Frequently asked questions.

Facilities leaders, building operations and engineering teams, property managers, and maintenance teams responsible for physical assets, particularly in organizations with large equipment inventories.

Educational campuses, government and municipal buildings, healthcare facilities, manufacturing plants, corporate real estate portfolios, hospitality and retail chains, warehouses, and data centers.

Moat scales from medium-sized organizations to large enterprises, typically those with a minimum of 20 buildings or 2 million square feet of facility space under management.

Common sponsors include facility management and engineering leadership, operations executives, finance and capital planning teams, asset and portfolio managers, building owners, property management directors, and architects/developers.

Pricing is based primarily on square footage, but considers number of buildings, asset count, value, and density. Annual subscription pricing includes discounts for multi-year agreements and tiered discounts based on total square footage. No limits on total users.

Moat typically uses annual agreements, though many customers prefer 3-5 year contracts. Pilots and proof-of-value engagements may be available.

Most customers treat it as operational expense (OpEx), though organizations may capitalize portions depending on accounting practices.

$15 in annualized savings for every $1 invested. Average savings estimated between $0.75 - $1.45 per square foot annually, categorized into capital savings via PM, operating savings via PM, operating savings via troubleshooting, and capital and operating savings via warranty utilization.

Moat offers a complimentary facility ROI assessment to demonstrate value using your real asset details before full deployment.

Moat offers implementation options ranging from mostly self-driven to white-glove service. Deployment ranges from a few days to several weeks depending on portfolio size. Phased deployment over months or quarters is also available.

Moat specializes in surfacing insights from forgotten and lost data sources. The platform can convert scattered O&M manuals, invoices, paper files, thumb drives, spreadsheets, and document repositories into usable intelligence.

No. The platform is designed for real-world facility teams with voice-to-text reporting, intuitive interface, responsive mobile design, natural language querying, and role-based views.

Moat provides scheduled check-ins, quarterly value assessments, online documentation, and ongoing customer support with dedicated assistance during business hours.

Moat can fill gaps with publicly-available manufacturer resources where available, though using your own documentation is ideal for version and model alignment.

Adoption is driven by reduced manual data entry, on-demand access to asset-centric details, faster issue reporting, clearer maintenance priorities, and demonstrable organizational impact.

The platform includes mobile-friendly alerts, voice-to-text work order reporting, responsive mobile UI, and voice-enabled chat with the AI engine.

Yes. Moat tracks warranties, alerts before expiration, and provides maintenance activity records as proof of warranty compliance.

Yes. The platform provides capital planning and equipment lifecycle forecasting for proactive budgeting and asset-specific historical logs for operational expense forecasting.

No. Moat focuses on asset intelligence, planning, and preventative maintenance without replacing building automation systems, SCADA, or IoT sensor networks. Execution remains human-driven.

No. Moat is purely SaaS and requires no new hardware, sensors, or building retrofits.

Yes. Moat is cloud-ready and supports third-party integrations, though configurations depend on inter-platform compatibility and available APIs.

Moat uses a hybrid architecture combining natural language processing, document parsing, machine learning models trained on asset and maintenance data, and rules-based logic grounded in OEM standards.

Yes, Moat leverages modern AI techniques, including language models, where appropriate — but results are anchored to source documents rather than free-form generation.

Moat mitigates hallucination risk by training solely on information and data from your portfolio rather than broad sources. The system is designed to extract, infer, and structure — not invent — flagging low-confidence outputs.

Yes. System outputs are traceable back to source documentation, hyperlinked directly to specific documents and pages, allowing teams to audit and validate recommendations.

Data generates insights including asset inventories, maintenance schedules, and forecasts. Information comes primarily from manufacturer O&M manuals with optional customer-specific details. De-identified, aggregated data may improve models; PII is not used in model training.

Customers retain full ownership of their data. Moat acts as processor, not owner.

Moat employs encryption in transit and at rest, secure authentication, and role-based access control.

No. Moat is offered solely as a cloud-hosted SaaS solution with no on-premise version currently available.

Potential comparisons include CMMS, IWMS, and EAM platforms, as well as spreadsheets, legacy databases, and homegrown systems.

Legacy systems require time- and labor-intensive asset discovery and manual data entry, resulting in incomplete inventories. Moat uses AI to preload actionable insights, eliminating long setup timelines, human error, and gaps in operational coverage.

Moat can complement existing systems by providing better data or serve as a replacement depending on organizational needs. Many initially run both systems; those identifying limitations tend to transition fully.

Moat exists because trillions in physical assets are managed using antiquated, incomplete legacy systems, spreadsheets, tribal knowledge, and forgotten documents.

To become the system of intelligence for the built environment, transforming static documentation into living operational knowledge. The company aims to create an ecosystem bringing data from throughout the facility management lifecycle and establish insights from across the FM landscape to enable resource efficiency and reduce waste.

Moat Systems, Inc. was incorporated in 2024 as an AI-native solution built from inception rather than retrofitting legacy tools.

Lehi, Utah.

Via the Book a Demo option or Contact page on the website.

Book a demo, request a pilot, or schedule a complimentary ROI value assessment directly from the website.