Challenge
A large, well-established engineering and infrastructure firm had built its reputation over decades by delivering complex, multi-year projects across transportation, energy, and water systems. With thousands of employees across global divisions, the company had amassed an enormous trove of knowledge—but struggled to manage and apply it consistently.
Despite investing in sophisticated document management and ERP systems, teams often worked in silos. Valuable insights from project retrospectives, RFP wins and losses, or site-specific innovations were rarely reused. The firm faced duplication of effort, long onboarding cycles, and inconsistent execution across projects. Executives saw this as a risk to both quality and growth.
“We had the knowledge—it just wasn’t accessible at the right time, in the right format, to the right people. We were reinventing the wheel too often.”
Solution
The firm partnered with Accrete to implement a tailored Knowledge Engine agent across its operations, aiming to unify and operationalize enterprise knowledge at scale. The AI initiative was championed not by IT, but by a cross-functional task force spanning engineering, operations, and business development.
The resulting AI agent, internally known as Pulse, became a strategic layer sitting atop the firm’s existing systems, connecting data across functions and surfacing insights in real time. Key applications included:
- Proposal & RFP Optimization: Pulse analyzed past bids, client feedback, and win/loss patterns to help teams craft more competitive proposals tailored to client and industry-specific needs.
- Site Knowledge Retention: The Knowledge Engine captured engineering notes, field reports, and lessons learned from each project phase, making that insight reusable across global job sites. By reusing engineering notes and site learnings, Pulse reduced costly rework incidents by 28%, saving an estimated $3M annually in project correction costs.
- Cross-functional Onboarding: Pulse onboarded new project leads by aggregating playbooks, past project analogs, and relevant regulatory requirements—significantly reducing ramp-up time and errors.
What set Pulse apart was its ability to go beyond search. It reasoned across data sources, retained tacit knowledge from expert interactions, and offered tailored answers based on role and context through its expert agents.
“It wasn’t about just retrieving documents. It was about capturing the "how" behind our success—and making that actionable for anyone, anywhere in the company.”
Results
Within 12 months of implementation, the company experienced:
- A 35% reduction in proposal development time, allowing teams to pursue more opportunities with greater precision.
- Significant knowledge reuse across projects, reducing errors and accelerating execution timelines.
- Pulse accelerated onboarding for new project leads by 45%, enabling earlier contribution and reducing supervision burden on senior staff.
- Improved employee satisfaction and retention, as knowledge flowed more freely and institutional memory became an asset instead of a liability.
The Knowledge Engine became a strategic differentiator, enabling the firm to scale expertise across markets without scaling headcount proportionally.
“Pulse gives us leverage. It ensures we’re not just scaling operations—we’re scaling judgment, insight, and execution quality,” said the leading executive from the firm. Metrics proved it was working with a 35% reduction in proposal development time, which enabled a 40% increase in RFP volume per quarter, without increasing headcount.
Takeaway
Executives recommend treating AI not as a standalone project, but as an enterprise capability. Success came from deeply embedding the Knowledge Engine into individual tasks, team workflows and eventually the entire organization, not layering it on top. They also emphasized the importance of cross-functional ownership and clear outcomes.
“You can’t bolt on AI and expect magic. You have to train it on the heartbeat of your business. That’s what Pulse does for us.”