AI Engineering for Legal Practice Support

We automate document review, precedent research, and discovery processing — your team analyzes, not reads.

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What We Automate

We don't know every problem in your operation. You do. These are the types of work we engineer AI for. You tell us where it hurts most.

Common Questions About AI for Legal Practice Support

How do we start?
We start with a 2-week AI Roadmap. Our engineers spend time inside your operation — watching how document review teams process productions, how researchers find precedent, and how discovery gets scoped and managed. The output is a ranked list of workflows by ROI, with implementation timelines and expected savings. No commitment to build beyond the roadmap.
Does this integrate with Relativity and our existing review platforms?
Yes. We connect to Relativity, Nuix, Concordance, DISCO, and any platform your team uses for document review and discovery. The AI sits on top of your existing stack — no rip-and-replace. Your reviewers keep using the tools they know.
What about privilege and work product protection?
Everything we build runs inside your infrastructure, not ours. Your data never leaves your environment. Privilege logs are maintained automatically. We sign NDAs and agreements addressing privilege and confidentiality obligations. Encryption at rest and in transit, role-based access controls, and comprehensive audit trails.
Can AI really handle the nuance of legal document review?
AI handles the volume — your team handles the nuance. The system classifies, prioritizes, and pre-codes documents based on patterns learned from your senior reviewers. Every flagged document includes the reasoning. Your team reviews the edge cases and makes final calls. The AI learns from each correction. First-pass review volume drops by 60-80%, and your senior reviewers focus on documents that actually matter.
How do we measure success?
We define success metrics with you before building anything. Typical metrics: documents reviewed per hour, cost per document, research turnaround time, and discovery budget variance. We set up dashboards that show before/after in real-time. If the numbers don't improve within 90 days, we have a problem — and we fix it on our dime.
What's the total cost for year 1?
Two options. Per Workflow: $25K-$75K per workflow, best for starting with one high-impact process like document review or precedent research. Dedicated Team: ~$22K/month for 2+ AI engineers embedded in your operation, building multiple workflows continuously. Most practice support teams start with document review automation, prove the ROI, then expand to research and discovery. Every engagement starts with a free 30-minute assessment — no commitment.

Your practice support is too complex for manual processes.

Book a free 30-minute AI Assessment. We'll map your highest-volume workflows and show you the 3 processes with the highest ROI.