Through advanced automation designed specifically for legal workflows, Practice AI was developed to support law firms seeking more coordinated, data-informed operations. The platform reflects founder Hamid Kohan‘s vision of modern legal environments, where structured data processes and human expertise work side by side to support case preparation, document handling, and client communication cohesively. The launch centers on an integrated five-solution suite built specifically for personal injury law firms, combining multiple workflow functions into a unified operational framework.
Kohan’s path toward building legal workflow technology began long before entering the legal sector. Showing early technical aptitude, he completed an engineering degree and later pursued advanced business education focused on technology commercialization and product strategy. His early career exposed him to large-scale software development environments and enterprise infrastructure design, shaping his understanding of how complex systems can support real-world professional work. Over time, his focus expanded from pure technology development into business growth strategy, where he focused on helping professional service organizations scale operations while maintaining service consistency.
As his career progressed, Kohan increasingly focused on the intersection of operational efficiency and professional service delivery. His work with service-based organizations introduced him to the operational realities of high-volume document handling, client communication management, and data organization. These experiences led him toward the legal sector, where he saw opportunities to design workflow systems that aligned with how legal professionals already work, rather than requiring firms to change their processes around software.
His experience at Legal Soft, Practice AI’s sister entity, as founder and CEO, exposed Kohan to the operational challenges law firms face when managing high volumes of administrative and case-related data. “During that period, I spent a lot of time helping firms build steadier operational systems through virtual legal staffing and more thoughtful workflow practices. Those experiences shaped the direction of Practice AI. I combined what I had learned about legal operations with the possibilities of emerging AI technology,” he shares.
Kohan emphasizes the importance of designing systems that align with professional workflows. He says, “Automation should adapt to how professionals already work, supporting consistency and clarity while leaving room for expertise and judgment.” This philosophy informed the development of the platform, influencing how automation tools were structured to support rather than replace human decision-making. Over time, this viewpoint evolved into a broader vision of building connected workflow systems that support legal teams throughout the full lifecycle of a case.
The introduction of the new suite of solutions reflects that long-term vision. Rather than focusing on a single workflow stage, the platform brings together automated intake coordination, document collection workflows, case-summarization tools, demand letter drafting systems, and litigation research support into one connected environment, with more developments in the works.
The inbound and outbound intake solution helps firms keep early client communications organized, bringing messages from different channels into one place and offering flexible question flows and follow-up options. Document collection tools support the process of gathering files from clients, medical providers, employers, and other third parties, with structures that help keep everything verified and easy to track.
For firms working through large volumes of material, the case-summarization features can turn extensive document sets into clearer chronologies and summaries. Demand-drafting tools assist with assembling evidence-supported demand packages using the case information already on hand. Meanwhile, for teams managing research-heavy matters, the litigation support features help organize legal research, case law review, and contextual analysis within dedicated workspaces.
In addition to the primary workflow components, the suite includes features that may help teams manage the finer details of case preparation. These tools can support the creation of organized work products, help maintain structured internal notes, and offer ways to keep attorney-client interactions easier to reference as a matter progresses.
The platform also introduces options that may assist with broader operational visibility, such as evaluating potential new matters, observing general performance patterns, or coordinating larger sets of case materials. Research‑oriented functions are designed to help keep legal analysis more organized and searchable, making it easier for teams to revisit insights as needed.
Security and compliance remain key considerations within the platform’s design. The system incorporates encrypted data processing, secure cloud infrastructure, and compliance-aligned data handling frameworks intended to support firms working with sensitive legal and medical information. This structured approach to data handling is designed to align with professional data protection expectations while supporting automated workflows.
As legal operations continue to shift alongside advances in automation technology, the suite is positioned as a structured workflow environment designed to support personal injury and lemon law practices, managing complex documentation and client communication processes. By bringing multiple workflow functions into one system, the platform supports continuity from initial client contact through case preparation and litigation readiness.
The platform continues to evolve through ongoing development and feedback from legal professionals using the system in active case environments. Kohan’s long-term vision remains centered on building workflow technology that aligns with legal operations, supporting professionals as they balance case strategy, client service, and operational efficiency within an increasingly data-driven legal landscape.
