Running a Company on AI

Tomohiro Iida · Published June 23, 2026

Netsujo Inc. is a small startup based in Kyoto. We run the company by combining AI agents (Claude Code) and automation with quality gates that enforce facts through mechanisms rather than relying on individual care. This article publishes the details of that operating system as we actually implement it: it is not a showcase of flashy AI use, but an account of how we run day-to-day work. Three principles anchor it: humans hold judgment, AI handles implementation and verification, and errors are stopped by mechanisms rather than caught by attentiveness alone. We describe the three layers that check everything before it goes live, the audits that run automatically every week, and the decisions we deliberately keep in human hands: positioning, publication approval, first-hand verification of facts, and ethical boundaries. We also share, honestly, what has worked and what AI still cannot do.

Why we built an operating system

Netsujo was founded in June 2023 as a small, Kyoto-based startup working in Web3 and AI. As a small team took on more businesses and more sites to run, the amount that needed checking grew faster than headcount. Relying on human attentiveness alone was not sustainable: we experienced real errors, including a misstated job title and a mixed-up date. So we changed our approach. Rather than asking people to try harder to be careful, we built errors-cannot-happen-or-get-stopped mechanisms into daily operations itself. We call the resulting collection of mechanisms our operating system internally: AI agents handle implementation and verification, automated gates enforce quality, and humans hold judgment and responsibility.

Three operating principles

• Humans decide, AI implements: positioning, publication approval, and ethical judgment stay with people; research, code changes, analysis, and routine work go to AI agents.
• Facts are enforced by mechanism, not vigilance: relying on human attention always lets some errors through, so we require sources for claimed results, run automated checks for banned phrasing, and add third-party AI fact-checking.
• Everything gets recorded: decisions, work results, and audit outcomes are kept in markdown or Discord so they can be verified later, avoiding reliance on what one person happens to remember.

Three layers of quality gates

We do not publish what AI writes as is. Every piece of work is checked by three mechanisms of different character: one is CI, automatically enforced before anything reaches production; the other two are an operating rule and a verification script. Layering mechanisms with different levels of enforcement makes errors easier to catch.

Third-party fact-checking via Codex (operating rule)

Before anything is reported as complete, we run it once through OpenAI's Codex CLI (GPT) to cross-check the work against the actual repository, surfacing errors, exaggeration, and unverifiable claims stated as fact. Having an AI other than the one that produced the work check it reduces mistakes caused by the author's own blind spots.

CI that blocks factual errors (automated)

Errors that have actually happened before, such as a mistaken title, a fabricated credential, an old service name, or a wrong date, are registered as banned patterns in CI. If matching text appears, the CI check fails automatically before it reaches production. Of the three layers, this is the only one that is mechanically enforced.

Proof verification requiring a source (script)

Claimed achievements, such as speaking engagements, awards, or media coverage, must be registered with a verifiable source URL and a verification date; if either is missing, the verification script fails with an error. A human still checks the source content itself; the script only guarantees that a source and a verification date are always attached.

As an example: if someone's title were written incorrectly, under the old process the mistake could sit until a reviewer happened to notice it. Under this system it matches a banned pattern in the fact-error-blocking CI, and the check fails automatically before release. Turning a past mistake into a registered pattern lets the same error be caught automatically the next time.

Weekly automated audits

Quality does not hold up under a one-time check. Every Monday morning, several audits run automatically via GitHub Actions, with the results posted to Discord. Rather than checking only when asked whether there is a problem, this runs on a standing schedule.

• Full GSC audit: combines Search Console and GA4 to check sitemaps, indexing status, CTR underperformance, and canonical mismatches
• Core Web Vitals audit: measures mobile and desktop load speed via PageSpeed Insights
• Funnel effectiveness: analyzes traffic and results for local-government and Kansai Web3 funnels using GA4 and GSC
• IndexNow ping: automatically notifies Bing IndexNow of key URLs to encourage crawling
• llms.txt consistency check: verifies the URL consistency of the llms.txt file provided for AI search
• Originality audit: scores article content for originality and flags thin content

In addition to these, a fact-error-blocking CI check and a check for unresolved merge conflicts both run automatically before code reaches production. Serious issues found are fixed on the spot; minor ones are logged and addressed in the next cycle. We try to put as much of the path from detection to fix onto a system as possible.

What stays with humans

The more we delegate to AI, the clearer it becomes what humans must keep holding. The following decisions are never handed to a system; a person carries the responsibility.

• Deciding positioning and messaging
• The final call on whether something is fit to publish
• First-hand verification of facts, including proper nouns and track record
• Where the ethical line sits
• Deciding what to build next, and what to stop doing

This operating system does not aim to reduce the number of people involved. It aims to free people to spend their time on the highest-value work there is: judgment. The more we hand implementation and verification load to AI and to the system, the more time we have to think about what to build, who it reaches, and how to stay honest.

What we learned from delegating to AI

• Strength: repetitive work, such as bulk fixes, audits, and applications, shrinks dramatically with AI and automation.
• Strength: quality checks built into the system are not affected by anyone's workload or condition on a given day.
• Strength: because records persist, past decisions can be verified and reused later.
• Limit: AI still makes mistakes through exaggeration or unfounded assumptions; third-party checks and sourced verification remain essential.
• Limit: decisions about what to build and whether to publish cannot be handed to a system; they still require human judgment.
• Limit: building the system itself carries real upfront cost. Starting small and growing it is the realistic path.

Summary

Running a company with AI agents does not mean replacing people with a system. It means holding firmly to a division of labor: humans decide, AI implements and verifies, and mechanisms stop errors. Even a small startup can reach stable operations with fewer oversights once quality is built into the system this way. Netsujo tests this operating system daily on its own site: building the tools we use ourselves, and enforcing our own integrity through mechanisms. We believe that discipline feeds directly into the quality of the development and analysis work we provide clients.