EVERLAW

AJ Shankar was a PhD student at UC Berkeley studying computer science. He noticed that the legal industry's technology was stuck in the 2000s.

Ediscovery, the process of finding relevant documents in litigation, was dominated by Relativity, a tool that required on-premise installation, expensive consultants, and a learning curve that could make grown lawyers cry.

Shankar founded Everlaw in 2010 with the idea that ediscovery should be cloud-native, intuitive, and fast. He recruited co-founders from Berkeley and built the platform from scratch.

The early years were tough. Law firms do not adopt new litigation tools lightly, especially for high-stakes cases where a missed document can mean millions in liability.

The breakthrough came when Everlaw started winning government contracts. Government agencies have massive ediscovery needs and are increasingly open to cloud-based solutions.

Once agencies like the DOJ started using Everlaw, law firms on the other side of those cases needed access too. That created a network effect.

Everlaw charges a per-gigabyte fee for data processed on its platform, plus per-user licensing fees. It is a usage-based SaaS model.

The more data a case involves, the more it costs. For litigation involving millions of documents, this can be significant revenue per customer.

The model works because ediscovery is a cost center that law firms pass through to clients. When a client is paying $50 million in legal fees for a major case, the ediscovery platform cost is a rounding error.

This means Everlaw does not face the same price sensitivity as consumer software. Clients care about speed and accuracy, not saving $500.

Everlaw's platform covers the full ediscovery lifecycle: data ingestion, processing, document review, predictive coding, and production. The interface is designed to feel like a modern web app rather than an enterprise tool from 2005.

Everything runs in the browser with no installation required.

The AI features are the main differentiator. Everlaw's predictive coding uses machine learning to prioritize the most relevant documents.

The newer generative AI tools can summarize long documents, identify key themes across thousands of records, and draft privilege logs. For a major litigation case involving 10 million documents, these tools can cut review time from months to weeks.

Everlaw grew by being dramatically easier to use than Relativity. In demos, the speed difference is visible.

Searches that take minutes on Relativity take seconds on Everlaw. For lawyers who bill by the hour and review thousands of documents per case, that speed difference translates directly into cost savings for clients.

They also invested in AI-powered predictive coding early. This lets lawyers train a model to identify relevant documents, dramatically reducing the number of documents that need human review.

In 2023, Everlaw added generative AI features that can summarize depositions and draft document review memos. The AI angle attracted a $100 million Series D in 2024 from TPG.

Relativity is the 800-pound gorilla. It has been the default ediscovery platform for over a decade and has deep relationships with every major law firm.

Switching costs are high because lawyers and paralegals are trained on Relativity. Getting them to learn a new platform in the middle of active litigation is a hard sell.

There is also the security question. Litigation documents are among the most sensitive information in existence.

Putting them in the cloud makes some lawyers deeply uncomfortable, no matter how many SOC 2 certifications you wave at them. Everlaw has to overcome this objection with every new enterprise sale.

TPG led Everlaw's $100 million Series D in 2024, giving the company significant firepower to compete with Relativity. Andreessen Horowitz led the Series C.

Earlier investors include Menlo Ventures and K9 Ventures. The a16z investment in 2021 was notable because the firm rarely invests in legal tech.

When Marc Andreessen's firm backs a legal tech company, the market pays attention.