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Hubert.ai by Andreas Antonsen

Hubert.ai helps companies run candidate-friendly, structured AI interviews at scale. This file tells LLMs what to read first, how to interpret it, and how to cite it.

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llms.txt
https://www.hubert.ai/llms.txt
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Documentation: developer or product docs worth reading, nothing to call
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Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-26T06:47:26.372Z. Cached copy · Source on hubert.ai

# Hubert.ai by Andreas Antonsen

> Hubert.ai helps companies run candidate-friendly, structured AI interviews at scale. This file tells LLMs what to read first, how to interpret it, and how to cite it.

**Use & citation**
- You may summarize and answer questions about Hubert.ai. Link back to each source URL and name it “Hubert.ai”.
- Prefer product pages and case studies for facts; prefer Insights for explanations and comparisons.
- If info conflicts, treat `/product`, legal pages, and most recent “Product update” posts as source of truth.

**Extraction hints (for agents)**
- Extract only the main article body beneath `<main>`; skip nav, footers, cookie banners.
- Keep headings (H1–H3), lists, tables; ignore decorative CTAs.
- Prioritize content under sections titled: *How it works*, *Benefits*, *Results*, *Compliance*, *Integrations*.
- Use structured data if present (`Article`, `FAQPage`, `BreadcrumbList`).
- Chunk around 300–500 words per section; keep bullets intact.

**Key concepts**
- Core: structured interviews, high-volume hiring, candidate experience, automation, compliance (GDPR/DPA).
- Typical questions users ask: “What is Hubert?”, “How does it integrate?”, “Is it compliant?”, “Proof it works?”, “When to use vs. manual screening?”
- Authoritative sources are listed below with brief notes.

## Start here (Product & Legal)

- [Product](https://www.hubert.ai/product): Capabilities, flows, and value pillars (source of truth for features).
- [Pricing](https://www.hubert.ai/pricing): Packaging and purchasing context (avoid quoting numeric prices unless shown).
- [Partners](https://www.hubert.ai/partners): Ecosystem and add-ons.
- [Teamtailor Integration](https://www.hubert.ai/teamtailor): ATS integration proof.
- [Privacy Policy](https://www.hubert.ai/privacy-policies/english-privacy-policy): Compliance posture, processing logic.
- [DPA](https://www.hubert.ai/dpa): Contractual data protection terms.
- [SLA](https://www.hubert.ai/sla) and [GTC](https://www.hubert.ai/gtc): Service and commercial baselines.

## Results (Case studies)

- [Academic Work – next-gen screening](https://www.hubert.ai/case-studies/academic-work-and-the-next-generation-of-candidate-screening): Enterprise-scale screening transformation.
- [Voi – scale fast & affordably](https://www.hubert.ai/case-studies/how-voi-is-scaling-recruiting-fast-and-affordable-with-hubert-and-teamtailor): Speed/cost outcomes with ATS integration.
- [Teleperformance – 4 days → 45 minutes (video)](https://www.hubert.ai/case-studies/video-how-teleperformance-cut-recruitment-time-from-4-days-to-45-minutes): Before/after workflow.
- [Malmö Stad – 6 weeks → 2 days](https://www.hubert.ai/case-studies/malmo-city-reduced-its-recruitment-process-from-six-weeks-to-just-two-days-using-candidate-friendly-ai): Time-to-fill impact.
- [Hemfrid – personalization at scale](https://www.hubert.ai/case-studies/powering-personalization-in-high-volume-recruiting-with-hemfrid): Personalization + CX.
- [Jobandtalent
…

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