Enterprise LLM Layer for Legal Research & Discovery

Shipping Impact: Faster discovery cycles, consistent synthesis, audit-ready outputs

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AI Transformation
LLM
Semantic Retrieval
Knowledge Assist
Governance
See it in action

Executive Summary

NeuSix partnered with a legal organization handling complex, document-heavy matters to deliver an Enterprise LLM Layer that transforms legal research and discovery from keyword-only workflows into governed, evidence-linked intelligence.
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The solution enables teams to ask questions in natural language, retrieve relevant evidence across massive corpuses, and generate structured outputs—while maintaining strong governance, traceability, and access control.
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Outcome Highlights (representative)

  • Enabled research and synthesis across 1,254 documents totaling 4.2M pages
  • Evidence summaries generated with 98% confidence (internal scoring)
  • Faster path from question → evidence → structured summary
  • Improved defensibility through source-linked answers and auditability

Outcome Highlights

Enabled search and synthesis across 1,254 documents and 4.2M pages

Evidence summaries generated with 98% confidence (internal scoring)

More consistent structure and formatting of summaries across teams

Reduced analyst time spent on repetitive search and synthesis

Faster path from question → evidence → structured summary → export

Source-linked answers (citations + audit trail)

Strong governance to support confidentiality and compliance expectations

Client Context

The client’s teams routinely work with large collections of legal documents and case materials. Their outcomes depend on:
  • Speed of finding relevant evidence
  • Consistency of summaries and reasoning
  • Confidentiality and access control
  • Auditability and defensibility of outputs
  • Repeatability across matters and teams

They needed an AI solution that was practical, explainable, and safe—not a generic chatbot.

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The Challenge

Traditional workflows were slowing down research and increasing effort.

Key bottlenecks

  • Keyword-only search limitations: Relevant evidence was missed due to phrasing mismatch, inconsistent tagging, and limited semantic understanding.
  • Slow synthesis + variability in output quality: Analysts manually summarized findings, leading to uneven depth and formatting.
  • Low trust without traceability: Legal workflows require “show me the evidence,” not answers without citations.
  • Governance constraints: Confidentiality, role-based access, and audit trails are mandatory.
  • High cost of repetition: Similar questions were repeatedly researched across matters, consuming analyst bandwidth.

What NeuSix Shipped

NeuSix shipped an Enterprise LLM Layer designed for legal research and discovery—built to be evidence-first, governed, and scalable.

Retrieval Foundation (RAG Core)

  • Semantic retrieval across unstructured and structured sources
  • Chunking and metadata strategy aligned to legal use cases
  • Evidence-first response grounding (answers tied to sources)

Governed LLM Reasoning Layer

  • Responses constrained by retrieved evidence
  • Structured output templates (summaries, issue outlines, timelines)
  • Confidence scoring (internal) and consistency checks

Trust, Security & Governance

  • Role-based access controls (zero-trust style)
  • Audit trail for query → retrieval → response → cited sources
  • Guardrails for safe behavior and controlled outputs

Analyst Workflow Enablement

  • Natural-language Q&A interface
  • “Find → Cite → Summarize → Export” workflows
  • Reusable prompt patterns aligned to legal tasks

Harnessing AI to create relevance, not just competence

How It Works (Explainable by Design)

A cockpit view provides real-time measurement of processing times, queue health, and corrective action effectiveness.

Stage 4:

Measure and Improve (Transparency)

The system retrieves relevant passages using semantic search and metadata filters across the entire corpus.

Stage 1

Retrieve

The LLM generates outputs only using retrieved evidence, reducing hallucination risk and
increasing defensibility.

Stage 2

Ground

Outputs follow legal-friendly formats:

  • Evidence summaries
  • Issue-based outlines
  • Timeline extraction
  • Consistency/contradiction checks (where applicable)

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Stage 3

Structure

Every interaction is controlled and auditable:

  • Who accessed what
  • What was retrieved
  • What was generated
  • What sources were cited

Stage 4

Govern

Outcomes (What Changed)

Scale & Coverage

  • Enabled search and synthesis across 1,254 documents and 4.2M pages

Quality & Confidence

  • Evidence summaries generated with 98% confidence (internal scoring)
  • More consistent structure and formatting of summaries across teams

Productivity

  • Reduced analyst time spent on repetitive search and synthesis
  • Faster path from question → evidence → structured summary → export

Trust & Defensibility

  • Source-linked answers (citations + audit trail)
  • Strong governance to support confidentiality and compliance expectations

What We Measured

Coverage & retrieval quality:

precision, recall (internal evaluation), citation rate

User productivity:

time-to-first-evidence, time-to-summary, repeat query reduction

Trust & safety:

access policy compliance, audit completeness, controlled output adherence

Adoption:

active users, repeat usage, workflow completion rates

Governance, Trust & Reliability

This was shipped as a governance-first system (not a “general-purpose chatbot”):

  • Role-based access controls aligned to confidentiality
  • Evidence-only grounded generation with citations
  • Auditability of all outputs
  • Controlled templates for consistent, review-friendly formats
  • Human review pathways for critical outputs

Case studies

Healthcare & Life Sciences

AI-Powered Patient Growth & Lifetime Intelligence

Outcome Highlights:

2.8x
ROAS achieved
28%
Conversion increase
22%
CAC reduction
18+%   FTE saved
75%   downtime reduction
97+%   classification accuracy
55–60 units/min throughput

Airlines / Aviation

Digital Passenger Experience Transformation (Biometrics + Digital Twin + Predictive AI)

Outcome Highlights:

35–40%
reduction in processing time (biometric flow)
Passenger authenticated in less than 3 seconds
18+%   FTE saved
75%   downtime reduction
97+%   classification accuracy
55–60 units/min throughput

Retail & Consumer (Travel Retail)

AI‑Powered Travel Retail Transformation (Personalization + CV + Inventory Intelligence)

Outcome Highlights:

Real‑time analytics for campaign and store execution
Personalized recommendations with 85–96% match (model scoring)
18+%   FTE saved
75%   downtime reduction
97+%   classification accuracy
55–60 units/min throughput

Logistics & Ports

AI‑Driven Port Control Tower

Outcome Highlights:

30–40%
dwell time reduction
25%
faster exception resolution
Double‑digit demurrage reduction
18+%   FTE saved
75%   downtime reduction
97+%   classification accuracy
55–60 units/min throughput

Manufacturing & Industrial

FMCG Food Manufacturing Automation (Robotics + Vision + Digital Twin)

Outcome Highlights:

97%+
classification accuracy
75%
downtime reduction
18+
FTE saved
55–60 units/min throughput
18+%   FTE saved
75%   downtime reduction
97+%   classification accuracy
55–60 units/min throughput
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