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SCADA Edge computing and on-prem AI for large footprint uncarpeted industries

Oil patch sunset West Texas Oil and Gas

Connectivity and Edge AI in Oil and Gas

Reducing latency through on-prem autonomous networks,  learning, and delivering high-performance intelligence to the edge of oilfield operations.

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Case study: Connectivity and Edge AI for Oil & Gas Operational Impact

35% Latency Reduction

Improving real-time industrial communication and decision-making speeds across the facility through on-premises AI deployment.

$2M Annual Savings

Significant reduction in operational expenditures through optimized machine learning models and proactively reduced system downtime.

99.9% Uptime

Enhanced reliability for mission-critical industrial networks, ensuring autonomous and continuous operation in demanding environments.

oil patch TX dusty hard country

Field Challenges in hard country

Connect for speed, cost, reliability, and security

Our SLB Ecuador project demanded speed and extreme reliability in remote environments. An autonomous on-premises AI addressed critical infrastructure gaps:

  • Connectivity: Zero-latency edge processing ensures uptime despite unstable satellite links.
  • Security: Hardened physical data fabric with zero-trust architecture protects sensitive OT data.
  • Seamless Monitoring: Real-time autonomous oversight reduced incident response time by over 40%.
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Problems in the Oil Patch:

  • Remote, geographically distributed assets (well pads, pipelines, storage and separator tanks) across multiple sections.

  • Connectivity built from mixed technologies: private LTE/5G, Wi-Fi, microwave, fiber, satellite — often vendor-specific and siloed​

  • Manual troubleshooting requiring truck rolls to remote sites​

  • Limited real-time visibility across edge devices, sensors, cameras, and SCADA systems​

  • No unified context across multi-vendor industrial environments (radios, routers, security, OT systems, cloud dashboards)​

  • Network upgrades and hardening done reactively, not predictively

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Impact on operations and profitability

  • Extremely high downtime cost (production loss)​

  • Long MTTR due to remote geography​

  • Safety risk (gas leaks, overheating equipment, fire risk, cyber intrusion)​

  • Escalating OPEX costs from field technicians and managed service contracts​

  • Automation initiatives (drones, robotics, AI inspection) blocked by unreliable connectivity​

  • Theft of product, site materials

Connectivity impact

Rapid deployment:

  • Portable Kits for2000+ acre connectivity​

  • Deployment in 24 hours​

Vendor-Agnostic Multi-Access​:

  • Private 5G​

  • Point to point (P2P) connections

  • Industrial Wi-Fi, mmWave, Satellite​

Autonomous Operations:

  • AI drivenworkflows​

  • Predictive alerts and root cause analysis​

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Edge Intelligence:

  • Local AI Inference​

  • Leak and Fault Detection​

NaaS model :

  • Subscription based​ 

  • 24/7 Optimization​

Autonomous context-driven Network Operations:

  • Convert manual workflows into autonomous event driven actions and workflows.​

  • Agentic AI to detect issues, diagnose root cause, plans next steps and execute workflows automatically​

  • Customer Specific Knowledge Base (KB) for topology, policy and workflows​

  • Minimal Edge footprint (sensors) with cloud-based intelligence​

  • Workflow Store: A reusable library of proven operational workflows that standardizes how networks are onboarded, operated, and remediated​

  • Proprietary Knowledge Base: Network topology and devices, vendor behavior and edge cases, and real operational runbooks​

  • Context Engine: Maintains shared, real-time context across tools, vendors, andenvironments​

Scale with intelligence, not headcount

Start your ROI journey today. Specialized consultations to deploy autonomous physical AI and optimize industrial connectivity for your operations.

Connect to explore a fit
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