Laer AI was a litigation-focused AI startup whose Aida Discovery product targeted the expensive middle of document review: reducing first-level review volume, connecting facts across large discovery sets, and helping litigators move from raw document collections to deposition prep and post-deposition analysis faster. The standalone company now appears largely absorbed into Epiq after a 2024 acquisition: Epiq’s January 14, 2025 launch of Epiq AI Discovery Assistant and Epiq AI Labs says the product was initially developed by Laer AI and that the founders joined Epiq as vice presidents. Public evidence supports three concrete jobs: cutting down review volume and cost, surfacing early case insights across large datasets, and turning deposition transcripts into structured summaries and fact extractions. Public evidence is otherwise thin: no meaningful Reddit signal, no G2/Capterra footprint, no public pricing, no public standalone security documentation, and the laer.ai site returned HTTP 503 when checked on March 9, 2026.
Company Info
- Founded: 2018
- Team size: 1-10 employees
- Funding: $3.0M
- HQ: United States
- Sector: Litigation
What We Haven’t Verified
This page was assembled from publicly available information. Feature claims and workflow mappings are based on what the vendor and third-party listings publish — not hands-on testing or practitioner feedback.
Workflows
Based on practitioner evidence, Laer AI is used in these workflows:
What practitioners struggle with
Real frustrations from legal professionals — the problems Laer AI addresses (or should address). Sourced from practitioner reviews, Reddit threads, and case studies.
eDiscovery costs are insane — traditional vendors charge per-GB processing fees that can hit $100K+ for a single matter, making it economically impossible for small-to-mid firms to run proper discovery
500K documents to review, contract attorneys burning out after 4 hours of screen-staring, nobody knows if the review is consistent across 20 reviewers — and the partner watching the budget bleed
When my litigation team receives 100,000 documents in discovery and the partner wants an early case assessment by Friday, I need to understand the key facts, players, and timeline before we've even started formal review — but right now the only option is throwing associate hours at it and hoping we surface the right documents
Litigator has 200 pages of deposition transcripts and needs to extract the 15 key facts that matter for the motion — but reading and manually tagging each relevant passage takes an entire weekend, and there's no way to link those facts back to the specific transcript page when writing the brief
Where it fits in your workflow
Before Laer AI
Large litigation or investigation matter generates a review corpus -> outside counsel, corporate legal, or an ALSP needs to cut first-level review volume, identify key themes early, and prepare witnesses using the same body of evidence
After Laer AI
AI-assisted review narrows the set needing manual first-level review -> litigators use the same evidence graph for early case assessment and deposition prep -> post-deposition transcript summaries and structured extractions feed motion drafting, witness prep, and review strategy
Integrations & hand-offs
Document collection and review protocols feed the assistant -> Epiq review teams or litigation teams validate results -> deposition prep and transcript analysis outputs move into briefs, outlines, and follow-on manual review. Public documentation does not surface native DMS, case-management, or evidence-portal integrations.
Also used by similar teams
Community Data
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