Legal Services · Mid-sized legal services firm
80% less manual effort in contract review with attorney-in-the-loop AI
Engineered targets
reduction in manual review effort
review throughput per team
clause-extraction accuracy target
Representative engagements illustrating INFIQON's delivery approach. Client names are withheld; figures illustrate the outcomes these solution patterns are engineered to achieve.
The challenge
Review teams read every contract line by line to extract parties, terms, obligations, and risk positions into checklists — skilled work at the edges, mechanical work in the middle.
Turnaround targets forced a trade-off between speed and thoroughness, and scaling meant hiring ahead of demand in a tight market for experienced reviewers.
Our approach
- Built a clause and entity extraction pipeline over the firm's playbooks and historical reviews
- Encoded the firm's risk positions so deviations from preferred language are flagged automatically
- Assisted redlining: suggested edits from the playbook, applied only on attorney approval
- Structured review workspace replacing spreadsheets — extraction, flags, and documents in one screen
- Measured quality against senior-reviewer baselines before expanding scope
The solution
The system reads incoming contracts, extracts the fields the checklist needs, and flags clauses that deviate from the firm's positions — indemnity caps, termination rights, data-protection terms — with the exact source text linked.
Attorneys start from a structured risk summary instead of a blank page. The mechanical layer is automated; the judgment layer is faster because everything it needs is already on screen.
Facing a similar challenge?
We'll walk you through how this pattern would apply to your systems, data, and controls — and what results to expect.