Knowledge Engine for Legal

Move agreements forward without losing control of risk.

Connect contracts, playbooks, approvals, customer context, and obligations into one reviewable legal workflow.

KEP

Commercial agreement review

Review the enterprise agreements blocking this quarter’s deals and identify the issues that need legal leadership.

I connected contract versions, playbooks, deal context, security reviews, approvals, and prior positions. I’m preparing the risk brief now.

Commercial agreement risk briefReport

58%

lower token cost

when using Knowledge Engine

2x

more tasks

vs. frontier models

32%

more accurate with open source

vs frontier model

19 of 34

tacit-knowledge facts captured

vs. RAG’s 1

Source: “Knowledge Engine Platform for Long-Horizon Professional Agents” and “Context Graphs for Recovering Tacit Organizational Knowledge”, Accrete AI, August 2026.

Contract risk hides in versions, handoffs, and missing context.

Legal teams assess language in the context of the customer, commercial terms, security commitments, prior positions, approval matrices, and future obligations. When those records are scattered, cycle time grows and risk becomes harder to see.

Legal cognitive fabric

Connect the context behind the work.

Agents learn the language of the work, create the data flows that connect its systems, and build a living context graph for each request.

Agents learn your language

They learn your teams’ terms, shorthand, and ways of working, then create a shared understanding of what everything means.

Agents connect your systems

They create the data flows that bring the right information together for each task, without manual setup for every source.

Agents build the context graph

They connect people, records, decisions, and outcomes while preserving a clear path back to the source.

From domain language and source data to a context graphThe context model resolves jargon, synonyms, abbreviations, and intent into shared entities and relationships. Source mappings connect those concepts to tables and keys while agentic data pipelines prepare durable data. The resulting context graph can be virtualized over its sources or materialized.NATURAL-LANGUAGE REQUEST“Which agreementsare blocking deals?”CONTEXT MODELDomain languageJARGON · SYNONYMS · INTENTMSA→master agreementedit→contract changeintent: find legal blockersCanonical modelENTITIES · RELATIONSHIPSContractDealTermGOVERNSBLOCKSSemantic bindingsCONCEPT → SOURCE DATAContract→DocuSignDeal→SalesforceTerm→NetSuitetables · columns · keysAGENTIC DATA PIPELINESCONTEXT GRAPHDocuSignSalesforceNetSuiteAgreementsDealsTermsJOINLegal contextAcme MSACONTRACTQ2 RenewalDEALLiability capTERMS. KimOWNERVIRTUALIZEDquery sources in placeMATERIALIZEDpersist selected subgraphs
The context model interprets a domain’s vocabulary, synonyms, abbreviations, and intents, then resolves natural-language requests to shared entities and relationships. Source mappings connect those concepts to tables, columns, keys, dialects, and execution integrations. KEP can query the resulting context graph virtually over source data or materialize selected graph data.
  • DocuSign
  • PandaDoc
  • Salesforce
  • Google Workspace
  • Microsoft 365
  • Slack

Workflows

Change the work, not just the interface.

01

Triage intake and risk

Gather the request, agreement, counterparty, deal context, policy requirements, and approval path; then classify the work and prepare the legal review queue.

02

Review against approved playbooks

Compare language with clause standards and prior approved positions, surface material deviations, and prepare proposed edits and questions for counsel.

03

Track obligations and exposure

Connect executed agreements to owners, renewals, commitments, privacy and security terms, spend, and reporting so obligations do not disappear after signature.

One end-to-end example

From scattered agreement context to a counsel-reviewed position.

  1. 01

    Connect

    Gather the latest contract, version history, clause playbook, commercial context, security and privacy review, approvals, and relevant precedent.

  2. 02

    Analyze

    Identify material deviations, compare approved positions, trace related commitments, and separate routine language from issues requiring counsel.

  3. 03

    Deliver

    Create the risk brief, clause comparison, proposed redlines, open questions, owner list, and negotiation summary.

  4. 04

    Control

    Keep legal advice, redline acceptance, external communication, signature, and obligation changes under authorized counsel review.

What the team gets

Outcomes people can inspect and use.

Intake and risk brief

Matter context, material terms, policy requirements, risk classification, approvals, and next actions.

Clause review package

Source-linked deviations, approved precedent, proposed language, open questions, and counsel decisions.

Obligation and exposure view

Executed commitments, owners, dates, renewals, exceptions, and evidence available for review.

The Accrete product

Context, work, and control in one path.

The Knowledge Engine organizes contract context, review work, and obligations while keeping legal judgment, negotiation positions, signatures, and consequential actions with authorized people.

Start with one workflow

Define the outcome before expanding the system.

Bring the workflow, its information sources, its approval requirements, and the result your team needs. We’ll map a focused first deployment and the path to reuse.

Discuss this workflow