Projects / AI Solutions

Explore VDCO Data's built-environment projects and AI-enabled solutions—a source-backed portfolio of engagements, tools, and workflows we have built, tested, and used to solve real business problems and deliver trusted operational intelligence.

Clients

Organizations we have worked with

Built-environment case studies

Selected engagements

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Portfolio · Massport, with WSP

Massport Integrated Network of Digital Twins (MINDT)

The Massport Integrated Network of Digital Twins (MINDT) is a multi-year program to connect the information behind Massport’s buildings, assets, spatial records and operations. Working with WSP, we support Massport’s Digital Technologies Integration Group on data integration strategy, information management planning and AI usage frameworks. We contributed to the Phase 1 strategic plan and roadmap, which is complete, and we continue to support the program through a follow-on bridge phase.

Digital twin strategyUse case portfolioRoadmapResponsible AI

Challenge: Massport’s most important decisions depend on several systems at once: BIM for project records, asset management for equipment history, GIS for location, and operational and business systems for performance and cost. Those records are refreshed on different cycles and reconciled by hand, which slows decisions and erodes confidence.
Approach: Phase 1 set a shared vision and definitions, then built a portfolio of use cases and a phased roadmap. Six starter use cases are sequenced in three waves: trusted foundation, operational intelligence, then passenger journey. Alongside it we developed a Use Case Factory, a repeatable gated process for selecting, defining and validating further use cases.
Value: Phase 1 gave Massport a shared definition of what a digital twin is and is not, a benefits logic tied to decisions rather than technology, a sequenced starter portfolio of six use cases, and a repeatable process for adding more. The bridge phase is now preparing the program restart.

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Portfolio · Massport, with Woolpert

Massport Enterprise Asset Registry

Massport’s Enterprise Asset Registry Definition and Design initiative creates the policy, standards and master list of asset types that let Massport’s systems describe the same asset the same way. Delivered through Woolpert, the work began in February 2026, and we lead it as project lead and asset management consultant.

Asset registryMaster asset listData standardsAsset information governance

Challenge: Asset information sits in separate systems, including asset management, GIS, ERP, BIM, building controls and spreadsheets, with no common classification or identifiers across them. Massport’s strategic plan makes an enterprise asset registry a foundation initiative, so the registry has to exist before downstream systems and field tools can rely on it.
Approach: We follow an asset information maturity path: policy and standards first, then templates and procedures, then process optimization. We classify asset types against UniFormat and OmniClass, assign each type to an information tier, and give each a persistent identifier. We test the list through facilitated workshops and a formal stakeholder review with Massport’s maintenance and capital teams.
Value: Version 1.0 of the Master Asset List was completed after stakeholder review, with a simplified appendix for contracts and an explainer guide for ongoing maintenance. The Asset Information Standard, which defines the attributes captured for each asset type, is the next deliverable.

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Portfolio · Pence Contractors

Pence Contractors AI strategy and roadmap

Pence Contractors asked us to help its executive team get ahead of AI. In January 2026 we led an AI education and visioning workshop with five senior leaders, then delivered a written AI roadmap covering policy, platform, training, a scored portfolio of use cases and implementation proposals.

AI strategyAI literacy and trainingAI governanceAdoption roadmap

Challenge: Leadership wanted AI’s benefits without exposing financial data, client information or proprietary estimating methods, and without staff leaning on tools they did not understand. The workshop produced nineteen ideas, but formal governance, training and infrastructure were not yet in place.
Approach: We opened with executive AI education in plain language, then ran a facilitated session to surface and rank use cases. We scored the nineteen ideas across six dimensions and developed implementation proposals for the top five. The report added a risk-based AI use policy, a platform recommendation, a three-tier training pathway, and a phased roadmap that starts with foundation work and one pilot.
Value: Leadership left aligned on a decision-ready path: a scored use case portfolio, a draft AI use policy, platform and training recommendations, and a “foundation plus one pilot” roadmap with a decision gate before scaling. Workshop to final report took nine days.

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