PwC consultants needed faster, more-accurate generation of proprietary-language code and lengthy spec documents. Early Gen-AI prototypes lacked real-time feedback, produced inconsistent results (~10% accuracy), and offered little transparency into ROI—undermining user trust.

Background Context

PwC began a firm-wide Gen-AI transformation two years ago, initially building its own plug-in framework. As use-case complexity grew, the team paused to reassess tools that could boost accuracy and deliver better user experiences without steep learning curves.

Why They Chose Us

Crew AI offered a low barrier to entry for non-expert developers yet allowed deep API customization for advanced teams. Native agent-monitoring integrations gave PwC unprecedented visibility into task durations, tool selection, and human-versus-agent effort—crucial for demonstrating ROI.

Implementation Overview

• Re-engineered SDLC workflows with Crew AI agents that generate, execute, and iteratively validate proprietary-language code.
• Embedded agents to draft and refine long functional & technical specifications with real-time consultant feedback loops.
• Leveraged Crew AI’s monitoring stack to track agent choices, run times, and accuracy, feeding KPI dashboards for leadership.

Results Summary

Crew-powered agents boosted code-generation accuracy from 10% to 70%, slashed turnaround time on complex documents, and supplied granular data to prove ROI. Enhanced accuracy and user experience restored consultant trust, accelerating adoption of agentic solutions across PwC.