How I auto-enrich a brand database with AI on cache miss (Lovable + Claude API)

#ai #buildinpublic #lovable #showdev

Most database designs have two ugly options:

Manually seed thousands of rows (impossible for niche data like Japanese wholesale suppliers)
Force users to enter everything (terrible UX, dead-on-arrival)

Last week I shipped a third option in 30 minutes with Lovable: let the database grow itself.

Every search that misses the cache triggers Claude API to generate a real, structured entry — and saves it. The next user gets an instant hit.

Here's the exact pattern.

The pattern in 4 lines

async function search(query) {
  if (await db.has(query)) return db.get(query);
  const entry = await aiGenerate(query);
  await db.save(entry);
  return entry;
}

That's the whole thing. The magic is in what happens to the database over time.

Why this beats alternatives

Seed-only DBs require domain expertise upfront. For my Japan Brand Finder, that meant cold-calling Tsubame-Sanjo metalworkers — months of effort before launching.

User-fed DBs have chicken-and-egg. Empty DB → no value → no users → no entries.

Cache-miss enrichment sidesteps both:

Launch with 20 seed entries (1 hour)
AI fills the long tail as users search
Every miss makes the DB better for the next user
Cost grows linearly with usage (predictable)

The prompt that actually worked

The hard part isn't the pattern. It's getting AI to produce structured, useful entries instead of generic Wikipedia summaries.

What worked for me (Japan Brand Finder context):

You are filling a database row for a Japanese manufacturer.
The user searched: "[QUERY]"

Generate a JSON object:
- name_en: English brand name
- name_jp: Japanese name (kanji or kana)
- category: from this list [...]
- hq_location: city, prefecture
- english_support: "good" | "limited" | "none"
- business_culture_notes: 1-2 sentences

If the brand doesn't exist, return null. Don't invent.

Two key tricks:

JSON schema forces structure (no rambling output)
"Return null if doesn't exist" gives AI permission to refuse

The second one cut hallucination by ~80% in my testing.

Economics

Per search: ~$0.005 with Claude Sonnet
Per 1,000 searches: ~$5
DB grows: ~700 unique entries (cache hit ratio improves over time)

After Month 2, ~70% of searches hit cache → AI cost drops while DB value compounds.

What I'd improve

Verification batch job — weekly re-check generated entries against external sources
User flagging — one-click report for wrong entries
Quality tiers — mark "AI-generated" vs "human-verified"