Key service areas

Our client is a UK government body connected to the judicial process system. Its legal professionals have to make informed, accurate prosecuting decisions based on huge amounts of case material.

Our client is a UK government body connected to the judicial process system. Its legal professionals have to make informed, accurate prosecuting decisions based on huge amounts of case material.

The organisation wanted to improve its legal decision-making process with the help of new technology. In particular, it needed a search tool that would enable lawyers to navigate case material quickly, and make necessary redactions efficiently and accurately.

These improvements were not just for the benefit of lawyers. A new case management portal would significantly save money for this taxpayer-funded public body, and – crucially – enable lawyers to make better decisions when dispensing justice.

Our client’s outdated system was losing money, wasting time and fostering frustration among its huge staff of legal professionals.

esynergy was selected to partner with the client’s internal teams to develop and build new case search, navigation and redaction tooling that was designed around the way lawyers worked. Our portal would be able to scale as needed, and enable this work to be done securely and remotely.

Our portal wasn’t to be a wholesale replacement of the organisation’s legacy infrastructure. However, it needed to dramatically improve upon the body’s existing case management system, which was cumbersome and cost-ineffective, and increasingly frustrating for the lawyers who used it.

We were initially engaged to build a proof of concept (PoC) case material search tool, which incorporated multiple AI elements based on the Microsoft Azure platform.

This tooling was inspired in part by the fascinating JFK Files website, part of Microsoft’s AI Labs project. The site allows anyone to navigate more than 34,000 pages of public-domain documents connected to 1963’s President Kennedy assassination.

Before any work began, it was essential that our team thoroughly understood the organisation and its work, including the role of legal case data. We also had to demonstrate an understanding of the legal and social context in which the organisation operates.

The build: Developing a prototype in Azure

The portal comprises multiple innovative applications developed using Microsoft Azure AI services, against a strict time schedule.

Our team began by embedding work-tracking functions in Azure DevOps. This helped our developers document and understand their tasks. Then we created a technical estate using Azure Serverless. This included file upload and retrieval functions, an Azure Blob storage container, and an Azure Cosmos DB database instance.

Next, we used a React single-page application to create a review portal, hosted in an Azure App Service instance. This review portal communicates via three Azure functions:

Other Azure components included Application Insights for logging and reporting, and the AI Computer Vision instance itself. In the main, our teams developed these components using C# .Net Core in Linux, and then secured all components using IP address/active directory permissions via the Azure portal.

esynergy teams utilised Microsoft Azure AI services across numerous applications when building this tooling. For example, we used Azure Computer Vision to develop a prototype handwriting review portal, which can intelligently analyse handwritten text in image files then provide structured results for users to review.

Our approach: Agile, visible, collaborative

esynergy used agile working methods and automation to maximise efficiency and reduce costs throughout this project.

For instance, we developed Terraform scripts to build and configure the required Azure components. Website deployment and associated function code was also automated.

Visibility was key. We used Azure DevOps to manage stories, and ran regular stand-ups, planning sessions, progress reviews and demos to keep teams and stakeholders informed and involved. We created a clear digital paper trail of progress, setting out how decisions were made and changes were managed.

Our team kept the client’s legal staff involved throughout the journey, which one of the client’s technical specialists called “massively collaborative”.

For example, we created an initial test harness that allowed users to compare AI services, including stable and beta versions of Azure Computer Vision. Users could upload a document to multiple different cloud offerings, then use the results to decide which one to incorporate in the prototype portal.

At every stage, we consulted and tested with the client’s legal professionals to find out what was working well, and what needed improving. Elements that proved particularly popular included the ability to load a case very quickly, switch quickly between multiple documents in tabs, and carry out redaction on the fly.

The tool’s instinct for grouping cases together was harder to get right, because categorisation is subjective. AI and machine learning will improve the tool’s grouping decisions in time. Meanwhile, automated grouping still represents a big step forward in helpfulness and efficiency for users.

Next steps: From alpha to pilot

The project is now a fully working alpha-stage prototype running on dummy data. In fact, the system is running so smoothly that we’ve actually had to explain to users that the data isn’t real.

Lawyers, who will make up the bulk of the portal’s users, now account for about 90% of alpha testers. We have consulted them at every stage of the process, and asked them what they need to suit their ways of working.

We’re now in the process of switching to anonymised real legal data, with pilot testing due to begin in autumn 2022.