01
Section-aware, not text-aware
The slip is mapped to a section model before comparison, so a change is reported against the clause it belongs to rather than against a line number.
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Baseline-versus-renewal slip review as a structured, auditable analysis — financial deltas, coverage drift, missing sections and risk implications, each cited back to the source PDF.
The reality
The work is not difficult. It is long, repetitive and unforgiving of a lapse in attention on page 41 — which is precisely the profile of work that should not be done by a senior underwriter at eleven at night.
Generic document comparison tools diff text. They do not know that a change to the definition of Insured Person carries more weight than a change to the broker’s address.
Large language models will summarise a slip willingly, and will just as willingly summarise a section that was not there.
What renewal review needs is section-aware comparison with severity attached to every finding and a citation behind it.
How it delivers value
01
The slip is mapped to a section model before comparison, so a change is reported against the clause it belongs to rather than against a line number.
02
Every finding is classified high, medium or low risk, so a 62-item change list becomes a 20-item review list and a 42-item record.
03
Each finding carries the page and section it came from in both documents. Nothing has to be taken on trust, which is the only basis on which it gets used.
04
Baseline and renewal are identified from the period-of-insurance dates rather than from the filename, so the comparison never runs the wrong way round.
Capabilities
The approach
Written for the technical evaluator rather than the buyer. If you are going to put this in front of an architecture review board, this is the section they will read.
Text extraction runs first, then each document is mapped to the section model for its class of business. Comparison happens between mapped sections, which is what makes a finding meaningful rather than positional.
All sections are analysed in parallel, then a reduce step classifies and de-duplicates findings across the whole document pair. Flags raised in the map phase are confirmed or withdrawn in the reduce phase — not before.
Each class of business carries its own versioned prompt template. Versions are promoted explicitly to production, so a change to marine wording analysis cannot silently alter the D&O results.
Long-running comparisons stream their state — sections completed, elapsed time, signals raised — so the reviewer can start reading findings before the job ends.
Documents are held encrypted and the deployment runs inside your own Azure tenancy. Slips do not leave your boundary to be compared.
The breakthrough
Median and count figures are measured in production. Time-saving figures are expressed as ranges because they depend on your document set.