The challenge
Cascade's contract diligence practice was capacity-bound. A typical mid-market deal data room contained 3,000–8,000 contracts that the diligence team needed to summarize: counterparties, term lengths, change-of-control clauses, exclusivity, IP assignment, governing law. Manual review took 3 weeks per deal and capped the firm's deal capacity at roughly six concurrent engagements.
The approach
Cascade configured fluex with a custom contract schema covering 47 deal-relevant clause types. Documents from the deal room were processed through fluex's async API; extractions were piped into Cascade's internal diligence platform with full audit trail and source-citation page numbers preserved. Attorneys reviewed only the low-confidence extractions and the highest-risk clauses (change-of-control, exclusivity, IP).
The outcome
Diligence cycle time dropped from 3 weeks to 8 hours for a typical mid-market deal. Cascade ran 18 concurrent engagements in Q1 2026 vs. their previous ceiling of 6. Field-level disagreement against attorney review averaged 0.4% — well within the firm's quality bar — and disagreements concentrated in narrow clause types where Cascade is now adding active-learning examples. Incremental revenue from new deal volume in the first six months covered 6x the platform cost.
What this proves
Case studies don't generalize perfectly — every customer's volume, document mix, and compliance environment is different. But the architectural pattern repeats: replace the data-entry layer with a structured-extraction API, route the genuinely uncertain cases to humans, preserve a defensible audit trail, and the constraint shifts from headcount to judgment. That's the offer fluex makes; the metrics here are one shape of what it looks like in production.
For a side-by-side evaluation against your current workflow with your real documents, talk to our team. For pricing, see pricing.