Change Order & Claims Intelligence Pro

Best for

Strongest evaluation contexts.

• Commercial construction teams reviewing change-event records before internal claim-readiness decisions.
• Defects and issues management platforms where the downstream chain runs from issue/defect to cost impact, change event and variation/claim readiness.
• ERP and contract-control platforms evaluating structured change, evidence and open-action workflows.
• System integrators assessing a review-ready source-package offer for contract administration environments.

Disqualifiers / not for

Where this should not be forced.

Visible boundaries help unfit buyers self-select out.

• Buyers seeking legal advice, entitlement assessment or arbitration support.
• Teams expecting automatic claim validity, guaranteed recovery or legal outcome predictions.
• Organizations requiring public source-package access, live client data or production proof from the public website.

Deterministic core

Workflow logic first.

Claims Pro controlled-pilot AI review path: deterministic-first Claims Pro with eval harness evidence for extraction, structuring and analogous case search pending controlled technical review.

• Evidence completeness.
• Evidence grouping.
• Timeline reconstruction support.
• Open-action and review-state tracking.

Signal layer

Construction signals made reviewable.

• Evidence intelligence support.
• Missing-evidence signals.
• Contract clause citation signals.
• Notice, date and evidence-reference signals.
• Commercial impact and open-action signals.
• Timeline gap and sequencing signals.

ML-assisted candidates

Candidate patterns only.

ML-assisted candidates depend on buyer data, labels, evaluation design and governance.

• Contract clause extraction with verifiable citation evaluation path.
• Change event narrative structuring evaluation path.
• Analogous case semantic search evaluation path.
• Audit-log design for AI calls under controlled technical review.
• Change-event classification candidate.
• Evidence category candidate.

Buyer validation requirements

Buyer-side validation required.

• Confirm whether the reviewed bundle is eval_harness_only or implementation_and_eval_present before using AI-augmented implementation language.
• Validate extracted clauses against source documents and citation references.
• Validate precision/recall by clause type, macro F1, narrative parsing agreement and Recall@K for analogous search as methodology validation; buyer-side validation required.
• Confirm buyer-curated goldens or agreed evaluation cases before buyer-specific validation.
• Confirm service-mode evaluation scope separately from eval harness methodology validation.
• Confirm model card, prompt version, model id, input hash and known failure mode logging expectations.
• Confirm the workflow supports decision review only and does not replace legal governance.

Buyer-data-dependent ML

Data and labels shape any evaluated pattern.

• Clause extraction evaluation depends on buyer-approved contract documents, clause labels and citation review.
• Narrative structuring depends on buyer-approved change event examples and reviewer agreement criteria.
• Analogous case semantic search depends on indexable case summaries, permissions and Recall@K evaluation; buyer-side validation required.
• Provider/model behavior is measured separately through service-mode evaluation available during controlled technical review.
• Timeline support depends on reliable dates, source references and buyer workflow semantics.

Governance boundary

Human-in-the-loop review.

• Traceable evidence references and review-state audit notes.
• Every AI output is logged with prompt version, model id and input hash where AI is enabled.
• AI assists extraction, structuring and search. It does not predict claim outcome, provide legal advice, determine entitlement or guarantee recovery. Human verification and buyer-side validation are required.
• AI-augmented positioning requires controlled technical review.
• Public site does not claim production validation or public production model performance.
• Human-in-the-loop review before any claim or commercial action.
• No legal advice, no claim-validity determination and no guaranteed claim recovery.

Not claimed

Public ML and outcome limits.

• Commercial, contract or buyer-side reviewers retain responsibility for interpreting evidence and events.
• Human review required before claim, notice, entitlement, commercial or legal action.
• No legal advice.
• No claim-validity determination.
• No legal entitlement assessment.
• No claim outcome prediction.
• No guaranteed claim recovery or legal outcome.