Making AI Work in Legal Tech: Finding the Right Balance Between Cost and Performance

By Kristina Ureña, Data Engineer, INVID

In the rapidly evolving field of legal tech, operationalization refers to the process of implementing and integrating new technologies, such as AI-based solutions, into business processes, operational systems, and workflows to improve business outcomes. It involves three key players: technology, people, and processes. Without them working together, there is no effective operationalization and, therefore, no value delivered [1].

The legal sector has been traditionally conservative, but in recent years, it has embraced AI integration and innovation advantages. The willingness to explore new tools like large language models (LLM), machine learning (ML) models, and natural language processing (NLP) is opening unthinkable possibilities to improve processes, reduce operational costs, or simply innovate [2].

AWS, Azure, and Google provide fully managed platforms, tools, training, and certifications to prototype and deploy AI solutions at scale. For instance, AWS Sagemaker, AWS Bedrock, Azure AI Search, Azure Open AI, and Google Vertex AI [3,4,5,6,7]. All these platforms overcome the challenges behind processing complex volumes of unstructured data, like PDFs, images, video, and audio.

Other frameworks like LangChain provide free resources to build, run, and manage LLM apps that are capable of interoperating with different providers. They help you prototype faster, evaluate multiple approaches, deploy, and monitor at scale under attractive pricing models [8]. Integrating workflows with LangChain and other packages like NTLK, Spacy, and Tesseract can lead you to a powerful and affordable AI-based solution [9,10,11].

Key learnings while operationalizing AI solutions in Legal Tech

Below there are some key elements to be considered when operationalizing AI-based solutions in Legal-Tech [12,13]:

Implementation of Insights

Data operationalization involves transforming analytical insights, predictions, or recommendations derived from data into actionable value. Therefore, it bridges the gap between data and practical application, ensuring that the insights are effectively used to drive business operations and decision-making.

Most data sources are unstructured, like PDFs. They come in all flavors: different formats, templates, and from different legal processes, sizes, and quality.

Additionally, we have the human factor, which introduces grammar, semantic, and structural intrinsic challenges. Open-source data engineering tools and techniques can help us to improve the extracted text, fix grammar and semantic errors, and summarize with high reliability, low cost, and high performance, for instance:

Detect and fix rotated pages using PyPDF2 and PyMuPDF [14,15]
Extract text from complex structures like images and tables using Tesseract
Extract key patterns like entities, names, dates, and cause types using NLTK
Fix grammar and semantic errors using Spacy-trained pipelines
Extractive summaries using Sumy [16]

Integration with Operational Systems

On the other hand, the solution can be designed and packed by applying decoupled principles and using tools like Docker [17] and/or Kubernetes [18] to integrate with existing systems. They can be proprietary, third-party, open-source, and run either on-premises or in the cloud. Either way, the solution has to bring value to the day-to-day operations. For instance, consider designing and implementing API layers on top of your AI solution to allow systems integration and interoperability.

Automation and Scalability

Operationalization normally involves automating processes and workflows to enable scalability and efficiency. By automating data processes, organizations can ensure that insights and models are consistently applied to new data and operational decisions, reducing manual effort and improving responsiveness.

In Legal-Tech, you build and deploy step-by-step to ensure insights and models are reliable, repeatable, and delivered to the end users. Tools like Airflow [19], Kedro [20], or Prefect [21] provide industry-probed automation and great scalability when your solution is moving from processing a few thousand MBs to some TBs or PBs of data.

Monitoring and Feedback Loops

Feedback loops enable organizations to evaluate the effectiveness of the operationalization, measure outcomes, and make necessary adjustments to improve results over time. Make sure to implement external and internal metrics using configuration-driven approaches in the solution. External metrics can be implemented using Business Intelligence (BI) tools and shared with the clients to measure performance.

Internal metrics can be very technical, like hyperparameters, which can be tuned over time. For instance, LLM prompts versions, Optical Character Recognition (OCR) setups on Tesseract, n-grams set up in NLTK, etc. Consider tools like CicleCI [22] for Continuous Integration (CI) and Continuous Delivery (CD) to speed up testing new changes and their deployment to production.

Governance, Security, and Compliance

Organizations need to ensure that the implementation of data-driven insights aligns with legal and ethical standards, safeguards sensitive data, and maintains compliance with applicable regulations. In Legal-Tech, extractive and abstractive summaries are among the most important outcomes. Ensure that subject matter experts (SMEs) validate your LLM and extractive algorithms, which should be industry-proved and tuned. This is one of the most important parts; without it, operationalization won’t be possible, and the adoption of the solution will be at risk.

Continuous Improvement

Organizations should continuously assess the effectiveness and efficiency of operationalization activities, refining models, improving LLM outcomes, and considering SME feedback and new data to drive ongoing enhancements.

At INVID, we are building a Generative AI solution for Legal-Tech using Azure and best practices in operationalization. The solution collects and ingests large volumes of data (terabytes of legal documents) from on-premises to an Azure Resource Group using proprietary tools.

Before indexing the data using Azure AI Search, we deployed a proprietary workflow that transforms unstructured data into a format usable by our NLP and LLM pipelines. They extract features and patterns and produce extractive and abstractive summaries, which are deployed through APIs, reducing or preventing the utilization of cloud features like skillsets. This hybrid approach delivers up to 50% cheaper solutions without compromising the outcome’s quality and scalability.

Challenges and opportunities in LegalTech

Operationalizing AI solutions in the LegalTech domain requires addressing technical, organizational, and regulatory considerations to build trust and ensure the interpretability of AI models. One of the main challenges is ensuring the technology’s reliability, transparency, and ethical alignment with the legal industry’s standards and practices. Addressing these challenges requires implementing best-practices approaches to data management, model development, and deployment. Additionally, establish robust governance frameworks that prioritize the integrity of the legal decision-making process.

In LegalTech, AI solutions can be leveraged to enhance various aspects of legal practice, such as contract analysis, legal research, document review, and even predictive analytics for litigation outcomes. However, the successful operationalization of these AI-powered tools requires a deep understanding of the legal domain. This includes the specific workflows and processes involved, and the unique data and regulatory requirements that govern the legal industry [23,24].

The Bottom Line

Given the complexity of legal decisions and the potential impact on individual rights and citizens’ well-being, it is crucial that the AI solutions deployed in the LegalTech domain are transparent, accountable, and aligned with ethical principles [25]. To ensure the ongoing integrity and fairness of AI-powered systems, develop robust data governance frameworks, implement explainable AI techniques, and establish continuous monitoring and auditing mechanisms.

For more information or to discuss how we can help you harness the power of legal tech in your organization, feel free to contact us.

References

[1] https://www.forbes.com/councils/forbestechcouncil/2024/04/25/operationalizing-ai-5-steps-to-maximize-the-impact-of-ai-investments/

[2] https://legal.thomsonreuters.com/en/insights/articles/ai-and-its-impact-on-legal-technology

[3] AWS Sagemaker

[4] AWS Bedrock

[5] AZ AI Search

[6] AZ OpenAI

[7] Google Vertex AI

[8] https://www.langchain.com/

[9] https://www.nltk.org/

[10] https://spacy.io/