Use case 1
Verified research and drafting
Builds research packets from linked sources, drafts arguments from those sources, flags citation risk, and keeps the attorney review step explicit.
For managing partners, firm owners, litigators, paralegal leads
03
A private AI workflow layer for small and midsize law firms.
OpenNash helps lawyers and paralegals research, draft, monitor court activity, and keep Clio current while attorneys stay in control of legal judgment, client advice, filings, and approvals.
01
The work that is quietly eating margin.
Verified research, Clio automation, and private AI workflows for firms without BigLaw support staff.
1ChatGPT and Claude are useful, but lawyers still worry about hallucinated citations and confidential matter data
2LexisNexis and Westlaw are expensive, while free public sources are scattered across court, government, and docket websites
3Research findings, emails, attachments, and filings are copied back into Clio by hand
4Court dockets and new orders have to be checked manually, often every day and matter by matter
5A missed order can create a missed deadline, a rushed response, or a malpractice concern
6Scanned PDFs, naming rules, pleading formats, and matter folders create hours of nonbillable document work
02
The closed-loop system OpenNash builds.
Same operating pattern, tuned to SMB Law Firms workflows.
01 Intake
Capture
Read work from the tools your team already uses.
02 Reason
Decide
Classify, retrieve context, choose the workflow, and flag risk.
03 Draft
Produce
Create the response, report, memo, update, or work product.
04 Approve
Control
Route judgment-heavy or sensitive actions through human review.
05 Learn
Improve
Measure outcomes, exceptions, quality, and cycle time.
Feedback loop: every completed workflow becomes a signal for the next version.
03
High-value use cases.
Start where the volume, pain, and business value overlap.
Use case 2
Clio matter automation
Reads matter context, drafts task updates, links documents, prepares calendar items, and keeps the matter file cleaner without silent writes.
Use case 3
Court order monitoring
Checks supported docket and court sources for new activity, summarizes orders, and proposes response tasks or deadlines for human confirmation.
Use case 4
Document naming and filing
Classifies PDFs, emails, attachments, pleadings, and discovery materials, then applies firm naming and folder conventions for review.
Use case 5
Matter summary agent
Creates running matter summaries from Clio notes, filings, correspondence, research packets, and key dates so the next reviewer starts oriented.
Use case 6
Privacy-first AI workspace
Gives the firm a controlled legal agent with audit trails, approval gates, and a stricter private deployment option for confidential matter work.
Note: OpenNash supports legal teams. It does not practice law, give legal advice, file documents, contact clients, or set final deadlines without human review and approval.
Related service pages:
AI agent implementation /
AI agent training
04
Trust, privacy, and source access.
Clear boundaries for confidential legal work.
Privacy without the fine print.
Many lawyers hear that business AI tools do not train on their data and assume that means zero data retention. Those are different promises.
- ChatGPT Business states that business data is not used for model training.
- Anthropic Claude Team states that content is not used for model training by default.
- No model training does not automatically mean zero data retention, no logging, or no administrative access.
- OpenNash can design a stricter private or zero data retention deployment path for confidential matter workflows, with audit trails and attorney-controlled approvals.
One legal agent, many public sources.
OpenNash can connect the research workflow to credible public legal and government sources, then keep source links visible for review.
CourtListener
Opinions, judges, oral arguments, and legal search from Free Law Project.
RECAP
Federal court docket and filing access where documents are available through RECAP.
Federal Register API
Federal regulatory documents and public inspection materials.
GovInfo API
Official publications from the U.S. Government Publishing Office.
Congress.gov API
Federal bills, amendments, members, committees, and legislative activity.
Open States API
State legislative information through a public JSON API.
05
Before and after.
Representative outcomes depend on scope, data quality, systems, and volume.
Before
Manual drag
- Research starts in one system and ends in another
- Paralegals spend hours filing, naming, converting, and re-uploading documents
- Court orders are checked manually and deadlines depend on human memory
- Lawyers use consumer AI but are unsure what is retained, trained on, or logged
to
After
AI-enabled ops
- Research packets come with source links and attorney review checklists
- Clio matter records stay cleaner with less manual document handling
- New court activity becomes a proposed task, not a surprise
- The firm has a clearer privacy path for everyday AI and confidential work
Admin drag
-30-50%
Representative range
Research packet prep
Hours to minutes
Representative range
Court order checks
Daily coverage
Representative range
Sensitive actions
Human approved
Representative range
Next step
Build a private legal AI workflow for your firm.
Book a 30-minute meeting or email [email protected] with the workflow that hurts, the tools involved, and what success would look like in 90 days.