Proprietary Legal Intelligence designs, builds, and tunes on-premises AI systems for law firms — so your clients' files are read by machines you own, in a server room you control, and nowhere else.
PLI is run by a practicing litigator who uses this exact stack in his own caseload every working day. We sell what we operate.
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Private AI deployments for law firms and legal organizations: on-premises hardware and model selection, document-automation pipelines that produce court-ready output, and retrieval systems built over your own work product. Designed around how litigation actually runs — intake to filing — not around a software demo.
Our open arm. PLI Labs publishes open-weights, expert-pruned model checkpoints optimized for legal work — leaner versions of strong open models with the parts legal work never uses removed — alongside open-source tools for legal document processing and evaluation. Free to download, inspect, and run on your own machines.
Lawyers carry a duty of confidentiality that does not bend to a vendor's terms of service. With an on-premises system, client documents are processed on hardware your firm owns. Nothing is uploaded, retained, or used to train someone else's product.
No per-seat metering, no surprise price changes, no model that quietly changes behavior overnight. Your firm controls which model runs, when it updates, and what it costs after day one: electricity.
Every workflow PLI designs started as a problem in an active litigation practice — drafting, discovery review, financial tracing, exhibit preparation — and was refined under real deadlines before it became something we would put in your firm.
PLI's founder is a South Carolina family-court litigator with more than eleven years in practice. Over the past several years he designed and built a complete local-AI stack for his own firm: a multi-GPU, on-premises inference fleet; a document-automation system that produces deterministic, court-ready filings; retrieval across decades of his own work product; and pipelines for selecting, pruning, and evaluating the models that run on it.
That stack is not a prototype. It drafts, summarizes, traces, and checks real cases under real deadlines. PLI exists because other firms kept asking how it was done — and because the answer should not be "hand your client files to a cloud vendor and hope."
PLI Labs publishes source-level research on legal model pruning, Hugging Face releases, B70 and V100 inference, and conservative legal automation. The research notes summarize the work in plain language and link back to the public GitHub and Hugging Face records.
The full June 18 public drop: PLI Labs documentation, formal model cards, Hugging Face release pages, B70 research, V100 research, and owned-hardware launch profiles.
Why legal-domain model pruning should preserve legal writing, source fidelity, long-context synthesis, and refusal behavior instead of optimizing only for coding benchmarks.
How four Intel Arc Pro B70 cards became a practical local legal AI lane, and why the proven result is a reliable layer-split serving profile rather than a tensor-parallel speed claim.
What older owned GPU fleets can realistically serve, and why topology, backend selection, and KV-cache settings decide whether a local legal AI stack works.
Five public model repositories now document the MiniMax-M2.7 legal REAP candidates and GGUF artifacts for local evaluation.
How legal AI tooling should behave when evidence is ambiguous: stop, record the reason, and require human review.
A short conversation is enough to tell whether on-premises AI fits your firm's size, caseload, and budget.