iManage Innovation & Technology Culture

How do your teams stay ahead of emerging technologies or frameworks?

Applied research is how we ensure our models are performant and cost-optimized. The team keeps a pulse on the latest research — publications, industry seminars and professional networks — and experiments with a strategic focus so that the work gets integrated and adopted. In the last several years, infrastructure design and AI governance has become as important as getting the algorithm right and so that has been a priority focus for the team as well. This all aligns with an iManage core value that we call “hunger for learning.”

Can you share a recent example of an innovative project or tech adoption?
Recently, the applied AI team researched fine tuning open weights models for handling specific legal tasks and deployed a small language model built with Llama 3.2 into our platform.

Rather than defaulting to large, general-purpose models like OpenAI or Claude, the team evaluated where small language models perform better on targeted tasks. By tuning and deploying the model on iManage-controlled infrastructure, our AI dev team have full design and tuning control, which simply isn’t possible with closed, hosted APIs.

The benefits of this approach include significantly improved accuracy, security and privacy since customer data and context never leave the iManage ecosystem; cost efficiencies at scale as inference costs and infrastructure become a design choice; and architectural flexibility. In other words, we can optimize, fine tune and evolve the model as product needs change rather than being constrained by a vendor.

How does your culture support experimentation and learning?

Our applied AI team culture is built on the foundation that continuous experimentation and learning are essential to our success. We’ve institutionalized learning through four core pillars: structured knowledge sharing via our biweekly applied AI series, mentorship, active participation in the broader AI community through conference attendance, and embedding experimentation directly into our project workflows.

How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?

Businesses understand that data is critical to success and the future, but the vast amount of data flowing through these organizations every day can often overwhelm rather than empower knowledge workers. At iManage, we focus on integrating AI into our products to directly address this challenge. Our AI solutions are designed to cut through the noise and activate the collective knowledge stored within the world’s top knowledge work organizations, enabling knowledge workers to access insights in seconds, whether it’s knowledge assets, such as precedents and guides, or the experts who created those assets. By embedding AI across our product suite, we ensure that this crucial knowledge is always at the user’s fingertips, allowing them to focus less on the mundane and more on their clients.

What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?

While AI is transforming the legal tech landscape, the core principles of understanding user needs and solving real-world problems remain constant. Our approach begins with a strong emphasis on information architecture before AI. This means we focus on building a solid foundation by carefully structuring and connecting data in a way that aligns with user needs. By doing this, we enable our AI-enabled products to deliver precise, actionable insights and ensure the right knowledge reaches the right person at the right time. 

Equally important is the continuous evaluation of the state-of-the-art. We constantly test the latest developments in AI to fully understand both its strengths and its limitations. This helps us design systems that take advantage of AI’s capabilities while addressing its weaknesses. By pushing the boundaries of what’s possible and refining our understanding of the technology, we ensure that we’re always implementing AI in a way that adds real value to our users. This combination of a strong data foundation and ongoing evaluation ensures that our systems are not only capable of keeping up with AI’s rapid evolution, but also provide practical, everyday solutions.

Can you share some examples of how AI and ML has directly contributed to enhancing your product line or accelerating time to market?

A prime example of how AI has transformed our product line is document enrichment. We use AI to automatically enrich every document uploaded to the DMS with crucial attributes, such as document category, parties, jurisdiction, key dates and more. This automated process not only makes documents far more searchable and provides users with immediate, context-aware results, but it also relieves our customers from the time-consuming task of manual data entry. 

By automatically structuring and connecting data, we ensure quality and enable knowledge to flow seamlessly through organizations. This allows our customers to unlock their collective knowledge, making it accessible across teams and departments, which enhances collaboration and drives smarter decision-making. In essence, we’re empowering organizations to make the most of their data, ensuring that it’s not just securely stored, but also actively used to create value.

What’s it like to work on the AI and machine learning team at your company?

It is highly collaborative, learning-oriented and focused on continuous improvement. We work closely with product and engineering stakeholders, including knowledge engineering, legal experts, backend and frontend engineers and SREs.

A big part of the work is understanding how AI models and applications fit into the broader system, developing a deep understanding of customer pain points and engineering constraints, to enable us to design and develop business-meaningful and trustworthy AI solutions.

The team encourages curiosity, continuous learning and growth. We strongly encourage exploring new ideas, experimenting with emerging technologies and sharing knowledge; and we back that up with a high tolerance for failure. This culture of rapid experimentation is how we turn the best ideas into real, reliable solutions for our customers.

How is your team applying emerging technology in practical, business-relevant ways?

We apply AI to concrete legal document management workflows, working closely with product and legal expert teams to evaluate different use cases, from traditional machine learning to cutting-edge generative AI and agentic applications, such as document classification and enrichment and agentic workflow automation. The goal is not to use AI because of the hype, but to solve real business problems and improve customer workflows.

We stay on top of AI research and evaluate which ideas can actually become useful products. There is a strong experimentation culture around moving from research idea, to prototype, to evaluation and eventually to production when the idea proves valuable.

We also care deeply about AI system efficiency and scalability. Even in the agentic AI era, "garbage in, garbage out" still applies and arguably even more so. At iManage's scale we need both strong model quality and good latency, throughput and cost efficiency.

We are also bullish on leveraging AI-assisted coding tools, such as Claude Code. We actively learn and share best practices to use them in a reliable and verifiable way, rather than simply chasing "token-maxxing."

What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?

Candidates should know that the work spans the full AI stack: data curation, model training or fine-tuning, evaluation, LLM inference, agentic system development, backend integration, deployment and production monitoring.

We value people with both breadth and depth. The best candidates are curious full-stack generalists who can reason across the system while also going deep in one or two areas such as LLM architecture, inference optimization, evaluation, or applied ML engineering.

Collaboration and communication skills are very important. AI work here involves many cross-functional partners, so being able to explain ideas, solutions and tradeoffs clearly is just as important as technical depth. Clear context and a good understanding of the "why" behind decisions makes the work feel more meaningful and grounded. We tend to see that people who think beyond technical silos and combine strong technical skills with solid product sense also make the best technical bets.