Case study · 2025

Automated market-report generation

A multi-agent pipeline that rebuilds PDF market reports as schema-marked, indexable HTML at scale.

Role: Architect and engineer

Problem

A large library of market reports existed only as PDFs. They were invisible to search engines, could not be reused as web content, and represented locked-away value.

Constraints

Output had to be accurate (no fabricated figures), consistently styled (a fixed component library, not AI-invented layouts), and search-friendly (valid structured data, crawlable data tables behind every chart).

System

A multi-stage agent pipeline ingests each PDF, enhances content, generates HTML against a hand-built component library, and runs a QA pass that validates figures and structure before publish. Component selection is deterministic, so layouts never drift, and every chart is backed by a crawlable data table with Schema.org markup.

ingestenhancegenerateQApublish
ingest -> enhance -> generate -> QA -> publish

Outcome

241 reports were rebuilt as schema-marked, indexable HTML pages, turning locked-away PDFs into reusable content that a search engine can actually read.

Stack

A multi-agent generation pipeline, a hand-built HTML and CSS component library for deterministic layouts, and Schema.org JSON-LD with crawlable data tables for every visualization.

Outcome

241
reports rebuilt as indexable HTML
5-stage
agent pipeline with a QA gate
100%
charts backed by crawlable data tables

Stack

Multi-agent pipelineSchema.org JSON-LDHTML/CSS component library

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