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Ale Cabrera

SHIPPED · DPARENTS · 2026 · 10 WEEKS

Building a Source-First AI Agent for Traveling Families

An AI search agent for worldschooling families that plans its own search, checks what it found, and only cites sources it can actually show.

163 results rated by hand

Accurate: 122
Partial or off-target: 36
Needing sources: 3
Hallucinated: 2

Catalog review, 5,668 entries live

Regulations human-verified: 100% (270)
Entities human-verified: 81% (626 of 777)
Resources AI pre-screened: 72% (2,857 of 3,954)

How the agent answers

  1. Plan
    pick tools + queries
  2. Search
    catalog, web, YouTube
  3. Evaluate
    good enough?
  4. Finalize
    grounded answer + sources

Three risks, and what happened

RiskHow it was handledResult
Value. Generic web and YouTube search returns a wall of results with no judgment about a specific family. Without trust there is no product.Trust as a data problem first: a structured taxonomy with review states, seeded by a hand-curated set of 300 entries.Every source shown in an answer is a real search hit, never a model-invented URL.
Feasibility. Unclear whether a solo, AI-built agent could stay grounded.A LangGraph agent that plans, searches, evaluates, then refines or finalizes. Refinement capped at 2 rounds.75% of 163 hand-rated results accurate, 2 hallucinated.
Compliance. Logging real questions with location context meant retention and privacy had to be designed in, not added later.Request-log IPs stored as HMAC hashes, 90-day retention with a daily purge.Image uploads only with consent.

My role. Defined the problem and the trust principle the product is built on. Hand-curated the original dataset of 300 entries. Designed the taxonomy and the review states. Owned the guardrail spec.

AI collaboration. Claude Code and Cursor stood in for an engineering team: 220 merged PRs, 145 database migrations, 249 automated tests.

Product leader with a data scientist past · Zapier, Clearbit, HubSpot

Product doc: ale-cabrera.com/portfolio/dparents