BKOAI converted PI tag mapping into a structured reasoning workflow and stored the full decision path in a Context Graph, creating reusable decision memory with improved accuracy and traceability.

BKO AI delivers automated fitness-for-service and pipeline integrity analytics that rapidly assess in-line inspection (ILI) data, prioritize remediation actions, and support standards-based inspection and repair decisions.

BKOAI delivered PI Vision–based Operations Surveillance for a large LNG facility, rebuilding PI AF into reusable templates and a standardized asset hierarchy. The result improved remote visibility, faster anomaly detection, and a scalable foundation to replicate Train 1 for Train 2—aligned with Seeq for a single analytics context.

BKOAI streamlined monitoring of an LNG cooldown cargo operation, where piping and tanks were cooled from 80°F to −260°F during ship unloading. By deploying a Seeq dashboard that centralized critical parameters and automated threshold alerts, BKO AI enabled a smooth cooldown while minimizing operational risk. Project Duration: 3 months

BKOAI streamlined commissioning for an LNG facility preparing its first gas-cooldown cargo by building a unified asset registry and aligning all SIS tags across engineering and operations systems. This ensured data integrity, strengthened safety compliance, and accelerated readiness for LNG production.

BKOAI’s developed a simulation-aware FMEA system that combines Deep Search Agents with the Agent-as-a-Service (AaaS) framework to make risk analysis adaptive and data-driven

Used ML models grounded in thermodynamics to forecast power consumption, enabling real-time, proactive efficiency optimization.

We transformed pump maintenance from reactive to proactive. Engineers now rely on smart alerts and diagnostics to prevent failures early.

A fully automated data pipeline streamlined daily commercial operations, reducing manual work by over 90% and delivering real-time insights for faster, smarter decisions.

Modernized data operations to enable independence from legacy systems, replacing critical workflows with faster, scalable solutions on Microsoft Fabric and Databricks—fully integrated with Power BI and PowerApps.

We turn P&ID data into smart knowledge graphs, so engineers get instant, context-rich insights for safer, faster decisions.

Our Deep-Search Agent solves this challenge using aRetrieval-Augmented Generation (RAG) architecture

We helps operators protect critical assets by monitoring check valves — key components that prevent reverse flow and ensure system safety.

The MCP architecture connects LLM agents to engineering simulations through a lightweight client-server framework.

Our multi-agent system maps PI points to physical equipment across SCADA, maintenance, and operational databases.

We modernize power generation data systems for real-time insight and operational efficiency.

Optimized power plants operations through monitoring, proactive maintenance, and staff training, enhancing efficiency and decision-making.

Automated data transfer and calculations across many furnaces, significantly reducing manual labor from several months to just one month, and ensuring seamless adaptability to data changes.

A power generation client needed better market bidding. BKO’s tool uses weather and plant data to optimize bids, boosting profits with 168-hour results via spreadsheet or API.

Templatized asset analytics unify KPIs and calculations, providing consistent insights for operators and managers, leading to better decision-making and improved operational efficiency.

Organizations face challenges in migrating analytics tools. BKO’s methods quickly re-create data architecture, backfilling in weeks, not months, with 53 billion points backfilled in 3 days.

Organizations struggle with asset management. BKO’s Asset Configurator automates this, ensuring consistency, efficiency, and ease of use, empowering SMEs to be more productive.

Automates real-time issue detection for SMEs, enabling quick responses and faster troubleshooting with custom annotations and metadata.

We solved asset monitoring, troubleshooting guidance, maintenance optimization, and efficiency improvement with real-time data, enhancing decision-making and trust.

Creating predictive models is complex and time-consuming. ML Core automates this with AutoML, offering a simple interface for quick, accurate real-time data predictions.