Contract and Legal Document Analysis
Extraction, comparison and obligation tracking across a contract portfolio, cited back to the exact clause, built so a lawyer verifies in seconds rather than trusting a summary.
Legal teams are right to be sceptical of automated contract review, and the products that earn their trust share one property: they make verification faster rather than asking for it to be skipped. Every extraction points at the clause it came from, and the lawyer decides.
Contract and legal document analysis is the automated extraction of clauses, terms, parties, dates and obligations from agreements, together with comparison against standard positions, identification of deviations and unusual terms, and tracking of the obligations and dates the contract creates.
Three jobs, in increasing order of value
- Find and extract. Locate clause types, parties, dates, values and defined terms across a portfolio nobody has read in years. Turns an unknown estate into a queryable one.
- Compare against your position. Flag where a clause deviates from your standard playbook, and by how much. This is where review time is actually saved, because the lawyer reads the exceptions rather than the whole document.
- Track what the contract obliges you to do. Notice periods, renewal dates, reporting duties, service levels and change-of-control triggers, surfaced before they matter rather than after.
The third is where most of the commercial value sits and the one most often left out of scope, because it requires the output to reach an operational system rather than a legal one.
How we build it
Encode the playbook with your lawyers
Standard positions, acceptable fallbacks and unacceptable terms, per clause type, written down. Most legal teams have this partly in a document and mostly in the heads of two senior people, and extracting it is a valuable exercise regardless of what gets automated.
Extract with citation, always
Every extracted clause, date and obligation points at its exact location in the document. A lawyer confirms in one click. This single property is what determines whether legal teams adopt these systems or quietly stop using them.
Handle the reality of real contracts
Scanned PDFs, amendments that alter a clause three documents later, schedules and annexes, defined terms used inconsistently, and the negotiated version that differs from the executed one. Where documents are scanned, extraction sits on top of intelligent document processing.
Flag deviation, do not judge it
The system reports that the liability cap is mutual where your standard is one-way, and shows both texts. It does not advise. That boundary is deliberate: it keeps the tool useful to lawyers rather than putting it in a position it cannot defend.
Route obligations to the people who must meet them
Notice deadlines, reporting duties and renewal dates delivered into calendars, ticketing or the contract management system, owned by a named person. An obligation register nobody is alerted from is a document, not a control.
Measure against your own lawyers
Extraction and deviation flagging are scored against a set your legal team has reviewed, with per-clause-type accuracy. Recall matters more than precision here: a missed unusual indemnity is far more costly than an unnecessary flag.
Recall over precision, deliberately
We tune these systems to over-flag rather than under-flag, because the cost of a lawyer dismissing an unnecessary flag is seconds and the cost of missing an unusual term is measured in litigation. That trade is agreed explicitly with your legal team and reported at the operating point.
What it changes
| Task | Before | After |
|---|---|---|
| Reviewing an incoming contract | Full read, clause by clause | Read the flagged deviations, verify each in one click |
| Portfolio questions | Manual search through a drive | Query across extracted clauses with citations |
| Due diligence | Weeks of reading, sampled | Full coverage, exceptions surfaced for review |
| Renewal and notice management | Spreadsheet, maintained by hand | Obligations routed to owners with reminders |
| Regulatory change impact | Which contracts contain this? Nobody knows | Query the clause corpus, get the list with sources |
How the engagement runs
The playbook is encoded with your lawyers first; it is what makes deviation flagging possible.
Playbook and clause taxonomy
Standard positions, fallbacks and unacceptable terms encoded per clause type with your legal team.
Extraction
Clause, party, date and value extraction with citation to source, handling amendments, schedules and scanned documents.
Deviation and obligation logic
Comparison against the playbook, deviation flagging tuned for recall, obligation identification with dates and owners.
Delivery and routing
Review interface with source highlighting, obligations routed into calendars or contract management, portfolio query built.
Legal evaluation and handover
Accuracy scored against your lawyers' review set, then handover with the playbook maintenance process.
What you receive
Faster review, a queryable portfolio, and obligations that reach the people who must meet them.
Encoded playbook
Standard positions, acceptable fallbacks and unacceptable terms per clause type, maintainable by your team.
Extraction system
Clauses, parties, dates and values with citation to exact location, across scanned and digital documents.
Deviation flagging
Comparison against the playbook, tuned for recall, with both texts shown side by side.
Obligation register
Dates, duties and owners routed into calendars, ticketing or contract management.
Portfolio query
Search across extracted clauses with citations, for regulatory and diligence questions.
Accuracy report
Per clause type against your lawyers' review set, with the recall and precision trade stated.
Is this the right engagement?
Worth being direct. Contract and Legal Document Analysis is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- You hold a contract portfolio nobody has read end to end.
- Legal can encode standard positions and review a scored sample.
- Review time is a genuine bottleneck on commercial activity.
- Obligations are currently tracked in a spreadsheet, or not at all.
- Citation-based verification is acceptable and expected.
Choose something else if
- The intention is to remove lawyers from review rather than speed them up.
- The portfolio is small enough to read.
- No standard positions exist and nobody will write them.
- The documents are unstructured correspondence rather than agreements.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
Will this replace our legal review?
No, and any supplier claiming otherwise should be treated carefully. It reads the whole document, extracts what it finds with citations, and flags where terms deviate from your standard positions. Your lawyer reads the exceptions and decides, which is faster than reading everything and safer than reading nothing.
How accurate is automated clause extraction?
Good on common clause types in well-drafted agreements, weaker on unusual drafting, heavily amended documents and poor scans. We report accuracy per clause type against a set your own lawyers reviewed, rather than a single figure, because the variation between clause types is large.
Can it handle amendments and schedules?
It has to, because most portfolios are full of them. Amendments that alter a clause in an earlier document, schedules that contain the commercial terms and inconsistent defined terms are all normal, and handling them is a substantial part of the build.
Does it give legal advice?
No. It reports that a term deviates from your standard position and shows both texts. It does not opine on whether to accept it. That boundary keeps the tool useful to lawyers and keeps it out of a position it cannot defend.
What is the biggest practical benefit?
Usually obligation tracking rather than review speed. Notice periods, renewal dates and reporting duties surfaced to named owners before they matter prevents the expensive surprises, and it is the part most often left out of scope.
Often paired with this
Most clients combine two or three engagements from the Natural Language Processing pillar. These are the ones that most often run immediately before or after.
Document Summarisation
Summaries written for a named reader and decision, checked for faithfulness against the source.
Read more →Text Classification and Entity Extraction
Labels and entities extracted from text at volume, with a scheme your experts agree on and per-class accuracy.
Read more →Knowledge Graph Construction
A graph built around the questions you cannot answer today, with entity resolution and provenance per fact.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.