We build private, auditable workflows that read court notices, client intake packets, signed forms, email attachments, and matter notes, then prepare deadline tasks and matter updates for attorney or staff approval.
Court notices, signed forms, intake packets, medical records, and client emails arrive in different formats and inboxes.
Staff have to recognize what matters, calendar the next step, and update the right matter before legal work can move.
Privileged facts, client data, and matter strategy need careful handling, not public chatbot workflows.
Industry document automation research highlights contracts, legal filings, case documents, discovery, correspondence, notices, and compliance documents as legal automation targets.
Legal files often include dense clauses, tables, handwriting, signatures, and multi-document case files that require extraction with source grounding.
Legal document automation research emphasizes visual grounding and defensible audit trails, which is why our workflows show evidence before tasks or matter updates move forward.
Extracts dates, parties, matter references, and required actions from notices and filings.
Prepares calendar entries and task drafts with source evidence for staff approval.
Summarizes intake packets, highlights missing forms, and prepares matter setup fields.
Drafts structured updates for case management systems after human review.
Summarizes discovery, medical records, correspondence, and PDFs with page references.
Staff manually read every notice, email, and attachment, then decide the deadline, matter, task owner, and system update.
The workflow extracts key facts, proposes tasks and matter updates, shows source evidence, and waits for approval before anything is written back.
Private workflows can use hosted private-cloud inference, dedicated cloud or VPC deployment, or local/on-prem inference when client data cannot leave your environment.
Avoid billing surprises with clear workflow-based packages. We do not penalize you for using more AI like other vendors.
Every result can carry page references, extracted fields, approval status, and write-back history so the firm can reconstruct who approved what and why.
We choose one document-heavy process with clear volume, risk, and business value.
We configure extraction, source evidence, human review, and safe write-back boundaries.
You see turnaround time, manual steps removed, review quality, and the roadmap for the next workflow.
Founder, Performance AI Lab | Former Meta SuperIntelligence Lab
With 25 years in software engineering and enterprise infrastructure, I've built systems for some of the world's most demanding environments. I founded Performance AI Lab to bring private, auditable AI workflows that reduce manual work and preserve operating know-how to mid-market operators.
If your team is buried in documents, emails, approvals, spreadsheets, or software handoffs, we can help identify the first workflow worth automating.
Private AI workflows for the work that falls between your documents, apps, approvals, and operating knowledge.