GT Gilligan Tech Inc.
Regulation Intelligence

How public law data becomes source-cited RAG.

Data protection laws, age restriction laws, and CO2/climate laws are separate product sections. This is the one shared explanation of how Gilligan Tech Inc. turns public sources into searchable, source-cited knowledge feeds.

Data protection lawsOpen live feeds
Pipeline proofdiscover -> scrape -> extract -> chunk -> embed -> retrieve -> cite -> serve

The same operating process supports multiple independent knowledge products without forcing the markets into one page.

Why this page exists

One method, three markets.

This page is allowed to connect the sections because it explains the analysis system. The commercial pages stay independent.

Analysis stages

Source discoveryIdentify official agencies, laws, regulators, guidance, ratings, and stable public repositories.
Scraping and extractionCollect web pages and documents, normalize text, preserve source URLs and metadata.
Chunking and enrichmentSplit documents into retrievable passages with jurisdiction, category, title, and citation context.
Search and answerCombine lexical and vector retrieval, feed grounded context to the model, and return citations.

Product boundaries

  • Data protection and AI laws have their own buyer, corpus framing, and live feed.
  • Age restriction laws have their own policy vocabulary and risk model.
  • CO2 and climate laws have their own climate/compliance source base.
  • The shared pipeline reduces operating cost without collapsing product positioning.

Retrieval design

Search uses source metadata, jurisdiction filters, keyword relevance, vector similarity where available, and answer generation constrained by retrieved evidence.

Citation discipline

Each product page should make clear that the value is source-grounded analysis, not generic model opinion.

MCP delivery

The same corpora can be surfaced to browsers, REST consumers, and MCP-capable agents for operator workflows.