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HVAC Data Intake Template
A standardized data collection form for chillers, pumps, and terminal units to accelerate project scoping.
Start Your Project in Minutes
Share your project details and site photos through our simple online forms. We will take it from there.
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Site Walkthrough Checklist
On-site checklist to capture control strategies, point counts, and comfort observations during commissioning prep.
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ClimaMind One-Pager
Company overview and pilot path summary for commercial HVAC owners, operators, and local partners.
Need anything else? Email info@climamind.com and we will help right away.
Complete FAQ
Technical and commercial answers in one place—filter what matters most to your operation.
Technical
Does the AI layer compromise the stability of the existing BMS or plant control?
ClimaMind runs in parallel with your BMS. Every command is constrained by multi-level safety limits and rate-of-change policies. If telemetry is out of bounds, the system reverts to the native sequence, and operators retain manual override at all times.
Technical
What part of the HVAC system do you optimize?
ClimaMind mainly focuses on central plant optimization, especially chiller-side systems. In some cases, we may interact with limited AHU-related control logic, but we are not focused on VAV-level or zone-level control. We work with the data and control points already available in the existing BAS/BMS environment whenever possible, and we generally do not require additional sensor deployment as a starting condition.
Technical
Do we need new sensors or significant hardware retrofits?
Most deployments reuse existing plant instrumentation—supply/return temperatures, flow, and power meters. We add a hardened edge server with UPS backup and networking gear but avoid intrusive sensor retrofits whenever possible.
Technical
How long before energy savings are measurable?
Once telemetry quality, safety limits, and operator approvals are validated, we enable reinforcement learning in a staged rollout. A 30-day alternating-day test or historical baseline comparison then produces auditable savings results.
Commercial
How do the CapEx and shared savings models differ?
CapEx projects include a perpetual license for the edge platform with optional service agreements. Shared savings engagements are 5–8 year partnerships where ClimaMind funds the deployment and is paid from verified energy reductions.
Commercial
How are savings calculated and validated?
We prioritize alternating-day comparisons—AI control one day, native control the next—using utility metering or plant power data. Where historical data is reliable, we also offer multi-year baseline models to satisfy ESG reporting requirements.
Technical
Can the platform run in the cloud?
Yes. ClimaMind can operate fully on-premises via the edge appliance or integrate with a secure cloud environment. Commissioning typically uses an isolated network segment with firewall rules that align to your cybersecurity policies.
Technical
Why does the web dashboard differ slightly from live BMS values?
The web UI is near real-time, not raw direct readout. Data passes through ingestion, quality checks, and aggregation, so short delays are expected. When discrepancies appear, compare the same point, unit, and timestamp window first.
Technical
Can operators issue control commands from the web portal?
Yes, based on role and site policy. Projects can run in read-only, advisory, or closed-loop mode. Any command-capable setup should enforce RBAC, approval workflow, and full audit logging before enabling production write access.
Technical
How are web alerts triggered and managed?
Alerts use threshold plus duration logic with anti-chatter filtering, rather than one-time spikes. Teams can acknowledge, assign, and close alerts with traceable logs, making incident handling auditable for operations and compliance reviews.
Technical
Can we export energy performance reports from the web portal?
Yes. The portal supports daily, weekly, and monthly summaries with exportable charts/tables. For external reporting, include the baseline method and weather normalization assumptions so savings claims remain consistent and review-ready.
Technical
Does the AI control system require a detailed first-principles model before go-live?
The rollout is staged. We start with model-driven optimization to provide stable, verifiable early performance, then introduce model-free reinforcement learning after sufficient site data is accumulated. This balances commissioning reliability with long-term adaptive savings.