PlantOps: AI-driven operational efficiency for chemical manufacturing
Get real-time operational insights, intelligent automation and unified data flows across your chemical manufacturing using our AI and ML-driven soltion.
Deploy it across your facilities to get actionable insights and achieve strategic advantage faster.
Partnerships and certifications
BENEFITS
AI-based catalyst for better performance
PlantOps provides a unified data and intelligence layer that connects OT and IT systems across your facilities. It standardises analytics and AI workflows, allowing every line, unit and site to operate from the same trusted models, metrics and governance framework.
Real-time quality prediction
Reduce defects and improve specification compliance before issues appear. Use AI and ML models based on your most influential process parameters to support predictive quality control.
Dynamic process optimisation
Continuously adjust batch and process parameters to maintain stability, increase throughput and respond to variable production conditions. Golden Batches and operating envelopes are identified automatically.
Integrated data landscape
Remove data silos by integrating with PI System, MES, LIMS, ERP and CMMS/EAM. Gain full operational visibility without replacing established infrastructure.
Advanced contextualisation and analytics
Move beyond standard Asset Framework structures. Apply multi-source, domain-specific context to production data to turn raw values into actionable operational knowledge.
Gap analysis and missing data detection
Detect incomplete or missing data that restricts optimisation, especially in areas linked to quality, yield and sustainability KPIs. Understand where key insights are limited.
Cost savings and efficiency gains
Use data-backed recommendations to lower energy use, reduce raw material consumption and minimise waste. Strengthen quality management with earlier, proactive validation.
Flexible deployment with your control
PlantOps can be deployed on-prem, in hybrid setups or at the edge. This flexibility helps you integrate AI wherever it brings the most value without changing existing control architectures.
Rapid time-to-value
Industrial integrations and pre-built components support quick adaptation to your environment. Deployment times are reduced so ROI can begin earlier.
FEATURES
Elements that strengthen your operations
Pre-built, certification-ready components accelerate implementation in regulated environments and reduce the effort needed for audit preparation. All delivered codebases follow ISO and IEC guidelines from the start.
AI and ML-powered intelligence
Our solutions uses machine learning models trained on correlated process variables specific to chemical manufacturing. They can predict quality outcomes, optimise parameters and detect anomalies in real time.
Importantly, they can be tailored to your needs.
Low-effort system integration
Designed to connect seamlessly with your current infrastructure:
- AVEVA PI System and OSIsoft Asset Framework
- MES platforms
- LIMS and laboratory data systems
- ERP systems (SAP, Oracle and others)
- CMMS/EAM platforms
- SCADA/DCS control systems
Modular architecture
A scalable structure built to evolve with your organisation:
- Add new capabilities without reworking the core
- Support parallel development of independent modules
- Scale from a single site to multi-facility operations
Golden Batch and operating envelope identification
Automatically identify the conditions that deliver optimal production results:
- Top-performing batches in batch operations
- Operating envelopes for continuous processes
- Data-backed efficiency benchmarks
Cybersecurity by design
The platform supports organisations operating in regulated environments by ensuring full data governance and sovereignty. You retain control over all operational data and model outputs, with deployment options that meet your compliance requirements.
- ISO 27001 alignment
- IEC 62443 compliance
- Early threat modelling and risk profiling
Monitoring and retraining pipelines
Built-in monitoring and retraining pipelines help your AI and ML models stay accurate over time. As process conditions, equipment behaviour or feedstock properties shift, the platform updates models to maintain reliable predictions and stable performance.
Predictive maintenance
The platform supports predictive maintenance through anomaly detection, advanced contextualisation and equipment-level modelling. It helps you identify early warning signs, schedule interventions and protect uptime.
IMPLEMENTATION
Establishing your operational formula
Phase 1: Discovery and data activation (3-6 weeks)
Scope:
Onboarding data sources into the PlantOps data layer and preparing the first Golden Batch model.
Deliverables:
- Stakeholder workshop summary and agreed objectives
- Defined business high-impact case/s
- IT/OT landscape and data availability assessment
- Data source mapping (PI Systems, MES, LIMS, ERP)
- Baseline data quality report
- Use-case data mapping specification
- Model prototype(s)
- Connectivity and security compliance plan
- KPIs & metrics definition
- Model architecture and ROI outline
- PlantOps demo
Phase 2: Configuration and module deployment (3-6 weeks)
Scope:
Rollout of PlantOps modules for Golden Batch, dashboards and integration.
Deliverables:
- Selection of target production lines or units
- Configuration of integration layer
- Adaptation of solution in selected environment
- Plug & Play Model Inference
- Configuration of dashboards
- Iterative feedback and tuning with your teams
Phase 3: Full-scale rollout and capability transfer (1-2 weeks)
Scope:
Enabling your teams to operate, interpret and extend PlantOps. During full-scale rollout and handover, the complete codebase is transferred to your organisation under a highly permissive licence that supports future development without vendor lock-in.
Deliverables:
- User training
- Operational handover
case studies
Lab-to-plant examples
Quality prediction
Challenge: Out-of-spec batches detected only after lengthy curing steps, causing waste and delays.
Approach: ML models predicting key properties from early-stage parameters and intermediate results.
Expected impact: Earlier detection, potential for in-process corrections, reduced out-of-spec production.
Advanced contextualisation
Challenge: Disconnected plant systems preventing comparison and best practice sharing.
Approach: Unified data platform linking PI historians, MES and LIMS across facilities with consistent contextualisation.
Expected impact: Enterprise-wide visibility, reliable performance benchmarking and faster adoption of best practices.
Data-driven efficiency gains
Challenge: High variability leading to inconsistent quality and frequent off-spec periods.
Approach: Statistical identification of optimal operating envelopes supported by real-time monitoring.
Expected impact: Improved stability, reduced variability and higher first-pass yield.
Meet our experts
With over 20 years of technical and consulting experience emphasising innovation and digitisation in business, I have expertise in AI services, AR/VR applications, and IT modernisation across various sectors, including robotics and the chemical industry. I am a strategic thinker adept at optimising production costs, introducing sustainable solutions, and enhancing sales support through technology and efficient data management.
As a people-focused leader, I understand that building cooperation around an accomplished team is the key to developing reliable business relationships and delivering solid results.
Meet our experts
I am a highly experienced Software Engineer with a distinguished career spanning over 15 years. Currently, I hold the role of Enterprise AI Consultant, where I leverage my extensive expertise to create cutting-edge AI solutions tailored for business needs. My specialised skills encompass a robust proficiency in data quality automation, the development of generative content systems, and the design and implementation of conversational AI technologies. My work reflects a deep commitment to innovation and excellence in the rapidly evolving field of artificial intelligence.
NEXT STEPS
Ready to transform your operations?
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Schedule a discovery call
Meet with our chemical manufacturing specialists to discuss your challenges and goals.
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Receive a custom assessment
We’ll review your IT/OT environment, data readiness and high-impact opportunities.
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Review your tailored proposal
Get a clear implementation roadmap, timeline and transparent cost structure.
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Begin pilot deployment
Start with a focused pilot to demonstrate measurable value before expanding further.
Contact
Unlock the potential in your production data
When raw material prices are stable, a dosing overfill of 0.5%, giving the customer slightly more product than specified, is often treated as a negligible cost of doing business. However, when tariffs push material costs up, this “giveaway” becomes a direct leak of net profit. Our PlantOps platform monitors dosing scales and flow meters with millisecond precision, enforcing precision dosing guardrails. By tightening the acceptable tolerance from a standard ±2% to a firm ±0.5%, PlantOps effectively recovers 1.5% of high-value stock that would otherwise be given away. This reduction offsets the unit price increase caused by tariffs.
Higher tariffs mean the cost of scrap increases proportionally. A failed batch is no longer just a production delay; it represents a much greater financial loss because inputs were purchased at tariff-inflated rates.
Through real-time mass balance monitoring and early anomaly detection, PlantOps alerts operators to process drifts, such as temperature or pH deviations, minutes before they become critical. This enables immediate corrective action to save the batch, ensuring costly raw materials are converted into sellable product rather than hazardous waste.
Tariffs often affect steel, aluminium and machinery components, not only chemicals. Replacement parts and new equipment (CAPEX) can therefore become 20–30% more expensive.
By deploying predictive maintenance models, PlantOps monitors the health of critical assets, reactors, pumps and piping, to detect wear early. The aim is to extend the lifecycle of existing equipment through timely maintenance, delaying the need for costly replacements during high-tariff periods.
Changing tariffs and raw material fluctuations are external forces that plants cannot influence. We cannot dictate the spot price of monomers or steel. However, PlantOps gives you precise control over how efficiently these increasingly expensive resources are used. In an environment where tariffs can erode 10–25% of gross margin, PlantOps acts as an operational shield, mitigating financial impact through disciplined process execution rather than financial speculation.
PlantOps is an industrial AI platform designed for manufacturers. It provides a configurable data and intelligence layer that connects OT and IT systems to support optimisation, monitoring and decision-making.
The platform helps improve process visibility, identify anomalies, reduce downtime, support maintenance planning and apply predictive models to quality and performance challenges common in chemical and process industries.
PlantOps includes full data governance features. It enables manufacturers to maintain data sovereignty, adhere to internal policies and align with recognised industry standards. The platform can be deployed on-premises, in hybrid setups or at the edge.
Yes. The platform connects to a wide range of industrial data sources, including historians, SCADA, IoT devices, laboratory systems and enterprise applications. It supports ingestion, standardisation and processing across these data sets.
Spyrosoft provides implementation services, integration expertise and long-term support. The company also assists with scaling the platform to new sites and ensuring governance and compliance remain consistent.
Many plants manage data across separate systems such as DCS, MES, LIMS and ERP. Integrating these sources allows teams to monitor process, quality and cost metrics in real time. Spyrosoft supports this by designing unified data architectures and dashboards that provide live deviations, recommendations and cost-impact indicators.
Chemical manufacturers face fluctuating tariffs in energy, water, waste processing and raw materials. These changes raise operating costs and make production planning less predictable, especially when plants lack a unified view of cost drivers.
When tariff levels change throughout the day, companies can shift certain operations to lower-cost periods or adjust process parameters to reduce consumption during expensive intervals. Spyrosoft enables this through AI-driven optimisation models, predictive quality tools and “Golden Window” detection used in manufacturing optimisation projects.
Predictive models help manufacturers understand how tariff variations influence batch costs, energy use and throughput. These models can forecast outcomes and suggest actions before costs rise. Spyrosoft develops such models using Digital Twins, machine learning and physics-informed approaches to support real-time decision-making.
Yes. A predictive maintenance can model process behaviour under different tariff conditions, allowing plants to test adjustments safely. This helps teams predict the cost impact of process changes and choose the most efficient operating strategy.
