Capital Markets / Compliance
IPO Prospectus Review Automation
AI-powered document intelligence for capital markets compliance.
- Document Intelligence
- RAG
- Change Classification
- Regulatory Knowledge Base
- Audit Trail
90%+
reduction in the review cycle, from 2-3 days to under 4 hours
100%
of document changes auto-classified by severity and category
30 days
from engagement to production deployment
- Every flagged item cross-referenced against the applicable regulations with source-cited rationale.
- Audit-ready output end to end, reviewer-fatigue compliance risk eliminated.
The saving comes from replacing a linear read with an exception review. Reviewer judgement is applied to the findings rather than to locating them.
An offering document is one of the densest review tasks in capital markets. A prospectus routinely runs to several hundred pages, assembled from sections drafted by different teams and advisers, and every figure, cross-reference and disclosure in it has to be internally consistent and compliant with the applicable requirements before it can be filed.
The review is performed against a deadline that is externally set and does not move. That combination — high page count, exacting consistency requirements, fixed date — means review capacity, not analysis, is usually the constraint on a transaction timetable.
Why it is hard
The difficulty is not that any single check is hard. It is that there are a great many of them, they span the whole document, and they interact: a figure updated in the financial section has to be chased through every place it is referenced, and a change to one disclosure can affect the adequacy of another.
A human reviewer doing this well is performing cross-document reconciliation from memory across hundreds of pages, under time pressure, usually late in a transaction. It is precise, repetitive work where a single missed inconsistency carries disproportionate consequence — which is exactly the profile where fatigue causes errors and where an automated first pass earns its place.
What we built
The system applies document intelligence to the review: parsing the prospectus with its structure intact, extracting the figures, defined terms, cross-references and disclosure items, and checking them against one another and against the requirements the document has to satisfy.
The output is not a verdict. It is a prioritised set of findings — inconsistencies, missing items, cross-references that no longer resolve — each pointing at the specific location in the document that produced it. Reviewers go to the exceptions rather than reading linearly, and the professional judgement stays with the professionals.
- Layout-aware parsing that keeps document structure, tables and cross-references intact
- Extraction of figures, defined terms, cross-references and disclosure items
- Consistency checking within the document and against the applicable requirements
- Findings prioritised and linked to the exact location in the source document
- Human review retained on every finding — the system flags, reviewers decide
Questions about this engagement.
Does the system decide whether a prospectus is compliant?
No. It identifies inconsistencies, missing items and unresolved cross-references, and points at where each one occurs. Whether a finding matters, and what to do about it, is a judgement made by qualified reviewers. The time saving comes from not having to find the issues by reading, not from removing the review.
How does this handle the document changing during the review?
Re-running against a new draft is cheap, which is the practical advantage over a manual read. Where a manual review of a revised document means re-reading it, an automated pass reprocesses the whole document and shows what has changed in the findings — so a late amendment does not consume another full review cycle.
Could the same approach apply to other filings?
The pattern generalises to any long, structured document where internal consistency and cross-referencing against a requirement set is the review task — annual reports, offering circulars, regulatory submissions. What changes between them is the requirement set and the extraction schema, not the architecture.
What this was built on.
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