Revolutionizing Justice

In this blog post, we will explore the leading AI legal startups, their practice areas, business models, and the promising impact they are poised to have on the legal landscape. Additionally, we’ll distinguish between startups serving law firms and those directly serving end clients, shedding light on the diverse ecosystem evolving within legal tech.

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Market Dynamics For AI Legal Startups

The global legal services market size was valued at $904.1 billion in 2022 and is projected to grow at a Compound Annual Growth Rate (CAGR) of 5% from 2023 to 2032. The market for AI in the legal industry is currently witnessing a period of robust growth and transformation. As of 2024, the AI software market in the legal sector is valued at $2.19 billion and is projected to grow to $3.64 billion by 2029, with a compound annual growth rate (CAGR) of 10.70%.​ This growth is indicative of the increasing adoption of AI technologies for a variety of applications, including legal research, contract review and management, e-discovery, and more.

North America is leading the charge in adopting and developing legal AI technologies, attributed to the considerable presence of firms providing AI platforms and tools tailored for the legal industry.

The legal AI sector has seen significant investments lately—such as Harvey AI’s latest $80 million funding from Kleiner Perkins, Sequoia, and OpenAI and Clio’s USD $250 million Series D round—highlighting the potential for growth and the importance of AI in transforming legal operations. The legal AI software industry’s low market penetration rate presents a substantial opportunity for existing companies and new entrants.

Technological advancements are driving the growth of specific segments within the legal AI market. machine learning (ML) and deep learning (DL) technologies account for a significant market share, largely due to their capacity to analyze large volumes of legal data and automate various legal processes. The natural language processing (NLP) segment is also expected to witness considerable growth, transforming how legal professionals handle and analyze vast amounts of textual data​.

Legal AI Startup-Focused Areas

AI legal startups have ventured into a variety of practice areas to meet the specific needs of its clientele. E-discovery and analytics are two applications of AI in the legal industry that are seeing significant growth. E-discovery’s importance is rising with the increasing volume of electronic data, while analytics applications are growing due to the use of AI and ML for extracting insights from vast legal data, enhancing decision-making and efficiency in legal operations​​. Here’s more on each of these applications.

• E-Discovery involves identifying, collecting, and producing electronically stored information (ESI) in response to a request for production in a lawsuit or investigation. AI technologies, particularly machine learning and natural language processing, are used to automate the review of large datasets, identify relevant documents, and categorize them with a level of speed and accuracy that is unattainable by human review alone. This not only reduces the time and cost associated with e-discovery but also increases its accuracy by minimizing human error.
• Document Analysis encompasses the use of AI to analyze legal documents for various purposes, including identifying legal concepts, extracting relevant information, and summarizing content. AI tools in this area are designed to help lawyers quickly understand the substance of documents, prepare legal briefs, and ensure that all relevant information is considered. By automating the analysis of legal documents, AI helps legal professionals focus on strategic aspects of their work, rather than on time-consuming manual review.
• Contract Review and Analysis involves the examination of contractual agreements to identify risks, obligations, and opportunities for negotiation. AI-powered contract review tools can read and understand contracts in natural language and highlight key information, such as clauses that deviate from a standard or preferred position, potential liabilities, and deadlines. This not only streamlines the contract review process but also helps in standardizing the review process across a firm or legal department, ensuring consistency and reducing oversight.

AI-driven contract analysis tools go beyond mere review, offering in-depth analysis of contractual documents to identify potential risks, opportunities, and inefficiencies. These tools employ natural language processing to understand and interpret contract language, facilitating quicker negotiations and ensuring compliance with existing laws and regulations. By automating the analysis of terms and conditions, obligations, and compliance requirements, AI enables lawyers to focus on strategic decision-making and negotiation.

• Legal Research is a cornerstone of legal practice, involving the identification of precedents, statutes, and legal texts relevant to a particular case. AI technologies have revolutionized this practice area by providing tools that can sift through vast amounts of legal data, identify relevant information quickly, and even predict which cases and statutes are most applicable. This not only saves time but also enhances the comprehensiveness of legal research, potentially uncovering critical information that might otherwise be overlooked. Companies such as Casetext, now part of Thomson Reuters, offer enhanced legal research leveraging AI summarization, inference, and natural language processing to surface insights from case law faster for subscribers.
• Compliance with regulatory requirements is a major concern for businesses across all industries. AI tools in compliance help organizations monitor and ensure adherence to regulatory standards and changes. Through continuous scanning of regulatory updates and the ability to analyze company data against these regulations, AI assists in proactively identifying compliance risks and reducing the potential for costly penalties or legal challenges.
• Patent Analytics and Validation In the realm of intellectual property law, AI plays a crucial role in patent analytics and validation. AI tools analyze patents and related documents to assess the novelty of an invention, identify potential infringements, and evaluate the patentability of new inventions. This area of legal tech enables firms and businesses to make strategic decisions about patent portfolios, streamline patent searches, and conduct thorough market and competitor analysis.
• Litigation Prediction and Analytics are increasingly augmented by AI technologies to predict the outcomes of legal disputes and litigation. By analyzing historical data, legal precedents, and specific details of current cases, AI models can provide insights into the likelihood of success in litigation, potential damages awards, and the duration of legal proceedings. This predictive capability enables legal professionals and their clients to make more informed decisions about whether to settle or proceed to court, as well as to better prepare for potential outcomes.

Legal AI Business Models

Legal AI business models vary, with some startups opting for a subscription-based model, offering access to their AI tools for a recurring fee. Others provide a per-use or per-project pricing strategy, appealing to firms or clients with variable demands. Additionally, some have adopted a hybrid model, combining elements of both to cater to a broader market.

• SaaS models have historically been priced per user and vary based on size of the organization and payment term (i.e., monthly vs yearly). These models tend to provide optionality for organizations to limit licenses based on usage of the software and to slide across pricing tiers. Legal AI startups have deployed similar pricing strategies where, in some cases, the pricing tiers tend to differ based on the overall size of the organization. For example, a single office law firm may be priced at a lower tier price while a large national law firm may end up being priced at a different level given the overall value provided. This allows law firms to scale flexibly.

Examples of SaaS products include contract review tools like Kira Systems and eBrevia and eDiscovery software like Everlaw. Some legal AI software companies license access to aggregated legal data sets, predictive models and training datasets such as licensing benchmarking surveys, contract clause libraries, or litigation predictive models to legal departments and law firms for insight.

• Per-use or per-project model continues to be leveraged across different industries for multiple use cases due to its revenue predictability, though popularity has waned in the favor of SaaS based models. Within the legal tech startup space, companies like LegalZoom have leveraged the per-use / per-project model very efficiently to democratize legal document creation for consumers and small businesses.
• Hybrid models make sense for multiple legal use cases where a combination of SaaS model and Per-use / Per-project model become attractive. For example, litigation risk analysis tools like Premonition and LexMachina mine data and court records to assess likelihoods of case outcomes. They offer litigation risk reports on demand for set fees as well as data subscriptions. Hybrid models can include subscription and have specific pricing kicker for throughput.

The business model implications of AI in legal are evolving rapidly as software, data, and services intersect to enhance delivery for clients while managing costs for firms.

AI-Enabled Legal Firms to Compete with Traditional Law Firms

The transition of traditional law firms to AI-enabled law firms is already underway. In the near future, we foresee a sea change within the legal industry where all stakeholders benefit by use of AI. Here are potential scenarios that look very feasible:

1. AI tools may become the first line of analysis that can be conducted by any legal assistant, who then passes on the streamlined document to a partner or an attorney, thus speeding up the process. This scenario benefits the law firm where they can take on additional case load and maximize revenue opportunity.
2. Law firm customers do their own due diligence and leverage law firms to handle tricky and difficult sections of the case, thus bringing focused analysis. While this may seem negative from the law firm’s standpoint, use of AI by customers can weed out inefficient back and forth that’s currently prevalent and reduce time to result for the customer.
3. In the third scenario, attorneys can be replaced by AI-based lawyers where the AI chatbot manages the case. While this scenario could be exciting in some respects, we believe the first two scenarios are more plausible given that the sensitivity of issues being addressed.

Startups Serving Law Firms/Legal Firms/Legal Departments VS. End Clients

AI legal startups primarily focus their solutions on serving one of these two key customer segments —  law firms/legal departments or end clients. The approaches and models tend to be quite different.

Startups serving law firms and legal departments focus on increasing productivity, efficiency, and insights for the lawyers and legal staff by automating high-volume tasks like contract review, document discovery, analytics, etc. They also integrate with the workflows and systems already used by law firms (document management, billing, etc.) to complement the existing tech stack. Such startups also have a focus on complying with security, ethical, and confidentiality requirements regarding client data. Pricing models tend to be based on the number of lawyers/documents under management, modules purchased, storage consumed etc.

In contrast, startups aimed at end clients emphasize accessibility, ease of use, and standardization for end clients who may have little legal expertise. Such offerings include self-service legal documentation, recommendations, and assistance through interactive online expert systems and AI without involving lawyers. They may focus on the long tail of consumer and SMB legal needs that are underserved relative to corporate clients. Pricing models tend to be fixed fee pricing or tiered subscription plans for standardized or defined scope legal services like incorporation, wills, lease review etc.

This distinction underscores the diverse approaches within the AI legal startup ecosystem, with each model catering to the unique needs of its target audience. Startups serving law firms often aim to streamline operations and reduce overhead, while those serving end clients focus on democratizing access to legal services, making them more affordable and accessible to the general public.

In essence, the tech, workflows, business models, and branding differ based on whether they expand access for existing legal clients or enable new clients to bypass traditional legal expertise and costs. Hybrid models are also emerging. But the core emphasis remains quite distinct.

Promising Legal AI Startups and Their Impacts

The past year has seen a surge in investment for AI legal startups, reflecting a growing confidence in their potential to streamline operations, reduce costs, and enhance the delivery of legal services. Certain names repeatedly surface as the most promising among the plethora of AI legal startups due to their groundbreaking technologies and potential to disrupt the legal industry. These startups are expected to significantly impact how legal services are rendered by enhancing efficiency, accuracy, and accessibility. The automation of routine tasks frees legal professionals to focus on more complex and nuanced aspects of law, ultimately benefiting the end clients with faster and more affordable legal services.

In the rapidly evolving sector of legal technology, Harvey.ai has emerged as one of the well-funded AI legal startups. Founded in 2022 by a former Meta AI researcher and an ex-lawyer from O’Melveny & Myers, Harvey.ai has made significant strides in leveraging advanced natural language processing to automate and streamline legal workflows, such as contract review and document rewriting. The startup’s Series B round brought in $80 million, elevating its valuation to $715 million. This round was co-led by prominent VC investors Elad Gil and Kleiner Perkins, with significant contributions from OpenAI’s Startup Fund and Sequoia Capital, pushing Harvey’s total funding to over $100 million.

Harvey.ai’s innovative use of domain-specific AI models in collaboration with OpenAI, combined with its commitment to client data privacy, has rapidly gained traction among leading law firms and Fortune 500 legal departments. London’s Allen & Overy became the first firm to integrate Harvey into its global practice. Many other leading law firms — such as Baker Botts, Vinson & Elkins, Orrick, DLA Piper, Nixon Peabody, and Reed Smith — are using/piloting Harvey’s AI platform, which leverages a proprietary service based on OpenAI’s GPT-4 model and can be trained using firm data.

Competing in this arena, other startups like EvenUp and Darrow have also been making inroads, illustrating AI’s competitive yet flourishing landscape in legal services. Established legal technology companies like Casetext (acquired by Thomson Reuters) have responded by rolling out generative AI-powered tools. Casetext’s product, CoCounsel, utilizes GPT-4 for enhancing legal research, contract analysis, and document review processes.

Fisher Phillips, a major law firm, became the first to deploy CoCounsel firm-wide across its 500+ attorneys, highlighting the growing acceptance and integration of AI technologies within the legal sector. Since then, other large law firms such as Morgan Lewis and McGuireWoods have followed suit. As another indication of greater acceptance, all 41 of the Am Law 100 firms interviewed by The American Lawyer about their use of gen AI indicated that they were working with tools built by third-party vendors, with some firms saying they were also supplementing with their own software development efforts.

This burgeoning interest in AI for legal applications signals a transformative phase in legal services, where efficiency, accuracy, and data security become paramount. As startups like Harvey.ai continue to develop and refine their offerings, the legal industry’s landscape is set for significant change, marked by the adoption of AI to meet the complex and evolving needs of law firms and their clients.

At Alumni Ventures, we have funded several startups across a variety of legal use cases and business models:

Extend Your Practice and Portfolio

The transformative shift in the legal industry driven by the infusion of AI also presents a promising investment opportunity. As we’ve explored the landscape of AI in legal services, it’s evident that the sector is ripe for growth, innovation, and investment. The integration of AI into legal services is not just enhancing efficiency and accuracy but also democratizing access to legal support. The development of AI tools in legal practice areas has also opened new avenues for legal professionals to offer more affordable, accessible, and precise legal services, potentially transforming the traditional law firm model.

For lawyers and legal professionals, investing in AI legal startups offers a unique opportunity to be at the forefront of shaping the future of their profession. It’s a chance to support innovations that not only promise to improve the practice of law but also hold the potential for significant financial returns.

Join our webinar for more about the AI innovations in the legal sector, as well as the startups bringing those innovations to market. Register here.

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