Enterprise AI for Oil & Gas: Turning Raw Telemetry into Operational Margin
Energy never sleeps, and neither do we. STX Next integrates task-scoped, rules-based AI agents and domain-aligned data products directly into your Purdue Model infrastructure. Move past reactive dashboards to a secure environment where every virtual sensor reading and setpoint recommendation comes with an explicit, auditable evidence trail to capture your plant's tribal knowledge before it walks out the door.
Our Services
STX Next builds modern, secure data foundations tailored to your environment. Our solutions ensure trust, visibility, and scalability across predictive maintenance and compliance reporting.
1. Data Mesh & Lakehouse Engineering
Modern, domain-aligned data infrastructure built on AWS, Azure, Snowflake, or Databricks. We replace rigid, bottlenecked central data pipelines with decentralized "data products" where individual operational domains own their data lifecycles, schemas, and quality SLAs.
2. Production & Field AI Agents
Deploying localized, task-scoped industrial intelligence directly to the plant floor and field crews. This includes Root Cause Engines that cross-reference SCADA telemetry with shift logs, and secure, offline-capable mobile Field Service Assistants that instantly parse technical documentation and fleet manuals.
3. Soft Sensor Engineering (Virtual Telemetry)
Leveraging machine learning models to estimate critical, hard-to-measure variables (such as composition, wear states, or internal temperatures) from your existing network of easy-to-measure sensors. Our lab-validated, calibration-free virtual sensors function reliably where hardware sensors drift or fail in hostile environments.
4. Predictive Maintenance & Asset Reliability
Transitioning critical assets from rigid, calendar-based schedules to condition-based maintenance. By training explainable models on historical process telemetry, vibration data, and failure logs, we accurately predict the remaining useful life (RUL) of your rotating fleet, compressors, and furnaces to cut unplanned downtime by up to 20-30%.
5. Back-Office & Financial Operations Automation
Targeted multi-agent orchestrations designed to handle high-volume, rules-based digital workloads up to 10x faster. We build secure, sovereign AI agents using your existing automation stack (n8n, Copilot Studio, Claude) to accelerate supplier releases, ERP reconciliations, compliance reporting, and KYC processing.
6. Cross-Functional Systems of Intelligence
Connecting active field diagnostics and corporate execution layer systems. When a field asset flags a failure risk, the System of Intelligence autonomously initiates the multi-department response: checking technician schedules, mapping parts SKUs, routing procurement POs, and flagging required executive approval tiers.
7. Industrial AI Training & Enablement
Structured, hands-on capability development to drive true internal organizational adoption. We provide a 20-hour Technical Track for engineers focusing on secure integration patterns and quality-first coding with LLMs, alongside a 16-hour Non-Technical Track tailored for operations, finance, and procurement professionals using low-code automation tools. → Discover our AI Workshops.
Tech Stack for Unique Challenges in Oil & Gas
From dynamic scaling requirements, to real-time data processing needs, to compliance & regulatory alignment, we've got you covered.
Microsoft Azure
Snowflake
Databricks
AWS
6 Field-Tested AI Use Cases for the Oil & Gas Industry
Accelerating incident analysis from hours to minutes by autonomously correlating SCADA telemetry with unstructured shift logs.
- What you already run on: Your SCADA and historian infrastructure, shift logs, maintenance records, and existing SOPs/P&IDs.
- What we put on top: An agent that fuses disparate data streams to output a ranked list of probable anomaly causes alongside an explicit, auditable evidence trail.
- What you get back: Precision diagnostics down to minutes, capturing senior-level reasoning as an organizational asset so junior operators excel on day one.
Forecasting the Remaining Useful Life (RUL) of critical equipment to transition from rigid, calendar-based schedules to dynamic, condition-based maintenance.
- What you already run on: Active site sensors (vibration, temperature, pressure), operational historians (like OSI Pi), historical failure logs, and your existing CMMS/EAM systems.
- What we put on top: Explainable machine learning models that continuously analyze real-time telemetry against historical patterns to detect early-stage degradation anomalies long before a standard SCADA alarm trips.
- What you get back: Reduction in unplanned downtime and extended equipment lifespan, allowing you to service critical assets like compressors and furnaces exactly when needed – cutting unnecessary maintenance costs.
Empowering remote field technicians with an offline-capable knowledge agent for instant, localized technical documentation and safety protocols.
- What you already run on: Standard field smartphones, vendor manuals, asset registries, and regional service histories.
- What we put on top: An offline-ready assistant that allows technicians to snap a photo of an asset or ask a question in natural language to extract instant answers.
- What you get back: Maximized first-time-fix rates and reduced asset downtime, ensuring field insights automatically sync back to enrich global fleet history.
An always-on advisor that monitors live process variables to suggest optimized setpoint adjustments within strict safety envelopes.
- What you already run on: Process historians, defined safe operating envelopes, energy metering, and alarm history logs.
- What we put on top: A real-time closed-loop or human-in-the-loop advisor that logs every single setpoint recommendation alongside its specific operational rationale.
- What you get back: Drastically lower energy costs per unit while keeping human operators firmly in control with fully auditable, calibration-free logic.
Multi-agent orchestrations designed to handle rules-based administrative workloads up to 10x faster.
- What you already run on: Core ERPs, CRMs, document repositories, and highly repetitive manual workflows.
- What we put on top: Task-scoped, rules-based agents that execute digital workflows and maintain a comprehensive, 100% transparent audit log.
- What you get back: Reclaimed engineering and administrative hours, allowing your operational volume to scale effortlessly without requiring additional headcount.
Moving away from passive dashboard monitoring into automated, proactive operational intelligence.
- What you already run on: Customer master data, historical asset telemetry, CRM entries, and dispatch systems.
- What we put on top: A system that catches consumption anomalies or failure risks early and automatically triggers the underlying workflow across siloed systems.
- What you get back: Seamless end-to-end resolution – automatically scheduling the right technician, identifying SKUs, routing procurement POs, and closing the loop.
The True Barriers to Industrial AI
The bottleneck isn't the AI model itself but the legacy knowledge gap and the realities of your data pipeline.
Retiring Expertise
The most expensive consultant is the senior operator walking out the door. When veteran engineers retire, their undocumented understanding of your plant’s "personality" goes with them. Without a mechanism to capture this knowledge, the next generation is forced to rebuild that operational baseline from scratch.
Missing Foundations
Leadership teams are under constant pressure to deliver immediate AI ROI, even though upstream sensors and data infrastructure aren't yet scaled to support it.
Trapped Data
Critical operational insights remain siloed inside legacy historians, unindexed PDFs, and handwritten manual shift logs, completely cut off from modern analytical engines. You cannot train a model on data no one trusts.
Adam Krysztopa, PhD
AI/ML Team Leader
We don’t pitch generic, one-size-fits-all Machine Learning models. We isolate specific, high-impact opportunities where condition-based intelligence yields a measurable operational difference, then scale systematically from there.
The Modular AI Maturity Model: Two Paths, One Foundation
AI adoption in oil & gas is not a single project, but a sequence. Each layer is built on what is already working and depends on it. STX Next works across every tier of this model, starting where your data is today. Our starting point is always what you already have, not what a generic AI roadmap assumes you should have.
The Actual Foundation
Before any AI project starts, the data pipeline needs to be in place. Sensors, historian systems, network connectivity, and a place to store and process operational data. Without this, everything above produces unreliable outputs.
Soft Sensors · AI Field Service · Root Cause Agent
First wave of AI agents and virtual sensors supporting engineers directly on the asset.
Automation Stack · Back-Office Agents
Roll out LLM-based agents for finance, procurement, and HR operations on top of your existing automation toolchain.
Predictive Maintenance
Continuously analyzing real-time telemetry against historical patterns to detect early-stage degradation long before alarms trip.
Finance & Procurement Automation
Multi-agent orchestrations handling rules-based administrative workloads up to 10x faster across ERPs, CRMs and document repositories.
System of Intelligence
A dashboard tells you what happened. An intelligence system tells you what to do before it does. Once both tracks are running, they can be connected into a cross-functional system that makes decisions across operations and back-office, not just within one department.
Continuous capability building
AI-Augmented SDLC & Operations Automation · Tech Bootcamp + Business Enablement · Adoption built into every engagement.
Data Foundations: Data Mesh & Lakehouse Integration
Stop letting centralized data become your primary operational bottleneck. We modernize your environment by implementing domain-aligned data products that grant teams full lifecycle ownership of their schemas and quality SLAs.
Validated Results with Global Industrial Leaders
Our teams help global corporations adopt AI solutions responsibly, securely, and cost-effectively. How do we do it? Let our work speak for itself.
One PoC became four production Machine Learning systems in two years
- The Challenge: Legacy data transformation tools failed to process high-volume real-time telemetry from production sites, while rigid calendar-based maintenance schedules drove up costs across 72 olefin furnaces.
- The Solution: STX Next engineered a scalable platform utilizing Azure Data Explorer and a two-stage Machine Learning framework (classification + regression) paired with digital twins and SHAP-driven explainability.
- The Metrics:
- Successfully ingests and processes 100 million telemetry records per day across 11 factories.
- Delivered a 20% reduction in unplanned downtime across production sites.
- Processed over 600 million historical sensor data points down to clean, hourly resolutions.
Chemical & Industrial Manufacturing
Chemical Industry
Linde: Multilingual RAG Knowledge Automation
- The Challenge: Global teams spent hours manually searching through thousands of disconnected regulatory and corporate policy PDFs across multiple countries.
- The Solution: Built a secure, Azure-hosted Retrieval-Augmented Generation (RAG) system that automatically maintains its index and provides verified, source-cited answers.
- The Metrics: Compressed manual search timelines from hours to seconds across 50 countries while maintaining strict data sovereignty.
Making a difference
Our Corporate Social Responsibility (CSR)
The success of our customers and co-workers depends on smooth processes and informed decision-making. We have a roster of seasoned leaders applying the best management and communication practices on the market to our work.
We hold the EcoVadis Bronze Medal, placing us in the top 35% of sustainable companies globally. It validates our commitment to green energy and responsible operations.
Ready to transform your Oil & Gas enterprise?
Let’s discuss your current data infrastructure, site historians, and high-impact automation targets. Speak directly with an STX Next industrial technology specialist.
FAQs
We rely heavily on legacy historians and on-premise SCADA. Do we need a complete cloud migration to start using AI?
No. While we specialize in cloud modernization (AWS, Azure, Snowflake, and Databricks), we explicitly support on-premise and edge deployments that bypass the cloud entirely. We build integration layers over your existing Purdue Model architectures and site historians (like OSI Pi) so you can deploy industrial intelligence without disrupting stable control-room networks.
How do you protect against AI "hallucinations" in high-risk plant environments?
We don't deploy unmonitored "black-box" systems on the plant floor. Our Process Advisor and Root Cause Agents operate strictly within your pre-defined safe envelopes and utilize Explainable AI (via SHAP values). Every single recommendation is logged with a visible, clear evidence trail detailing why an action was proposed, keeping your human operators firmly in control.
Our data is siloed across multiple facilities, and a centralized data team is already a bottleneck. How do you handle this?
We tackle this structural barrier by introducing a Data Mesh architecture. Instead of forcing a single central team to manage a massive data dump, we pivot to domain-aligned data products. Each operational unit owns the lifecycle, documentation, schemas, and quality SLAs of its own data. This eliminates pipeline queues, ensures reliable data ingestion, and makes your telemetry instantly usable for site engineers.
We handle highly sensitive infrastructure, compliance, and asset data. How do you guarantee data sovereignty?
We guarantee 100% data sovereignty. Our solution blueprints are built to run within your own enterprise environments. Whether implementing secure, isolated Azure-hosted RAG networks to parse policy PDFs or deploying enterprise self-hosted automation platforms (such as self-hosted n8n), your proprietary operational data never trains external vendor models.
How do you ensure our field crews and plant operators will actually adopt these tools?
We don't drop technology off at the door; we build organizational adoption directly into our deployment roadmap. In parallel with software integration, we run two dedicated enablement tracks: a 20-hour Technical Bootcamp to teach your engineers quality-first development with LLMs, and a 16-hour Business Track to show operations and finance teams how to build localized, low-code automations independently.
