AI Compliance Analyzer for Marketing Materials
~75%
2–4 months
Not disclosed
HighCamp's small compliance team manually reviewed every marketing PDF from investment advisers against a 400-page SEC Marketing Rule, with no scalable way to flag violations without reading line by line.
PressW built a 14-chain LLM pipeline that replicates human compliance review, with a PDF annotation UI that inlines compliance flags into the source documents and a reviewer feedback loop that continuously improves detection accuracy.
PressW researched the SEC Marketing Rule, broke evaluation into granular pieces, built three AI components (rule recognition, disclaimer scanning, and full-text violation review with context), then developed the backend of 14 LLM chains, Azure API integration, and a UI with edit/delete feedback.
Small compliance teams and consultancies that review high volumes of investment-adviser marketing material against detailed regulatory rules.
- Azure (API integration and hosting environment)
- SEC Marketing Rule (400-page regulatory reference corpus)
- Investment-adviser marketing PDFs submitted for review (document source)
- Reviewer feedback store capturing edit and delete decisions
- Marketing PDF upload to the 14-chain LLM review pipeline
- Azure API integration connecting the pipeline components
- Pipeline flags to PDF annotation UI, inlined into the source document
- Reviewer edit and delete actions feeding the loop that improves detection accuracy
14-Chain LLM Review Pipeline
Flags Inlined Into Source PDFs
Reviewer Feedback Loop
Bryson Greenwood
HighCamp Compliance broke the rule into granular evaluation pieces and built three AI components: rule recognition, disclaimer scanning, and full-text violation review with context. Those run as a 14-chain LLM pipeline that replicates how a human reviewer works through a document, with flags inlined directly into the source PDF.
A 14-chain LLM pipeline forms the backend, integrated through Azure APIs, with a PDF annotation user interface for review. The specific models are not disclosed.
Line-by-line manual review was replaced by a pipeline that flags issues directly inside the marketing PDFs under review, and a reviewer feedback loop where editors edit or delete flags continuously improves detection accuracy. No percentage figures were disclosed.
The record does not state a timeline. The work covered researching the SEC Marketing Rule, decomposing it into granular checks, building three AI components, then developing the 14-chain backend, Azure API integration and the review interface.
Small compliance teams and consultancies that review high volumes of investment-adviser marketing material against detailed regulatory rules.
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