About Client
The client is a leading U.S. based commercial construction company with more than a century of industry presence. The company delivers large commercial projects through construction management, preconstruction services, virtual design and construction (VDC), and cost planning. The organization works on large commercial projects where accurate and timely cost insights are critical to ensure project feasibility, maintain margins, and accelerate approvals.
Challenges
The company’s project costing and preconstruction workflows relied heavily on manual processes and disconnected data sources, making it difficult to generate accurate insights quickly.
Cost data was spread across spreadsheets, emails, and documents. Teams spent hours compiling numbers, validating entries, and comparing projects manually. This created delays in analysis and slowed decision-making across projects.
Key challenges included:
- Cost data scattered across spreadsheets and emails with no single source of truth
- Manual compilation and validation of project costing data
- Inconsistent data entries leading to miscalculations and errors
- Slow cross-project comparisons that delayed decision-making
- Lack of real-time visibility for financial planning and variance detection
These inefficiencies meant teams spent more time preparing data than using it for decision-making.
Solution: A focused approach to solving high-impact workflow bottlenecks
Saviant implemented a centralized AI project costing platform designed to unify cost data across projects, automate validations, and enable faster project comparisons. Rather than attempting a full system overhaul, the solution focused on removing the highest-impact bottlenecks first, allowing the client to achieve measurable results quickly.
The platform built using Agentic AI introduced:
- Centralized cost tracking across projects
- Automated validation to improve data accuracy
- Web-based real-time project cost comparisons
- AI-powered document data extraction
- AI-assisted mapping of cost line items to WBS levels
- Conversational AI for users to talk to databases
This created a single source of truth for project costing data while reducing manual effort across workflows.
The platform was rolled out through a focused implementation approach to ensure rapid adoption and measurable impact.
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Started with value design
The preconstruction and project costing workflows were mapped end-to-end to identify where delays, manual effort, and data inconsistencies were affecting decisions. This helped prioritize the use cases that would deliver the highest business value first.
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Defined the architecture for scalability
Saviant’s Agentic AI team designed a centralized and secure data foundation that unified cost inputs across projects. This architecture enabled:
- Automated validations
- Consistent data structure across projects
- Faster cross-project analysis
- Reliable cost comparisons
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Core capabilities delivered first
Instead of building unnecessary features, the Agentic AI development team prioritized the most critical workflows, ensuring them to realize value immediately:
- Cost tracking across projects
- Automated validation checks
- Side-by-side project comparison views
- Structured cost mapping to WBS
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AI capabilities for costing automation
With the centralized platform in place, AI capabilities were introduced to further accelerate costing workflows. These capabilities include:
- Document AI for automated data extraction of cost from project documents
- Conversation AI enabling users to chat with their data using natural language without data going to any external LLMs
- AI-assisted mapping of cost line items to WBS levels
- Faster generation of cost insights
These features significantly reduced manual work and improved consistency across projects.
Key Results
The new AI-powered platform significantly improved efficiency, accuracy, and decision speed across project costing workflows.
- Increased operational efficiency: Cost tracking reduced from 24–48 hours to less than 1 hour
- Faster decision-making: Cross-project comparisons reduced from 4–6 hours to just minutes
- Improved data accuracy: Manual error rates reduced from 10–15% to around 2% with 97% validation accuracy achieved through automated checks
- AI-driven automation: Document data extraction reduced from 30–45 minutes to under 2 minutes with WBS mapping reduced from 1–1.5 hours to about 10 minutes per 100-line items
- Adoption: Onboarded 200+ users across project teams with secure role-based access
By addressing the most critical workflow bottlenecks first, the client was able to transform a fragmented costing process into a centralized, scalable platform. The result was a faster, more accurate costing workflow that enables teams to move quickly from project data to decision-making.
With the platform now in place, the company is positioned to expand AI capabilities further and scale intelligent costing across its project portfolio.