Weak data foundations.
Access, quality and infrastructure gaps undermine the pipeline before a model ever ships.
Industrial AI Solutions
Intelligence for assets and operations
Osol puts AI into production inside industrial operations. Our AI runs today in our design partners’ large fertilizer plants producing 2.5M+ metric tons a year.
Our engineers have worked on live plant floors for years: DCS data, historians, OT networks and shift operations. Every solution runs in a large, live plant before it reaches yours. From plant data to AI agents running in production, our work is delivered by senior engineers who know the plant floor.
Why plant AI stalls before it pays back
Industry data puts the share of AI/ML projects that fail to deliver their intended value at 70–85%. We see three reasons, and each one is worse in a plant:
70–85%the share of AI/ML projects that fail to deliver their intended value
Technology-led, not value-led.
Initiatives chase the technology rather than a measurable business outcome.
Outside-in, not inside-out.
Delivery teams work from outside the plant, missing the operating judgment that makes AI usable on the floor.
In a plant, the real cost isn’t the pilot. It’s the downtime, scrap and energy the pilot never touched.
Four pressures converging on plant leadership
Unplanned downtime
Industry benchmarks put the cost of unplanned downtime at 8–12% of annual revenue for mid-market industrial producers.
Yield, energy and quality variance
Raw material and energy costs keep tightening margins, while process variability erodes yield and quality consistency.
OT security is now audited
NIST SP 800-82 and ISA/IEC 62443 set the bar for OT/ICS security, and insurers now audit plant networks.
A widening skills gap
Plants compete with Big Tech for scarce AI talent, and most vendors don’t understand what actually runs on your plant floor.
Each of these pressures is measurable, and each one can be moved by AI running in production, not in a pilot.
What we solve
You buy a fix to an operational problem. AI, data and security are how we deliver it.
01
Unplanned downtime
Keep critical assets runningPredict failures on rotating and process equipment, and fix root causes before they stop the line.
Typical starting points: predictive maintenance, anomaly detection and root-cause agents.
Live in production: TurboWatch
02
Yield, energy and quality variance
Run every shift closer to your bestFind the best operating window and hold it, for less scrap, less energy per unit and steadier quality.
Typical starting points: golden-batch analytics, set-point guidance and vision inspection.
Live in production: Process Mix Optimizer
03
Safety and OT audit-readiness
Fewer incidents, audit-ready OTSpot risk before it becomes an incident, and secure plant networks to NIST and ISA/IEC 62443 expectations.
Typical starting points: safety analytics, OT security assessment, and AI and agent security.
Underneath every offer sit our core capabilities: AI & Machine Learning, Data Foundation and Cybersecurity, including OT, with cloud, platform and integration engineering in every engagement.
Where AI pays back across the plant
70+ industrial AI opportunities assessed at our design partners’ plant floors, live.
Unplanned downtime
Predictive maintenance and asset health, early anomaly detection on sensor data, root-cause analysis agents, condition-based maintenance intervals, vibration and inspection data analysis, work-order planning and prioritization, and spare-parts and MRO inventory optimization.
Yield, energy and quality variance
Golden-batch and operating-window analysis, set-point optimization and advisory control, soft sensors for lab-quality prediction, computer-vision quality inspection, energy and utilities optimization, digital twin and what-if simulation, and multi-site performance benchmarking.
Safety and OT audit-readiness
Computer-vision safety and PPE monitoring, incident and near-miss risk analytics, AI-assisted permit-to-work and JSA checks, OT asset inventory and anomaly monitoring, OT security gap assessment (IEC 62443), operator and engineer knowledge copilots, and shift handover and logbook summaries.
We start from your highest-value pain point and build from there.
Our AI runs today in two large fertilizer plants
Our AI runs in production at our design partners’ plants, Pak Arab Fertilizers (PFL) and Fatima Fertilizer (FFL): two large, continuous-process plants run by a $1.5B industrial operator with nine decades of history. Our engineers have worked on these plant floors for years, with DCS data, historians, OT networks and shift operations, so we know how a plant really runs.
~90years of industrial heritage behind the plants’ operator
2large fertilizer plants, AI live in production
$1.5Bannual revenue of the plants’ operator
2.5M+metric tons a year produced at these plants
AI solutions delivered in live, large plants
- TurboWatchReal-time anomaly detection and predictive maintenance for turbomachinery.
- Process Mix OptimizerML-based, real-time adjustment of process variables to improve the efficiency and output of Nitrous Phosphate.
- VeriShipmentComputer-vision fraud detection for the supply chain.
- HSE ChatbotAn assistant for field workers on plant operations and maintenance.
- FG BoltA mobile-first AI policy assistant for employees, including document generation.
- Farmer AssistantA multilingual, voice-enabled advisory assistant in native languages.
- AR AgentAn accounts receivable closing and reconciliation assistant.
- Delivery LensAn AI-based tool for project delivery, governance, monitoring and reporting.
How we deliver: forward-deployed engineers, inside your plant
We’re not a team that writes a roadmap and leaves. Our senior engineers sit with your operators, ship every week and own the result.
The team shape
- A forward-deployed engineer: senior, on site and accountable for the outcome.
- A dedicated build squad of 4–6 data, ML and OT engineers from our engineering center, building in your environment every day.
- Our proving ground, Fatima Group plants, where every accelerator runs before it reaches yours.
The operating rhythm
- Week one: an access checklist signed off for plant data, the OT network and safety induction.
- Every week: a working demo on your data, not a status report.
- Every two weeks: steering with plant leadership against the agreed metric.
- Throughout: one owner from pilot to expansion, with no hand-offs.
Senior engineers on site and a deep engineering bench behind them. That’s how we deliver senior work at mid-market price points.
Two ways to start. One path to production.
Every engagement is fixed-scope and paid, tied to a metric we agree with you before work begins.
A. Start small, prove the value
Diagnostic sprint (2 weeks) › Production pilot (8 to 10 weeks) › Dedicated team (deployment pod, monthly) › Operate (ongoing)First proof: value sized in 2 weeks; live in 10 to 12 weeks. Your protection: walk-away or money-back if the pilot misses the agreed metric. After go-live: dedicated team, then ongoing Operate support. Best when the problem is clear, but the size of the prize isn’t yet.
B. Start with a clear scope
Commissioning sprint (6 weeks) › Main engagement (fixed scope) › Operate (ongoing)First proof: a live use case in 6 weeks, after a 1-week kickoff. Your protection: fixed price, an agreed metric and weekly demo. After go-live: ongoing Operate support. Best when the scope and the data are already clear.
Many clients combine both: start with A on one problem, and use B where the scope is already clear.
Inside a production pilot: 6–10 weeks from plant access to a measured result
A production pilot for an AI advisory use case, such as predictive maintenance, process mix optimization, energy and set-point advice, quality prediction or forecasting.
Scope & baseline
- Use case, metric and baseline agreed in writing
- Data access set up from a single file, or historian, MES or ERP extracts
- Data security and access review
You get: a signed-off metric and baseline.
Build & validate
- Data cleaned and checked with your engineers
- Models trained and tested on your historical data
- Working demo reviewed with your team
You get: a model proven on your own data.
Deploy in production
- Recommendations delivered in your daily tools: dashboard, report or alert
- Your experts accept or reject each one
- Their feedback tunes the model
You get: AI advice your team uses every day.
Measure & decide
- Results measured against the agreed metric
- Code, documentation and runbook handed over
- Scale to more assets or sites, or walk away
You get: a measured result and a clear decision.
What we need from you: a sponsor, an expert who knows the process, and data access in week one.
*Illustrative. Total duration (6–10 weeks) depends on the data access and the scope of the use case.
Why Osol, why now
Buyers usually compare us with industrial platform majors and generalist IT or offshore firms. Osol combines what you want from both:
- Understands OT/IT convergence & plant-floor reality.
- Fixed price against an agreed metric, not licenses or headcount.
- Proven inside live industrial operations before customer delivery.
- You keep the code: no platform lock-in.
- Senior engineers forward-deployed in your plant.
Senior executives with decades of experience at leading organizations including Fatima Fertilizers, Microsoft, IBM, Teradata and Palo Alto Networks. Backed by 100+ AI, data and application development engineers.
Ready to
get started?
One plant, one asset or line, 6–10 weeks at a fixed price. A working system in your plant within ten weeks, measured against your metric, or you walk away.
Need the prize sized first? Start with a 2-week diagnostic sprint.