Conflict Check Resolution
AI-assisted conflict resolution with normalized entities, time-decay weighting, and a paper trail attorneys can defend.
Outcomes
- Conflicts team capacity expanded 2–3x without headcount addition
- Entity-normalization false-negatives materially reduced on cross-border work
- Defensible audit trail every conflict decision (malpractice insurance carriers like this)
- Conflict-resolution cycle time compressed measurably
How this surface works in production
Conflict checks are the legal-vertical AI surface where attorney trust is hardest to win and easiest to lose. A single false-negative conflict (engagement undertaken that should have been declined) is a malpractice exposure the firm cannot insure away. A single false-positive (engagement declined that could have been accepted with a waiver) is revenue lost and client relationship damaged. The cost asymmetry means conflict-check AI must be deployed with eval discipline that exceeds every other surface in the firm.
Our conflict-check surface does not run automated conflict approvals. It surfaces ambiguous matches to the conflicts team with three things the team needs and rarely gets: normalized entity matches across spelling variants and corporate entity hierarchies, time-decay weighting so a 2018 engagement is not weighted equivalently to one this quarter, and a recommended resolution path with the reasoning trace that the recommendation rests on. The conflicts attorney accepts the recommendation, overrides it, or escalates — every decision logged.
The Casetrack retrospective has the long version of why this surface is harder than it looks. The short version: entity normalization fails silently on non-Latin script names; time-decay is critical and rarely implemented; and showing your work to the conflicts attorney is the difference between adoption and shadow-IT-ing the entire system. Our deployments ship with all three.
What this surface does
Normalized entity matching
Cross-spelling-variant, cross-script, and cross-corporate-entity-hierarchy entity matching. Pakistani, Chinese, Arabic, Cyrillic, and other non-Latin scripts handled correctly.
Time-decay weighting
Attorney-tunable time-decay parameters per practice group — older conflicts weighted lower than recent ones, with explicit weighting curves the conflicts team can adjust.
Lineage metadata
Every match carries the audit-quality lineage trace: which database, which matter, which engagement, what relationship to the proposed work.
Recommended-resolution surface
AI recommends accept / decline / waiver-required / escalate-to-partner with the reasoning trace; conflicts attorney clicks one of three buttons.
Audit log
Every conflict-check decision logged immutably for the duration of the firm's matter retention requirement.
Architecture, in plain English
Foundation model: hybrid — fine-tuned classifier for the entity-matching layer (script-aware), Claude for the resolution-recommendation reasoning layer. Retrieval: graph database over the firm's relationship-and-matter history with edge-weighting for relationship strength and recency. Agent orchestration: deterministic decision pipeline (match → time-decay → relationship-graph → recommend → HITL). Observability: per-firm-tuned threshold reporting with attorney-graded override-rate tracking.
Conflict Check — frequently asked
Other agent templates
The four templates that generalize across mid-market law firms.
Intake Triage Agent
Inbound queries routed to the right practice group with conflicts pre-flagged in under 30 seconds.
Doc Review Assist
First-pass review of contracts, due diligence packages, and discovery documents with clause-level citation provenance.
Billing Reconciliation
AI-drafted billing entries with mandatory human-in-the-loop checkpoint, eval-disciplined for the surface where errors meet client trust.
Ready to deploy conflict check?
Start with the AI Readiness Audit — 2 weeks, $15K, founder-led. We will scope this surface (and any others) against your firm specifically.