The London Community
From two sites and no direction
to a working pipeline
Nic Stevens, Founder
"In two hours with Sam, we resolved the two-site question, mapped four areas where AI could add real leverage, and walked away with a working content pipeline built live in the session. The roadmap was in my inbox the next morning, specific enough to hand straight to my developer and get to work immediately."
Nic Stevens
Founder, The London Community
Background
Nic Stevens is building The London Community: a platform where foreign students can discover language schools, read student stories, get practical advice about living in London, and connect with others going through the same experience. The goal is something closer to a community and information hub than a traditional course comparison site, with school partnerships built around genuine engagement rather than price listings and commissions.
The longer-term vision is to prove the concept in London and scale globally, building a platform valuable enough to sell. Frances King School of English is already on board as an early partner, with introductions to other schools available through their network.
The problem coming into the Sprint
Nic arrived at the Sprint with two sites and an unresolved question about which direction to take. The London Community was the concept he was most excited about: community-led, student-first, differentiated. But he also had englishinlondon.net: a near-finished traditional comparison site that hadn't been updated with current prices and was already feeling like a maintenance burden before it had even launched.
Running both felt unscalable. Dropping one felt like leaving value on the table. And underneath both was a bigger question: which parts of this business could actually be automated, and what should be built first?
In his own words, what kept him up at night was whether to run both models in parallel, how to build and manage a community without a large team, and how to design lean, scalable systems from the start.
What we worked through in the session
The two-site question
The comparison site model (englishinlondon.net) depends on keeping pricing current across multiple schools: a manual, time-consuming job that gets harder as the platform scales. The community model doesn't have that constraint. We worked through the economics and maintenance burden of each, and the case for focusing on The London Community became clear: it's the differentiator, it's where Nic's energy is, and it's the harder thing for anyone else to replicate.
Four AI leverage areas
The session mapped four areas where AI could create real leverage for the business:
- SEO content at scale: automating the discovery of real student questions and turning them into articles, without an editorial team
- School data maintenance: using AI to monitor and flag outdated pricing and course information rather than doing it manually
- Community moderation and Q&A: AI-assisted responses to common student questions, reducing the load of managing an active community
- Outreach and partnerships: automating parts of the school outreach workflow to keep Nic in control without it consuming his time
What we built live in the session
The most concrete outcome of the Sprint was a working SEO content pipeline, designed and scoped live during the session. The logic: foreign students searching for information about studying in London are asking the same questions on Reddit that they'd type into Google. Intercept those questions, turn them into well-structured articles, and you build organic search presence without a content team.
The pipeline has five stages, most of them automated:
Topic discovery
A Python script scrapes six subreddits (r/london, r/StudyInTheUK, r/LearnEnglish, r/UniUK, r/AskUK, r/ImmigrationUK) using 10 search queries, deduplicates by URL, sorts by engagement, and writes results to a Google Sheet. Runs automatically every Monday via GitHub Actions.
Topic curation
Nic reviews the sheet, marks good topics "Approved", and adds a target keyword. The only manual step in the pipeline, and it takes minutes, not hours.
Content generation
A script reads approved topics and calls Claude Sonnet to generate a full article: title, meta description, 1,200-2,000 words, FAQ section, and slug. Drafts land in a second tab ready for review.
Review and editing
Every article is reviewed before it publishes. Nic edits in the sheet and marks drafts "Approved for Publishing": nothing goes live automatically.
Publishing
A script generates MDX files with frontmatter, creates a branch and pull request on GitHub per article. Nic merges the PR, and the article deploys through the existing pipeline.
| Component | Tool | Cost |
|---|---|---|
| Reddit scraping | Python + praw | Free |
| Topic storage and review | Google Sheets | Free |
| Content generation | Claude Sonnet | ~$4-5/mo at 2-5 articles/week |
| Scheduling | GitHub Actions | Free |
| Total | ~$5/mo |
What came next
Nic left the session with the pipeline built, the two-site question resolved, and a written roadmap in his inbox the following morning, specific enough to hand straight to his developer Enmanuel. The content pipeline runs automatically each week, surfaces real student questions, generates articles for review, and publishes through the existing GitHub-based deployment workflow. A content operation that would typically require a writer or an agency is running at around $5 a month.
Ready to build your own lean systems?
In two hours, we'll map where AI creates real leverage in your business and leave with a roadmap specific enough to act on immediately.
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