Documents and notes decision

NoticeRegistry

A technically capable user can build a limited, single‑state replacement (search, summaries, alerts) in about a week, but reproducing NoticeRegistry’s full 51‑state indexed archive, scale, and dataset is impractical without significant ongoing collection and data-engineering work.

Visit website
You pay

$9/mo

$108/yr

Read off the official pricing page.

You’d pay instead

$100one-off40 h to build

$100/mo4 h/mo upkeep

On cash alone, building overtakes the subscription at 13 seats.

No open-source build does this yet

Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.

What a replacement has to do

  • Ingest public notices, parse and extract entities, geocode and cross-link by address/party/case, provide search and notice detail pages, implement watchlists/email alerts and a simple REST API.

What it still won’t have

  • Full, multi‑state archive and daily ingest across 51 states
  • High-volume Data API quotas and CSV/Excel bulk export
  • Built-in PDF download limits and case dossier generation
  • Priority support, team/workspace features, and enterprise data hygiene

What remains hard

  • Proprietary dataEvery notice is pulled from official courthouse records and legal newspapers , parsed into plain English, geocoded, and cross-linked by address, party & case — with new filings added daily across 51 states.
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 13 seats.

Paid seatsseats

Money you would actually spend

Keep paying
—

Subscription price × seats × 12

Build it
—

AI build —APIs + hosting —

Time you would spend

—

—

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a single-state public-notice indexer and search app using Node.js + Express, Postgres, and a simple React UI. In scope: scheduled ingest from a single official source (CSV or scraped page), store raw notices in Postgres, run an LLM (e.g., OpenAI) to generate a short plain‑English summary and extract entities, geocode addresses via a paid geocoding API, cross-link notices by normalized party/case/address, provide a REST search endpoint and a web UI for search/detail, implement watchlists with periodic background job and email alerts (SendGrid). Out of scope: multi-state ingest, PDF generation, team accounts, and large-scale API quotas. Include basic error handling, retries for network calls, logging, and unit tests for the ingestion, parsing, and search components.
How we checked3 sources · 2/3 runs agreed · evidence score 55

How the score was reached

  • Partly verdict base52
  • 3 cited sources+3
  • Price verified on pricing page+3
  • Hard moats found in the evidence-3
  • Evidence score55

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 3

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat quoted from the page