Role: Lead Product Designer
Timeline: 7 Months
Company: Wolters Kluwer
Product Category: Enterprise B2B SaaS | LegalTech Platform
Core Team: Lead Designer (myself), 1 UX Researcher, 1 Engineer, and 1 Product Manager
Product Overview
As part of LegalCollaborator’s evolution, we introduced Generative AI-powered summarization and analysis tools that enhance how corporate legal teams evaluate law-firm proposals.
The system uses natural language processing (NLP) and machine learning to synthesize key proposal details — highlighting alignment to RFP criteria, surfacing differentiators, and predicting success likelihood.
This innovation transforms complex RFP evaluations into data-driven, insight-rich decisions that reduce manual review time and strengthen transparency.
The Objective
Empower legal departments to:
Automate the comparison of law-firm proposals using Gen AI summarization and contextual analysis
Accelerate decision-making through AI-driven scoring, predictive insights, and visual analytics
Improve accuracy and consistency in law-firm evaluations
Enhance collaboration between legal, procurement, and finance teams through transparent data presentation
Key Features & Enhancements
AI-Generated Proposal Summaries
Automatically condenses lengthy proposals into concise, comparable summaries.
Highlights scope alignment, methodology, pricing model, and differentiators.
Integrates human-in-the-loop review to verify critical details.
Impact: Reduces evaluation time by 40% while maintaining accuracy and auditability.
Comparative Analysis & Scoring
Evaluates proposals against predefined RFP criteria (e.g., pricing, expertise, risk profile, D&I metrics).
Scores proposals based on both quantitative (cost, timeline) and qualitative (experience, approach) data.
Impact: Enables fair, consistent, and evidence-based selection decisions.
Predictive Insights & Success Forecasting
Uses historical engagement data to forecast likelihood of project success for each firm.
Highlights patterns in efficiency, budget adherence, and outcomes.
Impact: Provides forward-looking insights to reduce risk and strengthen strategic partner selection.
Contextual Knowledge Retrieval
Pulls relevant information from past matters, billing data, and industry benchmarks to inform recommendations.
Surfaces similar case references and performance patterns.
Impact: Adds depth to AI summaries by combining historic and current context.
Visual Decision Dashboards
Displays AI-generated summaries, comparative scores, and predicted outcomes in an interactive dashboard.
Includes confidence indicators and traceable source links for auditability.
Impact: Improves transparency and stakeholder trust in AI-assisted decisions.
Use Cases
Managing Attorneys
Review AI summaries to quickly identify the best fit law firm based on expertise and pricing efficiency.
Legal Operations Managers
Use predictive insights to forecast budget impact and optimize firm allocations.
Procurement & Finance Teams
Validate pricing and efficiency metrics through AI-generated comparative data and visual dashboards.
Current Impact
Smarter Decisions
AI summaries and scoring models deliver data-backed clarity, helping legal teams choose confidently.
Increased Efficiency
Manual review time reduced by up to 40%, accelerating RFP turnaround and response cycles.
Greater Transparency
Built-in traceability ensures every AI insight is explainable, auditable, and trusted.
Enhanced Collaboration
Cross-functional teams align faster with unified insights and shared visibility.
