The Client: STP One

Founded in 1994, STP One initially provided an Insolvency Management Solution to help law firms manage processes and communication with debtors during bankruptcy. Expanding in 2012 with its first Law Firm Management Solution, STP One has since grown into a prominent player in the legal tech industry. With over 350 employees and 29 products at various maturity stages, the company is now focusing on AI to further enhance its offerings and drive innovation in the legal sector.

‍

The Mission: Interim Product Manager

STP One's product team required interim product management expertise due to the exit of the previous product manager, creating a temporary vacancy. As an interim PM, we took over the Legal Twin (AI Agent) and Smart Data products, aimed at enhancing lawyers' case management capabilities through AI and data APIs. The mission was to establish a smoother product transition, ensure alignment with company goals, and tackle key operational challenges identified in the absence of a dedicated PM.

The team, structured as a "Product Trio" consisting of a Product Owner, Tech Lead, and Product Manager, faced challenges such as a lack of documentation, minimal data tracking, and the absence of a clear roadmap. These issues significantly hindered prioritization and effective decision-making.

The goal was to bridge the product management gap by refining customer onboarding processes, conducting in-depth product discovery, and addressing legal and compliance requirements within the Smart Data platform.

‍

Our Main Quest: Enhancing an AI Product for Faster Case Analysis and Increased Lawyer Productivity

Explore and Conquer: Solved for the Client

Our mission focused on solving a range of issues, from enhancing AI-assisted client engagement tools to streamlining data management for legal processes.

🤖 Legal Twin: An AI-powered product designed to revolutionize case management for lawyers by providing quick overviews of case content and enhancing efficiency. Evolving into a full-fledged AI agent, Legal Twin aimed to answer legal questions, conduct legal research, and deliver intelligent insights, empowering legal professionals to work smarter and faster.📚 Smart Data: An API service that supplies legal firms with critical data on business and insolvency cases. To enhance Legal Twin’s capabilities, we also worked on developing a legal knowledge base using a Retrieval-Augmented Generation (RAG) model, enabling more accurate and context-aware AI responses.

‍

Initiative 1 - Refining and Delivering "Ask Your Case" AI Feature

Problem:

The newly introduced "Ask Your Case" feature, designed to allow lawyers to ask specific questions about the cases and documents they uploaded, lacked refinement. This impacted its usability and effectiveness in providing meaningful assistance to legal professionals during case inquiries.

What has been done:

We collaborated closely with Product Design to develop an intuitive user interface, ensuring the feature was user-friendly and visually aligned with lawyer workflows. Additionally, we assisted the Product Owner during the delivery phase, ensuring smooth implementation and alignment with technical goals. To drive adoption, we presented the feature to sales and customer success teams, equipping them with the knowledge to communicate its benefits effectively. These efforts optimized the AI’s ability to provide accurate, contextually relevant responses, significantly enhancing user experience and engagement.

‍

Initiative 2 - Product Discovery for Legal Twin (AI Agent)

Problem:

A lack of a clear roadmap to achieve the Legal Twin vision, coupled with limited visibility into potential use cases and user pain points, restricted the product’s growth and development opportunities.

What has been done:

We initiated a comprehensive product discovery phase to address these gaps. Collaborating closely with the sales team, we identified key customers and conducted in-depth interviews to uncover pain points, needs, and potential use cases. These insights informed the creation of an Opportunity Solution Tree, mapping out viable future developments and providing a structured framework for prioritization. This effort clarified the product’s direction, aligning it with user needs and market demands, while laying the groundwork for a compelling roadmap and long-term vision.

‍

Initiative 3 - Customer Onboarding Process Overhaul

Problem:

The onboarding process was fragmented and inconsistent, resulting in customer frustration, lower satisfaction, and increased early churn rates.

What has been done:

We developed a comprehensive onboarding workflow that filled existing gaps, aligning them with customer expectations and minimizing early churn by enhancing initial customer interactions.

‍

Initiative 4 - Compliance with New Insolvency Data Laws

Problem:

New legislation imposed stricter data privacy and retention requirements, necessitating enhanced deletion criteria for insolvency data to ensure compliance.

What has been done:

We developed and implemented an automated data deletion protocol that ensures insolvency data is securely removed from the platform after six months. This solution not only ensured full compliance with legal regulations but also reinforced trust with users by prioritizing data privacy and security.

‍

Initiative 5 - Legal Knowledge Base

Problem:

The need for an enhanced legal knowledge base for Legal Twin required identifying reliable data sources to support the RAG model, which lacked sufficient structured content.

What has been done:

We conducted a thorough search for relevant data sources, including legal databases and case law repositories, to fuel the RAG model. Additionally, we identified key opportunities to integrate external data to ensure the model’s responses will be more accurate, comprehensive, and contextually relevant for legal professionals.

‍

Initiative 5 - Upselling Feature

Problem:

Users lacked visibility into their page usage within Legal Twin, making it difficult for them to track consumption and recognize when they might benefit from upgrading their package. This limited upselling opportunities and potential revenue growth.

What has been done:

We introduced a usage tracking feature that displays the number of pages analyzed and the remaining balance in a user’s package. Additionally, we implemented upsell nudges at key thresholds, such as 80% and 95% of package consumption.

‍

Mission Achievements: Delivered Outcomes

💡 Created a clear roadmap and vision for Legal Twin (AI Agent), incorporating insights from the Opportunity Solution Tree, to guide future developments.

💡 Developed the "Ask Your Case" feature with a user-friendly UI.

💡 Ensured compliance with new legislation through automated data deletion protocols.