Beyond Keywords: Accelerating eDiscovery

Data management issues translate to lost revenue

In law, cases like high-stakes litigation and M&A are susceptible to three bottlenecks that are both time and energy intensive:

1. Sourcing the documents & potentially digitizing them in huge, disconnected datasets
2. Searching & collecting the appropriate documents (from the sentence, to paragraph, to document, to image level).
3. Manually reviewing the documents to build a case.

Sourcing documents is an exorbitant expense costing up to $18,000 for per GB of data¹ and complex cases could have 100s if not 1000s of GB of data. Many legal firms have a digitization issue, working with librarians to find the physical file and adding days before document review can even begin. This problem is compounded by fragmented, unintuitive, manual document management systems; emails in one platform, contracts in another, and court filings in a third all with their own UIs. Lawyers sometimes spend 17% of their workday on legal research⁴, time that doesn’t build a case or serve clients

Manual document review costs billions to the industry while missing half the relevant documents. Reviewing documents could take up to 5-10 hours to review, typically done by a legal team². Worse, the Blair & Maron study revealed a dangerous confidence gap: teams believed they found 75% of relevant documents when actual recall was around 20%⁴ so there’s an acute mismatch between perceived accuracy and actual accuracy for reviewed documents³. This isn’t document ambiguity, this is systemic failure that compounds every case.

Where current Tech Fails

Some of the case studies speak for themselves. A Fortune 500 construction company achieved 57% reduction in discovery-related costs using AI tools⁸. In a DOJ second request matter, AI-assisted review processed 322,000 documents with 85% coded automatically, meeting a 60-day deadline that would have been impossible manually⁷.

Technology assisted review (TAR) is emerging to address the review bottleneck. Major e-discovery platforms like Relativity, Everlaw, DISCO’s Cecilia now offer e-discovery TAR integrations but with significant issues.

• The “seed set bias”: TARs require seed sets, a small set of documents that human reviewers use to train the system. This creates a fundamental problem: you need to know what you’re looking for before you find it. Not only this, the small seed set introduces major biases compromising the review.
• Adversarial vulnerability: TARs also don’t protect against data poisoning / adversarial attacks. Standard tools cannot detect “coded” language or attempts to hide evidence through vague phrasing.
• Multimodal blindness: multimodal data remains a nightmare that’s not really integrated with TARs (emails, documents, audio files, images, videos, etc.) possibly leaving out critical evidence.

Adding to this is the UX component. Despite TARs improving accuracy during manual reviews the bottleneck is adoption and usability. Most solutions still have non-intuitive UIs and people are not properly taught on how to use this leading to resistance⁶. Finally, firms still heavily rely on keyword searches.

The solution: High value insights

As mentioned, legal teams are slowed down by their existing tools, spending billed time on administrative search tasks.

The true solution is multimodal semantic search over all assets yielding the simplicity of keyword search and the accuracy of TAR without the critical issues. Type what you’re looking for in natural language and the system can find what you’re looking for.

• True multimodal search: across text, images, audio, and video and zooming in to the paragraph or timestamp in question
• Instant context: no “training” phase that comes with predictive coding in TARs. Upload and ask questions immediately.
• Risk reduction: Reduce wasted time on e-discovery by finding genuinely relevant documents, accelerate first pass manual review, and free legal teams to focus on the more important aspects of the case rather than document triage

Some example use cases:

• M&A Due Diligence: Instead of manually reading 5,000 pages to find a specific "Change of Control" clause, use semantic search with natural language (such as “Company A’s termination agreement with a 30 day notice”) to zoom in immediately.
• Internal Investigations: Find evidence of "fraud" or "harassment" based on the tone and context of communications, not just by searching for specific bad words.

In conclusion

The concern that AI cannibalizes the billable hour is a dangerous misconception in the modern legal landscape. In reality, automation eliminates the low-value administrative friction that clients resent paying for, freeing up capacity for high-value strategy. By increasing efficiency, legal teams can take on more cases, effectively increasing their revenue. This shift allows firms to move from a model of scarcity to one of scale. Ultimately, the choice is clear: evolve to offer superior accuracy and speed, or cede the market to competitors who do.

Stop searching. Start finding. If you’re ready to modernize your review workflow, let’s talk.

Contact: ily@interpretai.tech