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.

Across Moat's customer base overall, $15 in annualized savings for every $1 invested, with 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. Returns vary by property type and asset mix; see the multifamily ROI answer below for that segment's benchmarks.

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

Moat's multifamily customers see $7-$28+ in savings for every $1 invested in the platform annually, roughly $0.40-$1.20 per square foot per year. That breaks down into asset lifespan extension ($0.20-$0.40/sq ft/yr), reduced unscheduled maintenance ($0.15-$0.45), better warranty utilization ($0.05-$0.25), and optimized system efficiency ($0.05-$0.10). In a typical multifamily portfolio, roughly 65% of fixed asset value sits in common building systems and about 35% in unit-specific appliances and HVAC.

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.

Each property's fixed assets can be inventoried within 1-2 weeks, depending on staffing. Most multifamily customers take a hybrid approach: Moat completes 100% of the common building systems and about 10% of the unit-specific inventory, accompanied by onsite maintenance staff who receive a 1-2 hour training and complete the remaining 90% on their own schedule. With a two-person Moat team, common building systems take 4-5 days (64-80 labor hours) and unit appliances run about 7.5 minutes per unit, or roughly 45 labor hours for an average 350-unit community. Onsite staff working alone typically need 6-7 days for common systems and about 10 minutes per unit. Moat asks for a 1.5-2 hour property tour up front, plus keys to access mechanical, electrical, water treatment, trash, garage, amenity, roof, and elevator areas.

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.

The onsite team, and the workflow enforces it. Property teams are kept stocked with unassigned Moat QR labels. To register a new fixed asset, a team member applies a label, scans the manufacturer label, and assigns the location. Because maintenance can only be logged by scanning a QR code, an unregistered asset cannot be serviced in the system, which forces swapped appliances and new equipment to be tagged before any work happens. Moat can also conduct monthly or quarterly business reviews to confirm data accuracy and adherence to process.

Moat uses the most durable industrial QR labels available, rated for both heat and light resistance. Indoors they last 50+ years; in high-UV, high-heat, or outdoor environments they last 15+ years, so field failure is rare. When a label is damaged, the onsite property team replaces it. Moat trains every team to apply labels and keeps them supplied with unregistered labels for new, replaced, or expiring assets.

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.

Moat's model logic exceeds 98% accuracy. It is trained on manufacturer spec sheets, OEM warranty and service manuals, and industry-standard useful-life tables such as ASHRAE and IREM/NAA benchmarks, then cross-referenced against each asset's install date, model number, and logged service history. Every AI-driven output is manually audited, and accuracy improves as more real-world assets and service events feed back into the model.

Because most legacy CMMS platforms publish APIs, Moat can push any record those systems are able to receive into their desktop and mobile interfaces. The key data sets include O&M manuals, OEM-specific preventative maintenance tasks and cadences, service histories, warranty expirations, chat-based troubleshooting guides, and capital planning data. Several Moat customers already run legacy work order systems alongside the platform. Where an existing CMMS API or UI layer cannot surface a given data set, Moat can embed an authenticated link inside that system that hands off directly to the Moat desktop or mobile app.

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.

Customers own all core asset data, including model, manufacturer, location, and commission date, and that data is fully exportable. Moat's proprietary layer is the AI-generated and human-curated data built on top of it: manufacturer- and model-specific O&M files, curated PM tasks and cadences, curated warranty expirations, and curated lifespan and replacement-value estimates.

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.

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