Legal reads every incoming contract end-to-end. Spotting deviations from the playbook takes ~4 hr per MSA. Legal is the bottleneck.
The AI approach
LLM parses each clause; vector search matches it to the closest playbook position; risk-scored deviations come back with suggested fallback language.
The outcome
4 hr → 25 min
First-pass redline in minutes. Legal focuses on the few clauses that need judgement.
Try itInput → Process → Output
Input — Incoming MSA
MSA — Vendor Xv2 · 14 pages
§ 4 · TermThis Agreement shall be effective for 12 months from the Effective Date.
§ 7 · Limitation of liability"Liability under this Agreement shall not exceed USD 10,000 in the aggregate, regardless of cause."
§ 9 · Governing lawSingapore law shall govern, with disputes settled by SIAC arbitration.
§ 11 · Data protectionVendor will process personal data per PDPA and Customer DPA Annex 1.
Process — AI pipeline
1Parse + segment clausesReadPredictive
2Match to playbook (vector)ReadSymbolic
3Flag deviations + risk scoreWriteGenerative
4Draft fallback languageWriteGenerative
Output — Redline pack
Click Run demo to flag deviations and propose fallback clauses.
CLAUSE REDLINE§ 7 · LoL
Risk · liability capHIGH ▲
As proposed"… shall not exceed USD 10,000 in the aggregate"Playbook standard"… cap = greater of 12 months feesorUSD 250,000, excluding IP indemnity"
Suggested fallback
"Liability cap shall be the greater of (a) fees paid in the 12 months prior; or (b) USD 250,000. The cap shall not apply to IP indemnity, confidentiality breach, or wilful misconduct."
Three AI types in this use case
SymbolicPlaybook database (clause → standard position → fallback ladder); risk-tier rules; redline output format (track-changes).
PredictiveClause segmentation, type classifier (liability, IP, term, gov-law), similarity ranking against playbook embeddings.
GenerativeLLM compares each clause to the playbook position, explains the deviation, drafts fallback language in the playbook voice.