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Data hall with rows of server racks, overhead cable trays and cooling units at the far end.

Engineering intelligent data centers at enterprise scale.

Private AI that reads your BMS, EPMS, DCIM and SCADA, explains every incident and prepares the response, inside your facility.

Select a highlighted asset to see the AI at work

Concept photography · illustrative AI overlay

Built with and alongside

Reads from the systems you already runBMSEPMSDCIMSCADAAVEVA PI SystemOPC UABACnetModbusMQTTServiceNowJiraCMMS

The problem

Sound familiar? Five problems every data center team knows.

The cost of getting them wrong keeps rising, and AI-era density makes all five harder.

1 in 5operators say their latest significant outage cost more than $1 million
85%of human-error outages involve procedures not followed, or procedures that were flawed
66%of operators struggle to hire or keep qualified staff
1.54global average PUE, little changed in six years

Sources: Uptime Institute Annual Outage Analysis and Global Data Center Survey.

  • Alarm floodsWhich of these 18 alarms actually matters?One chiller trip lights up four consoles. The shift assembles the context by hand.Costs response time
  • Ageing critical equipmentIt was fine at the last PM.Chillers, CRAH units and UPS strings degrade quietly between inspections.Costs availability
  • Cooling at AI-era densityWe can’t risk raising the setpoint.Dense racks leave little inlet margin, so plants run colder and costlier than they need to.Costs energy and water
  • No failure historyIt’s a new hall. Nothing has failed yet.Without past failures there is nothing to train a model on, so prediction waits for years.Costs months of blind spots
  • Must run air-gappedNothing leaves the site. Full stop.Most AI tools assume the cloud, so security never signs them off.Costs AI that never ships

Solutions

How we unlock value with AI.

Two ways in. Add AI to the consoles your operators already use, or rebuild the incident response around AI agents. Both stand on one foundation, inside your facility.

DCIM · DC1 · Hall 1CH-02 trippedChillers 2/3Max inlet 27.8 °C
CRAH-01CRAH-07CRAH-02CRAH-08CRAH-03CRAH-09CRAH-04CRAH-10CRAH-05CRAH-11CRAH-06CRAH-12ABCDEFGH
NeIO assistantAdded to your DCIM · cites every record

16 racks in Rows G–H run within 2 °C of the 29 °C inlet limit at peak, highlighted on the plan. CRAH-12 is at 94% fan speed with no headroom. The CH-02 work order is open, technician at 06:00.

With CH-03 online the plant keeps N capacity, but no spare.

The assistant works inside the console operators already use, and shows its answer on the floor plan.Illustrative

Peopledecide and approveHeld by policy · routed to ownerApproved
Shift leadApproves the response
Facilities engineerActs on site
NeIO OSPlans the run and routes every exception to the person who owns it.
Your facility · read-only
AI agentsdo the work
Correlation agent18 alarms consolidated into 1 incident
Cooling agentRanks the cause, checks redundancy
Response agentNotifies, updates work, drafts handover
Systemsstay in placeBMS · EPMS · SCADADCIM · topologyCMMS · ServiceNow
NeIO Pulse · on-call phoneP1 CH-02 trip: CH-03 running, Rows G–H recovering, handover drafted.No writes to controllers
One explained incident instead of four consoles. Engineers own every action.
One foundation under bothEach piece answers a barrier above.
  • Correlation and root cause

    BMS, EPMS, DCIM and SCADA alarms matched on one topology, cause ranked with evidence.

    Alarm floods
  • Physics-first prediction

    Degradation caught early, calibrated on your equipment.

    Ageing equipment
  • Cooling under policy

    Setpoint advice with margin checks, held during incidents, engineer-approved.

    AI-era density
  • Synthetic training data

    Models start trained on synthetic and physics-based failures, then calibrate on site.

    No failure history
  • Your boundary

    No external network path. Models run on site.

    Must run air-gapped

Ready-made solutions

Start from a solution already running in production.

Built on NeIO and configured to your facility systems, data and boundary.

Predictive Intelligence Engine screen: failure risk for chillers, CRAH units, UPS strings and pumps over the next 30 days, with a forecast for chiller CH-02 and its likely causeAI agent
Chillers · CRAH · UPS · Pumps

Predictive Intelligence Engine

Predicts chiller, CRAH, UPS and pump failures 7–30 days ahead, with the likely cause ranked by evidence.

Electrical Document Agent screen: a single-line diagram with UPS tags outlined, extraction counts and a flagged breaker-rating mismatchAI agent
Electrical · Single-line diagrams

Electrical Document Agent

Reads single-line diagrams, panel schedules and breaker data, links every tag and flags what doesn’t match.

Illustrative product screens with demonstration data.

Proof

Explore our deployments

Delivery across hyperscale data centers and industrial platforms. Client identities are confidential; detailed case studies are available on request.

Hyperscale data-center campus with substation and generator yard at dusk
Hyperscale · Predictive maintenanceUSA

Early warning, with the root cause at alert time

Challenge: Critical cooling and power equipment needed earlier warning, in a facility with no external network path.

What we did: Physics-first models calibrated on site, attaching the probable cause the moment an alert is raised. Delivered fully air-gapped.

Air-gappedno external path from the facility
At alertroot cause attached
Liquid-cooled high-density racks with coolant hoses
Incident management · Supervisory AISoutheast Asia

From alert to an approved decision, engineer in control

Challenge: Digital-twin outputs had to become decisions engineers could trust and approve.

What we did: A supervisory layer that turns twin outputs into explained recommendations for approval, now part of the provider’s own platform. Local and air-gapped, with no connection to control or safety systems.

30 salert to explained recommendation (design target)
30 + 15anomaly and degradation contexts (design baseline)
New data hall being commissioned, with racks and work lights
Industrial services · OnboardingGlobal

Sites go live sooner when onboarding is automated

Challenge: New sites waited on manual record mapping and integrations rebuilt from scratch.

What we did: Records and datasheets extracted with every field cited, configuration generated from a blueprint, integrations reused. Mapping needs no OT access, so it starts early.

3–5×faster QA on data and document mapping
Day oneinitial models configured and validated on site

Why Codvo

Why Codvo for data center AI.

We specialise in critical facilities, we already run in them, and we stay after go-live.

01Data-center specialistsReal deployments in hyperscale and critical facilities. We know chillers, UPS strings and BMS points as well as models.
02Secure, on-prem, air-gappedModels run inside your facility. Read-only integration, role-based access and a full audit trail.
03No cold startSynthetic and physics-based failure data trains models before your equipment has ever failed.
04Platform plus acceleratorsPre-built connectors, asset models and governance, so use cases go live in weeks.

Explore your data center AI roadmap

Turn data center intelligence into real-world impact.

Bring one power, cooling, capacity or response challenge. In a 45-minute working session we’ll review your environment, show a comparable scenario and agree what a pilot should prove.