When hiring for eDiscovery roles, one of the first questions that surfaces in a job description is which platform the candidate has worked with. Relativity, Reveal, and Nuix are the three names that dominate the conversation. Each has a distinct architecture, a different philosophy around data processing, and a user base with its own expectations. For hiring managers who are not practitioners themselves, the differences between these platforms can feel opaque, making it difficult to evaluate candidates fairly or write job descriptions that attract the right people.
This article breaks down what each platform actually does, how they differ in practice, and what those differences mean for the people you hire. By the end, you will have a clearer framework for assessing platform experience, understanding technical skill requirements, and building job descriptions that speak to the talent you actually need.
What Relativity, Reveal, and Nuix actually do
All three platforms operate within the eDiscovery space, but they approach the problem from different angles. Understanding their core purpose is the foundation for everything else in this comparison.
Relativity is the most widely adopted eDiscovery platform in the legal market. At its core, it is a document review and production platform, built around the concept of workspaces where legal teams organize, review, and produce documents in response to litigation, regulatory investigations, or compliance requirements. It is cloud-native in its current form (RelativityOne) and has a vast ecosystem of integrations, third-party applications, and a large global user base across law firms, corporations, and government agencies.
Reveal is a newer entrant that has positioned itself around artificial intelligence as a first principle rather than a bolt-on feature. Where Relativity added AI capabilities over time, Reveal was built with machine learning at the center of its review workflow. It is designed to accelerate the review process by surfacing relevant documents faster, reducing the volume of material human reviewers need to examine manually.
Nuix takes a different starting point entirely. Its primary strength is data processing and investigation rather than document review. Nuix excels at ingesting large, complex, and unstructured datasets, including forensic images, mobile data, and encrypted files, making it particularly common in regulatory investigations, digital forensics, and matters where the data itself is the challenge before review even begins.
Think of it this way: if eDiscovery were a pipeline, Nuix would often be at the front end preparing the raw material, Relativity would handle the middle and end stages of review and production, and Reveal would be a faster lane through that middle stage. In practice, many organizations use more than one of these platforms depending on the matter type.
How each platform approaches data processing and review
The architectural differences between these platforms become most visible in how they handle two core tasks: processing raw data and enabling document review. These differences have direct implications for the skills your candidates need.
Data processing
Nuix has long been recognized for the depth and speed of its processing engine. It can handle formats that other platforms struggle with, including encrypted containers, fragmented file systems, and large-scale mobile extractions. Practitioners who specialize in Nuix processing often come from a forensic or investigative background rather than a traditional legal review background.
Relativity’s processing capabilities have expanded significantly, particularly within RelativityOne, but many organizations still use dedicated processing tools and feed output into Relativity for review. This means a Relativity specialist may not necessarily have deep processing knowledge; their expertise may sit entirely within the review environment.
Reveal’s processing layer is competent but not where the platform differentiates itself. Its value proposition sits downstream, in how it handles the data once it is ready for review.
Document review and AI-assisted workflows
Relativity’s review environment is mature, highly configurable, and familiar to most eDiscovery practitioners. Features like active learning, analytics, and clustering have been part of the platform for years. Reviewers and administrators working in Relativity need to understand workspace configuration, coding layouts, saved searches, and production formats.
Reveal’s AI-driven approach means that its workflows look different from a traditional linear review. The platform uses continuous active learning to prioritize documents, which changes how review managers plan and track progress. Practitioners experienced only in traditional review models may find Reveal’s approach requires a shift in thinking about how review efficiency is measured.
For hiring managers, this distinction matters. A candidate with five years of Relativity experience and no exposure to AI-assisted review may need a learning curve on Reveal, even if the underlying legal concepts are identical.
What technical skills each platform demands from practitioners
Platform experience and technical skill are related but not the same thing. Each platform rewards a different combination of abilities, and understanding this helps you assess candidates more accurately during interviews.
Relativity practitioners
Relativity administrators and power users typically need to be comfortable with:
- Workspace setup, permissions management, and folder structures
- Saved searches, views, and coding layout configuration
- Processing workflows, either natively or via integrated tools
- Production specifications and quality control processes
- RelativityOne administration if working in the cloud environment
- Script and application installation within the platform
Some Relativity specialists also have experience with the platform’s developer tools and APIs, which becomes relevant for organizations building custom integrations or automating workflows.
Reveal practitioners
Working effectively in Reveal requires comfort with AI-assisted review concepts, including understanding how active learning models are trained and monitored. Key skills include:
- Configuring and managing AI review workflows
- Interpreting model performance metrics to make review decisions
- Communicating AI-assisted review methodology to legal teams
- Understanding how to validate AI-prioritized batches for quality control
Reveal practitioners who thrive tend to be analytically minded and comfortable working with data in ways that go beyond traditional document-by-document review management.
Nuix practitioners
Nuix expertise tends to sit closer to the technical and forensic end of the spectrum. Skills commonly associated with strong Nuix practitioners include:
- Data ingestion and processing of complex or forensic data sources
- Understanding of file formats, metadata, and data integrity
- Scripting and automation within the Nuix environment (often using Ruby or Python)
- Experience with regulatory or investigative workflows rather than pure litigation review
Nuix practitioners frequently have backgrounds that overlap with digital forensics, which means their career history may look different from that of a traditional eDiscovery reviewer or project manager.
Why platform experience doesn’t always predict candidate quality
This is one of the most important concepts for hiring managers to internalize: platform familiarity is a proxy for skill, not a measure of it. Treating platform experience as the primary filter in your hiring process can cause you to overlook excellent candidates and favor mediocre ones.
Consider two candidates. The first has three years of Relativity experience at a document review company, working in a narrow, repetitive role with limited exposure to complex matters. The second has two years of experience across Relativity and Reveal, managing end-to-end workflows on high-stakes regulatory investigations. On a keyword-filtered job application, the first candidate might score higher simply because they have more years on the named platform.
What actually predicts performance in eDiscovery roles is a combination of problem-solving ability, understanding of legal workflows, attention to data quality, and the capacity to learn new tools quickly. Platforms change, update, and are replaced. A practitioner who deeply understands why eDiscovery processes work the way they do will adapt to a new platform far more readily than someone who has memorized one interface without understanding the underlying logic.
This does not mean platform experience is irrelevant. If your organization runs entirely on Nuix and needs someone operational from day one, prior Nuix experience has real value. The point is to treat it as one signal among several, not as a threshold that eliminates candidates before you understand their broader capability.
You can explore available eDiscovery roles to get a sense of how platform requirements are typically framed in the current market, which can help calibrate your own job descriptions.
How to write job descriptions that attract the right platform expertise
Job descriptions in eDiscovery often suffer from one of two problems. They either list every platform under the sun as a requirement, creating an impossible candidate profile, or they over-index on one platform and screen out people who could do the job well with a short ramp-up. Neither approach serves you well in a market where specialized talent is genuinely scarce.
A more effective approach separates requirements into tiers based on what is truly essential versus what is learnable on the job.
Define the actual workflow, not just the tool
Instead of writing “must have five years of Relativity experience,” describe what the person will actually be doing. For example: “You will manage end-to-end processing and review workflows for complex litigation matters, configure workspaces, oversee production quality control, and advise legal teams on review strategy.” This framing attracts candidates who understand the work, regardless of which platform they have used to do it.
Separate must-have from preferred
Be honest about what is truly non-negotiable. If your organization uses Relativity and switching is not an option, then Relativity experience is a genuine requirement. But if you have the capacity to onboard someone to the platform over a few weeks, consider listing it as preferred rather than required. The pool of candidates who are strong eDiscovery practitioners but less experienced on your specific platform is larger than the pool of people who meet every listed requirement.
Signal the technical depth you actually need
A job description for a Nuix processing specialist should communicate that scripting ability and forensic data experience matter. A role focused on Reveal-based review management should signal that AI workflow experience is valued. Generic eDiscovery job descriptions attract generic candidates. Specificity about the technical environment signals to experienced practitioners that you understand the field, which makes your opportunity more credible and more attractive.
Avoid the platform checklist trap
Listing Relativity, Reveal, Nuix, Everlaw, and three other platforms as requirements in a single job description tells candidates that you do not fully understand the roles these tools play. It also signals that the role may be poorly defined. Focus on the platforms central to the role and leave room in the description for candidates to bring complementary experience.
If you are working with a specialist eDiscovery recruitment partner, they can help you refine these descriptions before they go live, which often makes a measurable difference in the quality of applications you receive.
How Iceberg helps with eDiscovery platform hiring
Finding practitioners with the right combination of platform knowledge, legal workflow understanding, and technical depth is genuinely difficult. The candidate pool is specialized, and the signals in a CV do not always tell the full story about what someone can do.
At Iceberg, we work exclusively within eDiscovery and cybersecurity recruitment, which means we understand the difference between a Nuix processing specialist and a Relativity review manager, and we know how to assess candidates beyond their platform checklist. Here is what we bring to eDiscovery platform hiring:
- A network built for this space: We have access to over 120,000 eDiscovery and cybersecurity professionals across 23 countries, including practitioners with deep expertise in Relativity, Reveal, and Nuix.
- Platform-aware candidate assessment: We evaluate candidates on the depth of their workflow understanding, not just the names on their CVs, so you receive shortlists that reflect genuine capability.
- Job description consulting: Before we search, we work with you to clarify what the role actually requires, helping you avoid the common pitfalls outlined above.
- Speed without compromise: We move quickly because we work in a focused niche, and our track record reflects that quality is not sacrificed for pace.
- Vacancy Health Check: If you are struggling to fill an eDiscovery role, our complimentary 30-minute consultation diagnoses where the process is breaking down and gives you actionable recommendations.
If you are hiring for an eDiscovery role and want a recruitment partner who understands the platform landscape as well as you do, get in touch with our team to start the conversation.
