AI for Energy operations
Sphere brings the engineering rigor of national-security programs to the energy sector – building secure AI, ML, and data infrastructure for the operators powering the grid, the pipeline, and the transition.
SOC 2 Type II
complaint infrastructure
FedRAMP-aligned
control mappings
NERC CIP
reference architecture
ISO 27001
and 27701 ready
SOC 2 Type II
complaint infrastructure
FedRAMP-aligned
control mappings
NERC CIP
reference architecture
ISO 27001
and 27701 ready
Organizations around the world trust us
The Operating Reality
Energy is becoming an AI problem – under adversary-grade conditions.
The same OT systems that move barrels and electrons are now expected to be intelligent, connected, and resilient. Six pressures dominate every conversation we have with energy leaders.
1
OT/IT cyber exposure
Legacy SCADA and modern cloud analytics are colliding. Most architectures weren't designed for both – and the threat surface is now nation-state grade.
2
NERC CIP & regulatory load
CIP-013, CIP-014, and incoming AI guidance are reshaping vendor risk, asset classification, and audit evidence – quickly.
3
Fragmented operational data
Historians, ERP, GIS, market feeds, IoT – all real, all needed, almost never joined. AI without a unified data spine fails in pilot.
4
Predictive maintenance gaps
Failure data is rare and skewed; physics-only models miss surprises; LLM-only models hallucinate. Real PdM needs a hybrid stack.
5
Energy-transition complexity
EV charging, batteries, hydrogen, carbon – new asset classes with no historical baselines, all needing forecast, optimization, and trust.
6
Institutional knowledge loss
The engineers who tuned the refinery, the grid, the well – are retiring. Capturing that judgement in models is now a board-level concern.
Our Solutions
Five AI systemsOne intelligent energy operation
Each module works standalone or as an integrated platform — connecting forecasting, maintenance, storage, and design into a single AI brain.
Capability 1
Mission-Critical Machine Learning
From lab notebook to 24/7 production.
Production ML systems that survive contact with real operational data, real adversaries, and real auditors. Designed to run for years, not just demos.
99.95%
Inference uptime SLO
<150 ms
Edge inference latency
12wk
Median prototype to production
What's inside
- MLOps pipelines with full lineage and reproducibility
- Drift, fairness, and adversarial-input monitoring
- Model cards aligned to NIST AI RMF
- Hybrid physics-ML for low-data regimes
- Air-gapped training and deployment supported
- Independent V&V workflows for regulated assets
Capability 2
Secure Data Infrastructure
Air-gapped, edge, hybrid cloud.
A unified data spine that joins OT historians, ERP, IoT, market feeds, and unstructured documents – under controls borrowed from defence-grade environments.
PB-scale
Time-series throughput
Zero
Cloud-egress required
100%
Lineage on prod tables
What's inside
- OT-aware ingestion (PI, OSIsoft, Wonderware, OPC-UA)
- Air-gapped, edge, and hybrid-cloud topologies
- Fine-grained RBAC and ABAC with audit trail
- Encryption in transit, at rest, and in use
- Data contracts and quality SLAs by domain
- Reference architectures for FedRAMP and CIP scope
Capability 3
Predictive Analytics & Digital Twins
Physics-plus-ML hybrid models.
Hybrid models that combine first-principles physics with data-driven learning – so they extrapolate honestly when the world drifts off-distribution.
−40%
Unplanned downtime (typical)
3–7×
Lead-time on failure alerts
+8pts
Asset utilization uplift
What's inside
- Equipment-level digital twins (rotating, static, electrical)
- Survival, anomaly, and remaining-useful-life models
- Reservoir, grid, and pipeline scenario simulators
- Calibration against historian and maintenance records
- Operator-explainable outputs, not black boxes
- Designed for failure-rare data (rare-event ML)
Capability 4
OT/IT Convergence Security
Zero-trust for real plants.
Zero-trust patterns adapted for environments where you can't just patch the PLC. Built on standards and informed by years of defence-grade red-team thinking.
<24h
OT asset inventory coverage
100%
Sensitive flows monitored
L1–L4
Purdue-model coverage
What's inside
- Asset discovery and passive OT fingerprinting
- Network segmentation and conduit hardening
- AI-based anomaly detection on OT protocols
- NERC CIP-013 vendor-risk automation
- Incident-response runbooks tailored to ICS
- Red-team and purple-team exercises for energy assets
Capability 5
Real-Time Decision Support
Operator-grade copilots.
Operator-grade copilots and decision tools built for the control room – fast, traceable, and always pointing the human at the right decision.
<2s
Recommendation latency
92%
Operator-rated trust score
+22%
First-call resolution uplift
What's inside
- Streaming inference from sensor and market data
- Recommendation systems with audit trail
- Natural-language interface to historians
- Shift-handover and incident-summary copilots
- Human-in-the-loop with confidence reporting
- Integration with EMS, DMS, and DCS systems
See Defence-grade AI for mission-critical energy in Action
A 20-minutes walkthrough on a sample commercial buildings: search, route and asset lookup from tech’s phone.
Five stepsRoughly 90 to 180 days.
From first conversation to a production pilot to a scaled operating platform – using an engagement pattern proven on supermajor and DoE programs.
1. Discover & Diagnose
Two-week working session with your engineers and our practice leads. We map data flows, threat surfaces, and the highest-leverage decisions to attack first.
Deliverable | Energy AI Opportunity Scan
2. Architect & Secure
We design the data spine, model architecture, and security baseline together. Reference patterns aligned to NERC CIP, FedRAMP, and NIST AI RMF before any code ships.
Deliverable | Reference Architecture
3. Pilot in Production
We ship a working pilot in 8–12 weeks against a live operational use case – not a sandbox demo. Hardened, monitored, integrated with your OT and IT stack.
Deliverable | Production Pilot
4. Scale & Operationalize
Once one use case proves out, we replicate the pattern across assets, sites, or business units – backed by a managed MLOps platform your team owns.
Deliverable | Scaled Platform
5. Govern & Improve
Continuous monitoring, drift detection, evidence collection, and quarterly model reviews. Your AI estate stays audit-ready and improves against business outcomes.
Deliverable | Continuous Assurance
Industries we serve
Four energy segments, one disciplined approach.
We work across the value chain – from upstream wells to last-mile EV chargers – applying the same engineering standard to each.
Oil & Gas
Upstream to downstream
Squeezing the last few points of efficiency out of mature assets – without compromising integrity or HSE.
Reservoir & production optimization
Forecast decline curves, recommend choke setpoints, surface actionable interventions for production engineers.
Pipeline integrity ML
Anomaly detection on pressure, temperature, and pig-run signals for leak and corrosion risk.
Refinery yield & energy intensity
Hybrid physics-ML models for unit-level optimization and CO₂-per-barrel reduction.
Power & Utilities
Grid, generation, nuclear
For ISOs, IOUs, and generators carrying the load of decarbonization while keeping the lights on.
Grid stability & load forecasting
Sub-hourly demand and renewables forecasting with weather-coupled deep learning.
Renewables integration
Curtailment minimization, DER orchestration, and reactive-power optimization at scale.
NERC CIP compliance automation
Asset classification, evidence collection, and continuous controls monitoring out of the box.
EV Infrastructure
Charging at scale
Scaling networks from hundreds of sites to tens of thousands without losing margin or uptime.
Site selection & demand forecasting
Multi-modal models combining traffic, demographics, fleet trajectories, and competitor density.
Charging network operations
Predictive uptime, dynamic pricing, and smart-charging schedules tuned per site profile.
V2G & grid-services orchestration
Bidirectional flow optimization across fleets, batteries, and wholesale markets.
Energy Transition
Batteries, carbon, hydrogen
Building the data and decision layer for asset classes that don't yet have decades of telemetry behind them.
Battery degradation & warranty models
Cell-level state-of-health forecasting across stationary storage and fleet portfolios.
Carbon-market analytics
MRV pipelines, project quality scoring, and price discovery across compliance and voluntary markets.
Hydrogen & new-fuels logistics
Network design, demand modelling, and risk analytics for emerging molecule supply chains.
ROI & Business Impact
Composite outcomes from active and recent engagements across O&G, utilities, EV, and energy-transition portfolios.
$4.2M+
Average annual OpEx savings per refinery or large asset under management.
+18%
NPV uplift on EV site-selection decisions vs. heuristic baselines.
−42%
Reduction in unplanned downtime across deployed predictive-maintenance programs.
3–5×
Faster time-to-production than traditional systems-integrator engagements.
Hear from
our clientsHear from our clients
Lee Ebreo
VP of Engineering at Credit Ninja
These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.
Selah Ben-Haim
VP of Engineering at Prominence Advisors
Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).
Ben Crawford
Senior Product Manager at Enova Financial
I would expect to be delighted. It's been a really positive experience, working with Sphere, and I would expect you to have the same.
Mark Friedgan
CEO at CreditNinja
Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. As an offshore team, they have been an integral part of our organization and we plan to continue growing with them.
René Pfitzner
Co-Founder at Experify
Sphere provided excellent full-stack development manpower to augment our team and help push our product forward. They are easy to work with, tech-savvy and proactive.
Bruce Burdick
Chief Information Officer at Integra Credit
We've been working with Sphere and its excellent consultants since our founding. I've found that they are true partners in the success of our business.
Jemal Swoboda
CEO at Dabble
The resources and developers that Sphere Software provides are skilled and have the required technical expertise, but more importantly, they have helped us build a culture of excellence within our team.
Arthur Tretyak
Founder and CEO at IntegraCredit
With Sphere, we were able to migrate in half the time it would take to train an additional FTE… and for a fraction of the cost. Our experience with Sphere has been exceptional.
Lee Ebreo
VP of Engineering at Credit Ninja
These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.
Selah Ben-Haim
VP of Engineering at Prominence Advisors
Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).
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Sphere in Numbers
We understand that actions speak louder than words and numbers but here are some key facts about us.
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Years of Excellence
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Projects Delivered
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Countries
Globally diverse, community-focused
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Clients
top 20 average 8+ years
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