Key takeaways
- Machine-assisted classification makes proportionate review possible on mid-size claims.
- Structure first: an indexed, deduplicated, searchable corpus is the prerequisite.
- Human expert judgement still determines what the documents mean.
The proportionality problem
Construction disputes generate extraordinary document volumes: daily reports, RFIs, transmittals, minutes, photographs, schedule updates, invoices and years of email. On a mid-size claim, manual review of that corpus can cost more than the amount in dispute.
The result is that meritorious claims get abandoned for reasons of arithmetic rather than merit.
Build the infrastructure first
Data analytics is about creating an infrastructure to collect, analyse and report data in an organised manner. Before any model touches the corpus, we normalise formats, deduplicate, OCR what needs it, and index by date, author, project entity and work area.
- Normalisation, deduplication and OCR across mixed-format records
- Entity and timeline extraction to build a chronology automatically
- Intelligent search and content analytics across the whole corpus
- Classification against the specific issues in dispute
Where judgement stays human
Tooling narrows a corpus and builds a chronology. It does not determine whether a variation instruction was validly issued, or whether a delay was on the critical path. Those conclusions come from experienced construction professionals who understand the contract and the methodology.
Used this way, AI and big data tooling do not replace expert analysis — they make it affordable on projects that could not previously fund it.
Written by Elite Analytics Data Practice. For advice on a specific project or claim, get in touch.
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