Aderant Launches iTimekeep Integration With Harvey

Aderant announced general availability of its iTimekeep integration with Harvey on August 17, enabling work performed in Harvey to create draft time entries in iTimekeep. The draft entries include matter context, duration, and an AI-generated narrative for lawyers to review, edit, and submit through their firms' existing approval processes. Lisa Erickson, Aderant's SVP of product management and AI, said the integration creates a more connected ecosystem for firms, while Matthew Guillod, senior product manager at Harvey, said it reduces the burden of manual time entry.

Aderant Launches iTimekeep Integration With Harvey Aderant announced general availability of an iTimekeep integration with Harvey on August 17, enabling work performed in Harvey to create draft time entries in iTimekeep. LegalTechnology.com reports that entries include matter context, duration, and an AI-generated narrative for lawyers to review, edit, and submit through their firms' existing approval processes. Aderant announced the general availability of its iTimekeep integration with Harvey on August 17. The integration allows law firms that use both products to turn work captured in Harvey into draft time entries in iTimekeep. According to LegalTechnology.com, the generated draft includes matter context, duration, and an AI-generated narrative. Lawyers review, edit, and submit the entry in iTimekeep under their firm's existing approval process. LawNext similarly reports that the integration applies to work performed in Harvey, including drafting, research, review, and analysis. Lisa Erickson, Aderant's SVP of product management and AI, said in a statement quoted by both publications: "Harvey already understands the work our clients' attorneys are doing every day. Integrating that knowledge directly into iTimekeep creates a more connected ecosystem for firms, enabling users to achieve more accurate time capture without requiring any changes to their billing process." Matthew Guillod, senior product manager at Harvey, said the integration is intended to reduce the burden of manual time entry: "Entering time is painful, and this is the first step towards solving that pain," according to LawNext and LegalTechnology.com. Linking legal AI work to billing records The release extends an Aderant-Harvey partnership announced in December, which LawNext characterized as connecting software used to operate law firms with software used to perform legal work. For legal AI and workflow teams, the integration places an AI-generated artifact, the activity narrative, inside a human review and approval process rather than directly posting billable time. That design follows a common pattern in professional-services automation, where generated records require practitioner validation because matter attribution, duration, and billability can affect client invoices and internal compliance. The public reporting does not describe the underlying model, data-retention controls, or the integration's mechanism for identifying and measuring work performed in Harvey. Those implementation details are material for firms evaluating governance, auditability, and deployment requirements. Key Points - 1Aderant's generally available integration converts Harvey activity into editable iTimekeep drafts, connecting legal AI work capture with timekeeping workflows. - 2Draft entries include context, duration, and an AI-generated narrative, while lawyers retain review and submission control under firm approval processes. - 3Comparable professional-services automations commonly preserve human validation where generated records affect billing, compliance, and client-facing invoices. Scoring Rationale The integration is a concrete legal AI workflow deployment that connects work performed in an AI platform to a billing-adjacent system. Its relevance is strongest for legal-tech teams and practitioners designing reviewed, auditable AI-assisted operational workflows, rather than for the broader ML ecosystem. Sources Primary source and supporting public references used for this report. Practice with real Ad Tech data 90 SQL & Python problems · 15 industry datasets Active Search Campaigns by BudgetEasy /problems/sql/active-search-campaigns-by-budget High CPC Clicks & Poor Landing PagesMedium /problems/sql/high-cpc-clicks-poor-landing-page Campaign ROAS by Attribution ModelHard /problems/sql/campaign-roas-by-attribution-model 250 free problems · No credit card See all Ad Tech problems /problems/datasets/adtech