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 works in production
Conflict checks are the legal AI workflow 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 an evaluation harness tighter than any other deployment in the firm.
Our conflict-check deployment 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 is logged.
The Casetrack retrospective has the long version of why this 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 it 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 a mandatory human-in-the-loop checkpoint, for the workflow where errors meet client trust.
Ready to deploy conflict check?
Start with the AI Readiness Audit. Two weeks, $15,000. We will scope this workflow (and any others) against your firm specifically.