Saba IP · Legal & Enforcement · Product vision

Meet Lex.
Your New AI Enforcement Teammate.

An AI agent that searches for infringements of Saba's protected marks, organizes what it finds, and will in time help the team prepare cases.

Analytics & Decision Intelligence · Vision and operating model · November 2026 · Current and planned capabilities are labeled
01 / The mission

Make enforcement expertise available to the whole team

Lex is an AI agent: software that can plan and carry out a sequence of tasks, such as searching sources and drafting summaries, within rules the team sets. Today the know-how for enforcement sits with a few experienced people. Lex is meant to make it shared and repeatable.

Available nowSearch

Run approved searches for protected marks and record what was and was not covered.

PlannedConnect

Match new findings to past cases, IPMS records, and court outcomes.

PlannedPrepare

Draft briefs, correspondence, and next steps for a person to review and approve.

What we are asking forEndorsement of the staged growth path below, and agreement on how Lex's performance will be judged before it takes on any step that affects a live case.
What Lex does not do: it does not reach legal conclusions, verify evidence on its own, or take action without approval.
02 / The employee experience

What changes in a day's work

Much of the time spent on a matter goes to gathering and reconciling information rather than judging it. Lex is meant to reduce that work so people can spend more time on judgment.

Today

  • Search websites and public sources.
  • Check several internal systems and historical files.
  • Cross-check IPMS (our intellectual property management system), email, and past case records by hand.
  • Confirm that parties, marks, and case status match.
  • Prepare evidence, summaries, correspondence, and follow-ups.
  • Reconstruct the reasoning when management asks.

With Lex (target)

  • Set the target and approved scope.
  • Review sourced findings and a list of what was not checked.
  • Review one case brief with open questions flagged.
  • Approve next steps; Lex prepares the drafts.

Today is six manual steps; the target is four. The right-hand column is the target: the case brief and drafting steps are not yet built.

03 / The knowledge model

Three sources of knowledge

Available nowIndustry practice

Investigation methods, evidence practices, and legal standards. Legal points must be checked against sources for each jurisdiction.

PlannedSaba's own knowledge

Team procedures, case histories, court outcomes, and approved templates.

Partly availableCurrent situation

Public search results today. Later, current IPMS case status, owners, and deadlines.

Trusted knowledge
→
AI reasoning and tools
→
Evidence-based work
→
Human review

Standard software enforces permissions, spending limits, record accuracy, and audit trails. The AI interprets unclear information, judges relevance, and proposes next steps for people to review.

04 / Team-led development

The team teaches Lex how Saba works

The proposed process turns reviewed experience into shared knowledge. It does not turn every opinion into a rule.

Contribute
Case, procedure, correction
→
Review
Expert validation
→
Approve
Named owner and version
→
Test and release
Checked against test cases
Team contributionHow Lex uses itGovernance
Investigation procedureFollows a repeatable, approved methodNamed owner, version, effective date
Previously handled matterRetrieves context and compares a new leadSource record, access controls, up-to-date check
Correction to a findingImproves rules, instructions, or test examplesReviewer's rationale and approval
Judgment or settlement outcomeShows relevant historical contextConfirm identity and legal scope; never assume it applies automatically
Recurring exceptionSuggests a documented escalation or process changeConfirmed across multiple cases before adoption
Important distinction: changing Lex's knowledge library, search index (the store it searches), or instructions is not retraining the AI model. Each change must be explicit, testable, reversible, and approved.
05 / The end-to-end ambition

From possible infringement to case

Discover
→
Reconcile
→
Assess
→
Prepare
→
Approve and follow up
Available nowDiscover and record

Run bounded text searches against protected marks, keep a history of each run, and report gaps.

PlannedCheck against case history

Is this lead new, already handled, previously reviewed, or a repeat? Show the matching evidence and what remains uncertain.

PlannedPrepare the case

Bring together evidence, related matters, missing facts, options, and draft correspondence for approval.

PlannedTrack follow-up

Track assigned tasks, approvals, deadlines, and outcomes through authorized systems.

Illustrative case: BlackRock

Suppose Lex finds a new BlackRock-related listing. A future case-history feature could surface a related past Saba matter and its recorded court outcome. Lex would compare parties, marks, conduct, jurisdiction, dates, and the scope of the judgment, then flag open questions for review.

It must not conclude that the new listing is already resolved just because Saba won a related case. The actual records and the scope of that judgment must be verified first.

06 / Trust and supervision

Responsibility with limits, not unchecked authority

ActivityRule
Search approved public sourcesAutomatic, within approved scope and limits
Interpret and prioritize leadsAI-assisted, backed by evidence, open to review
Link findings to past casesProposed link with a stated confidence level; a person confirms unclear matches
Prepare briefs and correspondenceDraft only, using approved records and templates
Send notices, start proceedings, or change official recordsSeparate permission and explicit human approval required
Change team proceduresGoverned review and release process

When Lex misses something

Lex should show what it searched, what it skipped, and what it could not reach. A missed case should trigger a documented review of the search terms, sources, and follow-up steps. Finishing the planned search does not prove that nothing was missed.

We will test Lex against known relevant cases, known false alarms, and cases where reviewers disagree. Success means saving the team real time without missing cases that matter.
07 / Growth path

Lex's responsibilities grow in stages

A stage opens only after its tests are passed. Each stage adds responsibility and requires more evidence.

StageResponsibilityProof required to advance
1 · Supervised researcherRun bounded searches and report sources and gapsReliable, complete execution and accurate reporting of coverage
2 · Evidence-aware investigatorIdentify relevant leads and prepare evidence for reviewReliably finds known relevant cases; every finding has its sources recorded and tested
3 · Case-aware analystConnect findings to Saba's case historyAccurate matching to past cases; up-to-date records
4 · Operations coordinatorPrepare approved work items, drafts, and follow-upsRole-based permissions, complete audit trails, approval workflows
5 · Supervised operational teammateCoordinate multi-step work and handle exceptionsEnd-to-end testing, a named operational owner, ongoing governance

Current position: early Stage 1. The next milestone is showing that Lex reliably finds known relevant cases, which has not yet been demonstrated.

08 / Performance

Measure outcomes, not just activity

Quality

Share of known relevant cases Lex finds, share of flagged leads that prove useful, how often reviewers agree, and whether each finding has its source.

Reliability

Completes approved work, reports gaps honestly, stops when asked, and keeps a complete audit record.

Operational impact

Review time saved, duplicate research avoided, accuracy of case matching, and timeliness of follow-ups.

Testing will include known relevant and irrelevant examples, difficult cases, incomplete data, and disputed findings. Targets are set by Legal & Enforcement leadership.

09 / Beyond Lex

A foundation other agents can reuse

Lex is the first agent built on ADI's shared controls: knowledge governance, tool access, approvals, testing, and audit trails. The same controls can support agents for other teams, with their own knowledge and rules.

Direction: build AI teammates that combine institutional knowledge, reliable software controls, and human accountability, and measure them by outcomes the business can see.