We Run Kaizen on AI
Case study · Our own company How we run Kaizen on AI An AI agent does the routine work of running our company. It sorts email, writes up meetings, reads contractor invoices, drafts client invoices, follows up on late work and reviews new federal awards. It prepares the work, and a person approves anything that leaves the company.
- ▪Case study · Our own company How we run Kaizen on AI An AI agent does the routine work of running our company.
- ▪It sorts email, writes up meetings, reads contractor invoices, drafts client invoices, follows up on late work and reviews new federal awards.
- ▪It prepares the work, and a person approves anything that leaves the company.
Hacker News (AI / LLM) files mainly under ai. We currently carry 6,216 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Kaizen |
| Canonical URL | https://kznconsulting.com/work/how-we-run-kaizen |
| Publication time | Thu, 24 Sep 2026 03:08:51 +0000 |
| Retrieval time | 2026-09-24T03:35:12.714Z |
| Last seen | 2026-09-24T03:35:12.714Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Case study · Our own company How we run Kaizen on AI An AI agent does the routine work of running our company. It sorts email, writes up meetings, reads contractor invoices, drafts client invoices, follows up on late work and reviews new federal awards. It prepares the work, and a person approves anything that leaves the company. We build the same kind of system for clients, so this page shows what it does and the rules it follows. In use since2025Email sortedEvery 5 minutesEmail, invoices and messagesSent only after a person approvesRaw meeting transcriptsDeleted within 24 hours On this page The rules it followsA working dayWhat it does, by areaWhat we changedWhat this means for you The rules it follows Most of the work is done by scheduled jobs: one for email, one for meetings, one for…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Kaizen.
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