Deloitte

Deloitte's audit and consulting practices routinely handle due diligence and client advisory engagements that involve reviewing tens of thousands — sometimes more than 100,000 — contracts and complex documents per project. Extracting and structuring the specific data points buried in these documents was done manually by professional staff, creating a process that was slow, inconsistent, and difficult to scale under the compressed timelines that characterize major business transactions. The volume of material required for thorough analysis often outpaced what human reviewers could reasonably process, exposing clients to risk from incomplete review and limiting the firm's capacity to take on high-volume engagements.

In autumn 2014, following pilots in both its audit and consulting divisions, Deloitte rolled out Kira Systems' machine learning contract analysis platform across the organization. Rather than deploying a generic tool, Deloitte built customized instances trained on thousands of firm-specific data points across hundreds of client project types. These Deloitte-trained models were branded internally: Argus for the audit business and D-ICE for consulting, with parallel exploration underway for tax and advisory applications. The platform's Quick Study capability allowed it to read contracts at scale, extract structured information, and generate comprehensive summaries — reducing hours of manual effort per document. Client teams were then able to use these Deloitte-configured instances directly, enabling analysis of hundreds of thousands of documents within weeks rather than months.

Deloitte achieved a 20–90% reduction in contract review time, with the range reflecting variation across document types and project complexity. The deployment scaled rapidly to 3,000 active users across audit and consulting, a milestone reached early in the rollout — signaling broad organizational adoption rather than isolated use by a specialist team. Projects that previously required months of manual document review could now be completed in weeks, strengthening Deloitte's ability to advise clients under tight transaction deadlines. The implementation was recognized externally with the 'Audit Innovation of the Year' award from the International Accounting Bulletin, validating the approach as a meaningful advance in professional services delivery.

• Internal branding (Argus, D-ICE) aligned the tool to specific business units, which accelerated adoption and made the platform feel purpose-built rather than generic.
• Piloting in both audit and consulting before full rollout allowed Deloitte to validate the approach in two distinct workflow contexts before committing to firm-wide deployment.
• Compounding value accrues as more models are trained: each new client project type adds to the platform's institutional knowledge, improving future performance.
• Allowing client teams to use the configured platform directly — not just Deloitte staff — extended the ROI and deepened client engagement.
• The wide 20–90% time-reduction range signals that document standardization and model specificity are key levers for maximizing gains.