avatar. They picture a studio. You need a window, a phone and twenty minutes — once. After that the avatar asks every candidate the same questions in the same words, which is what makes first interviews comparable.
• An AI interview label is not a measurement. In the case this method comes from, the labels of an AI interview did not line up with who was still working weeks later (report, story). hireburn puts the label next to the outcome so you can check it on your own people.
• The funnel leaks where nobody looks. No-shows, bookings nobody updated, people who got an account and never started, decisions that sat for weeks. Stage counts turn that into numbers.
• Telling candidates is the feature. Disclosure, consent, a human alternative and human review are built into the page and the rubric, so the compliant path is the default path.
• Outreach bots get accounts restricted. We hit a LinkedIn restriction when the pace was not human; that is why the default limits are where they are.

Quickstart in 60 seconds

git clone https://github.com/arsentev-ai/hireburn && cd hireburn
python3 -m hireburn demo                 # no install, no dependencies: runs all three parts

Or install the command and run each part on the synthetic example company:

pip install .
hireburn funnel   examples/example-company/funnel.csv
hireburn labels   examples/example-company/candidates.csv --day 30 --snapshot 60
hireburn avatar   script examples/example-company/interview.yaml > recording-script.md
hireburn avatar   page   examples/example-company/interview.yaml --rubric examples/example-company/rubric.yaml -o page.html
hireburn outreach draft  examples/example-company/outreach.csv --templates examples/example-company/templates.yaml -o drafts.json
hireburn outreach plan   drafts.json -o queue.json

Docker: docker build -t hireburn . && docker run --rm hireburn runs the demo.

Demo

Everything in the demo is synthetic: an invented "example company" with round numbers. No real company, candidate or vendor appears anywhere in this repository.

How it works

 1  RECORD ONCE                 2  REACH AT A HUMAN PACE           3  FIRST INTERVIEW               4  MEASURE
 ┌──────────────────┐          ┌──────────────────────────┐     ┌──────────────────────────┐     ┌──────────────────────┐
 │ avatar checklist │          │ outreach draft           │     │ candidate page           │     │ funnel               │
 │ avatar script    │          │   template + your hook   │     │   "you are talking to    │     │   loss per stage     │
 │   (every clip,   │          │ outreach plan / run      │     │    an AI avatar"         │     │   show-up rate       │
 │    disclosure    │──clips──▶│   pauses · caps · hours  │────▶│   consent gate           │────▶│   completion rate    │
 │    first)        │          │   a PERSON presses send  │link │   clips + ≤1 AI          │     │   days to first work │
 └────────┬─────────┘          │   STOP on first warning  │     │   follow-up per question │     │ labels vs working    │
          │                    │ outreach track           │     ├──────────────────────────┤     │   on day N           │
          ▼                    │   follow-ups · DNC list  │     │ rubric → HIRE/MAYBE/NO   │     │ disclosure checklist │
  your avatar provider         └──────────────────────────┘     │   + "not a measurement"  │     └──────────────────────┘
  (pluggable, no keys here)                                     │   + human review for NO  │
                                                                └──────────────────────────┘

1. avatar — record once

Why one follow-up and no more: the avatar is a recording, so every candidate hears the same question. A live voice AI gets one short clarification per question; more than that turns a structured interview into a different conversation for each person.

The opening clip says it plainly, in your voice:

Hi, I'm Alex Example, Head of Support at Example Company (synthetic). Before we start, one thing
you should know: the person on this screen is an AI avatar made from my recordings. You are not
talking to me live. Short follow-up questions come from a voice AI. A person on our team reads
every answer — the AI does not decide on its own. ...

Bring your own providers. hireburn ships interfaces, not integrations: AvatarRenderer, VoiceAgent, Transcriber, Scorer in hireburn/avatar/providers.py. Write a small class for whichever avatar, voice or model service you use, read its key from your environment, and pass --scorer package.module:ClassName. ManualScorer takes scores typed by a person. KeywordDemoScorer exists only so the demo runs offline — it is not an assessment and must not be used on real people. Rubrics that mention tone, emotion, accent or face are refused: score the content of the answers.

2. outreach — a human pace, a human hand

Default limits, conservative on purpose:

The queue checks the real log of what was done, not the plan, so the caps hold even if the plan is old or two queues run at once.

What hireburn will not do: log in, click, type or send on LinkedIn; run a headless browser; scrape profiles; rotate proxies or fingerprints; get around rate limits or detection. LinkedIn's User Agreement prohibits bots and automated messaging, and so does this project. Send through LinkedIn yourself, or through an official channel you are licensed to use.

3. funnel — where it leaks

$ hireburn funnel examples/example-company/funnel.csv

| stage                            | count | from previous | lost here | of first stage |
| -------------------------------- | ----: | ------------: | --------: | -------------: |
| applications                     | 3,000 |             — |           |         100.0% |
| screened                         | 1,200 |         40.0% |     1,800 |          40.0% |
| candidates                       |   300 |         25.0% |       900 |          10.0% |
| booked                           |   120 |         40.0% |       180 |           4.0% |
| held                             |    90 |         75.0% |        30 |           3.0% |
|   ↳ no_show (of booked)          |    20 |         16.7% |           |                |
|   ↳ cancelled (of booked)        |     5 |          4.2% |           |                |
|   ↳ ai_invited (of candidates)   |    60 |         20.0% |           |                |
|   ↳ ai_completed (of ai_invited) |    40 |         66.7% |           |                |
| accounts                         |    34 |         37.8% |        56 |           1.1% |
| started                          |    24 |         70.6% |        10 |           0.8% |
| working_day_30                   |    18 |         75.0% |         6 |           0.6% |

- show-up rate = held ÷ (held + no-show): 81.8%
- completion rate = completed ÷ invited: 66.7%
- ⚠ 5 bookings are neither held, no-show nor cancelled — usually a status nobody updated.
  Treat held and no-show counts as lower bounds.

The disclosure checklist is a yardstick, not legal advice. It lists requirements from NYC Local Law 144 (bias audit, notice 10 business days ahead, alternative process), the Illinois AI Video Interview Act (notice, explanation, consent, limited sharing, deletion within 30 days), Illinois HB 3773 (notice, no discriminatory effect, no ZIP-code proxies) and the EU AI Act (hiring is high-risk under Annex III; telling people they interact with AI; human oversight; no emotion inference at work). Whether a rule applies to you depends on where the role and the candidate are — ask counsel.

Inputs

All formats are documented at the top of each module and shown in examples/example-company:

YAML is read by a small built-in parser (maps, lists, quoted strings, block text, inline [...] and {...}); for anything fancier use JSON.

Limits

• The method comes from one company's case. hireburn makes the numbers easy to compute on yours; it does not tell you which candidates will work out.
• Small groups give wide uncertainty. The Fisher test is printed so a 10-point gap between two groups of ten is not mistaken for a finding.
• The candidate page is a starting point: put it behind your own upload endpoint, check it with hireburn disclosure --page, and have counsel read it before real use.

Cite

The method comes from the payrollburn case report on AI hiring in a live company:

Arsentev, E. (2026). AI Hiring in a Live Company: A 36-Day Case Study of an AI Hiring Agent and an AI Interview (payrollburn, part 2 of "AI managers in a live company"). Zenodo. https://doi.org/10.5281/zenodo.23927437

GitHub's "Cite this repository" button gives the reference for the software; metadata is in CITATION.cff and codemeta.json. Write-up for practitioners: DEV.to · research page: arsentev.ai/research/ai-hiring.

Author

Evgenii Arsentev, MD, PhD — author of contextburn and the payrollburn case series; Founder & CEO, Dad4Kids · arsentev.ai · ORCID 0000-0002-9120-7298

License

MIT — see LICENSE.