Legal operations

Get the legal record straight.

Matter data, intake logic, contract positions, billing narratives, approvals, and outside counsel work need one governed record before AI can safely help. Novendor builds the operating layer that makes legal work traceable, repeatable, and easier to review.

  • Matter management
  • CLM
  • Intake
  • Billing
  • Playbooks
  • Outside counsel

The situation

Legal work leaves a record. The record is hard to use.

Legal teams already have matters, contracts, policies, playbooks, billing narratives, approvals, and outside counsel notes. The logic behind the work is spread across documents, inboxes, point tools, and attorney memory. AI can draft and summarize, and legal teams need a governed record of what was requested, what position was taken, who approved it, and why.

The operating layer

AI operating layerReads matters, intake, contracts, billing narratives, playbooks, approvals, and outside counsel records; checks them against approved positions; drafts summaries and review packets; and flags exceptions against records your team has approved.
human-approved

1. Source systems

  • Matter managementMatters, owners, status, spend
  • CLM / contract reposAgreements, clauses, redlines
  • Intake forms & inboxesRequests, facts, routing
  • Billing systemsNarratives, time entries, outside counsel spend
  • Playbooks & policiesApproved positions, fallback language, approval rules
Your data already lives somewhere.

2. Pipeline & integration

  • Pull from matter, contract, billing, intake, and policy sources
  • Normalize matter, request, clause, approval, and spend records
  • Track state, handoffs, missing facts, and source freshness
Records move reliably.
Owned end to end.

3. Data modeling & warehouse

  • Data model for matters, contracts, clauses, requests, approvals, and spend
  • Warehouse on Databricks, Snowflake, Azure, or your existing cloud stack
  • Modeled around legal intake, review status, risk, cost, and precedent
Structured. Modeled.
Built for decisions.

4. Semantic layer & governance

  • Approved positions
  • Approval rules
  • Privilege & access
  • Audit trail
  • Approved positions, fallback language, matter taxonomy, and risk levels defined once
  • Access controls for privileged and sensitive records
  • Lineage from summary or packet to source record, with audit trail for decisions and handoffs
One definition.
Trusted by every team.

5. End users

  • General counselMatter risk, spend, and decision visibility
  • Legal opsIntake, routing, SLAs, and outside counsel control
  • AttorneysApproved positions and prior decisions in context
  • FinanceBilling narratives and spend reporting
  • AI agentsDraft, check, and summarize against governed legal memory
Trusted data.
Used by people and agents.
SourceIntegrateModelGovernDeliver

Control · continuity · auditability

Who this is for

The legal team buried in intake

Requests arrive through forms, email, Slack, and hallway conversations. The same facts get re-entered. Status updates depend on whichever attorney remembers the matter.

The GC controlling outside counsel and spend

Billing narratives, matter status, risk, and outcomes live in different places. Finance can see spend, and legal needs the decision record behind the work to act on it.

The team experimenting with legal AI

Attorneys are trying AI for drafts and summaries. The firm needs approved positions, playbooks, prior decisions, and review rules in one governed record before scaling usage.

Proof

Operating pattern

A legal operations team with intake, matter status, billing narratives, and playbook guidance spread across email, trackers, and point systems models requests, matters, approvals, and outside counsel handoffs into a governed record. Legal leadership gains visibility into work, spend, and recurring issues without asking attorneys for manual updates.

Common failure mode

A legal team rolls out AI drafting before centralizing playbooks, positions, approvals, and prior decisions. Outputs vary by user. Attorneys still inspect every answer manually because the system has no governed legal memory behind it.

The risk comes from using AI without a trusted record.

If legal AI still depends on scattered playbooks and attorney memory, let’s talk.

In 30 minutes, we’ll map your matters, intake, approvals, billing narratives, and playbooks into the operating layer legal teams need before scaling AI.