Kaia Legal
End-to-end eDiscovery intelligence that learns from every review.
From document intake through post-litigation analytics, Kaia Legal manages the entire eDiscovery lifecycle with AI confidence scoring. Every human correction makes the system measurably better — with full audit trails for court admissibility.
Built for Your Role
What Kaia Legal does for you.
Every role gets a purpose-built operating surface. Not a generic dashboard — a workspace designed for how you actually work in Legal.
Reduce review costs by 40%+ while improving privilege accuracy
See case strategy, cost tracking, and review completion across all matters. Approve escalations and track ROI.
Review documents 3x faster with AI-ranked priority queues
AI surfaces the highest-risk documents first. Every correction you make trains the model for everyone.
Process 10x more documents with the same team
Track batch throughput, SLA compliance, and team productivity across all active matters.
Full EDRM audit trail from collection through production
Every classification, review decision, and correction is logged with timestamp and reasoning.
After signup, you choose your role and land directly in your Kaia Legal workspace.
Full Lifecycle
Every stage of eDiscovery, powered by intelligence.
Intake
Document collection, custodian management, and preservation hold tracking.
Classification
AI-powered relevance and privilege classification with confidence scoring.
Review
Priority-ranked review queue with split-panel document inspection.
Production
Automated redaction, Bates stamping, and production set management.
Post-Litigation
Analytics on review patterns, cost tracking, and model improvement metrics.
Classification
Four categories. One confidence score. Full reasoning.
Highly relevant — key facts, admissions, smoking-gun content requiring immediate attorney review.
Potentially relevant — contextual information, background discussions supporting the case.
Not relevant — routine communications, administrative content that can be deprioritized.
Attorney-client privilege or work product — flagged for privilege review regardless of relevance.
The Learning Loop
Every correction makes the system smarter.
AI Classifies
Upload a document. The Intelligence Engine analyzes it and returns a classification with confidence score and detailed reasoning.
Human Corrects
Certified practitioners review classifications. If the AI got it wrong, they correct it — selecting the right answer and explaining why.
System Learns
Each correction creates a training data pair routed to the right learning layer. The model improves, measurably, every month.
Intelligence Engine
Every correction flows through the five-layer continuous learning system — from prompt fixes ($0.03, instant) to architecture evolution (monthly, human-required). The triage system routes each correction to the right learning mechanism automatically.
See the full Intelligence Engine architecture →Legal Differentiator
Court-admissible confidence.
In litigation, AI-assisted document review must meet the Daubert standard for scientific evidence. Kaia Legal is built from the ground up to satisfy that bar.
RRES
Target Benchmarks
Routing & Resolution Efficiency Score targets per vertical. Instrumentation in progress — auditable publication after production baselining.
0.85
Confidence Threshold
Every classification below 0.85 confidence triggers mandatory human review. No automated decisions on ambiguous documents.
1st
First Vertical to Market
Kaia Legal is our flagship vertical — deepest model training, longest learning history, highest benchmark scores.
Same Intelligence Engine · 8 Regulated Industries
See it work. Right now.
Classify a legal document. Correct the AI. Watch the learning loop in real time.
Kaia Legal Workflow
9-Stage Industry Process
Only eDiscovery platform targeting auditable privilege detection accuracy, TAR precision, and cost-per-page metrics trending month-over-month. Every client correction improves the model for all clients.
Identification
Data source mapping, custodian interviews, scope definition
FRCP Rule 26(f) — meet and confer obligation
Preservation
Legal hold notices, collection verification, chain of custody
FRCP Rule 37(e) — spoliation sanctions
Collection
S3 ingestion, batch upload, metadata preservation
Processing
Text extraction, deduplication, language detection, quality scoring
Review
AI classification (HOT/WARM/COLD/PRIVILEGED) + human review
FRE Rule 502 — privilege safe harbor
Analysis
Pattern detection, key facts, privilege clusters, entity extraction, timeline construction
Production
Bates numbering, load file export (EDRM XML/DAT), redaction
FRCP Rule 34 — production requirements
Presentation
Case strategy summaries, timeline visualization, key document highlights
Post-Litigation Intelligence
Knowledge capture, precedent database, cross-matter learning
Transparent Benchmark
Privilege Detection Accuracy
Target: 95% | Industry average: 87%
No competitor in Legal publishes this data.