AI for industrial enterprises
For industrial enterprises, AI delivers a measurable effect in four areas: predictive equipment maintenance (20–40% fewer unplanned outages), computer vision for industrial safety, process regime advisors (+3–8% process-stage efficiency) and LLM assistants for technical documentation inside a closed perimeter. 2026 has been declared the Year of Digitalization and Artificial Intelligence in Kazakhstan — enterprises need contractors who understand production.
Industry challenges and our solutions
Pain
Unplanned downtime of process equipment eats the repair budget and derails the production plan
Solution
Predictive diagnostics from process control data and maintenance history: an equipment risk list with a 7–30-day forecast
−20–40% unplanned downtime, transition to condition-based maintenance
Pain
Safety violations are recorded after the fact; inspectors cannot cover the territory
Solution
Video analytics on existing cameras: PPE, danger zones, hot work — with alarms to the control room
Round-the-clock monitoring, lower LTIF and fines
Pain
Product quality and energy/raw-material consumption depend on the shift and the operator's experience
Solution
Regime advisor based on the historical best shifts: quality soft sensors plus setpoint recommendations
+0.5–1.5% product yield, −3–7% energy and raw materials
Pain
Knowledge is locked in procedures and in veterans' heads; new employees take a long time to become productive
Solution
A local LLM assistant for the plant's technical documentation and procedures — with no data sent to the cloud
Fast access to knowledge, fewer staff errors
Typical solution architecture
- Sources: process control system/SCADA, CMMS (SAP PM, 1C), LIMS, video cameras, document management system
- On-premise data bus and data mart — read-only, no intervention in the control loop
- ML platform: predictive models, advisors, CV detectors, local LLMs
- Interfaces: production engineer's and mechanic's workstations, alarms to the control room, reports for management
Why an industrial enterprise needs AI if it already has a process control system
The process control system executes the given regimes — but it does not answer the questions “which regime is optimal for the current feedstock”, “which machine will fail in the next two weeks” and “is everyone on site wearing a hard hat”. The data for those answers is already being collected: historians accumulate years of tags, the CMMS holds maintenance histories, cameras deliver video streams. An AI layer turns that data into decisions — without a capital upgrade and without intervention in the control loop.
The 2026 context strengthens the motivation: Samruk-Kazyna's portfolio companies face a KPI of +5% EBITDA from AI. Private holdings set the benchmarks: the effect of AI solutions across the ERG group in 2025 was 55.7 billion tenge (KZT). A smart infrastructure-diagnostics pilot at Kazakhstan Temir Zholy (KTZ) (500+ TB of data) cut the defect-elimination time from 15 days to 1 day.
What “second-tier” equipment is and why it matters
Expensive monitoring systems are economically justified only for critical machines — usually 5–10% of the fleet. The other 90% — balance-of-plant pumps, blowers, cooling towers, conveyors — fail no less often, cause local downtime and eat the repair budget, but monitoring them with solutions at Western vendors' price level is impossible.
Our approach is a “light” mass-market product: model-free anomaly detectors that tune automatically to each machine, with no manual modeling of every unit. It is precisely the auto-tuning that makes the economics of mass coverage feasible. The output is a risk list for the whole fleet in the CMMS and in the inspection-round mobile app.
Example tasks by type of production
- Metallurgy and ferroalloys: electric-furnace advisor, heat quality forecast from the charge, CV monitoring of casting
- Chemicals and petrochemicals: quality soft sensors instead of waiting for the laboratory, reactor regime optimization, heat-exchanger fouling forecast
- Cement and glass plants: kiln firing optimization, clinker quality forecast, predictive maintenance of mills and kilns
- Machine building: CV quality control of parts and assembly, predictive maintenance of machine tools from current signatures
- Food industry and agribusiness: demand forecasting, CV product sorting, camera-based monitoring of sanitary procedures
- Transport and logistics: predictive maintenance of rolling stock, optimization of repair “windows”, assistants for regulatory documents
How an AI project gets approved inside an enterprise
- Chief engineer / chief mechanic — the owner of the pain: receives the effect calculated in their own KPIs and the results of a retrospective check on the plant's data
- Information-security department: receives an on-premise architecture — read-only connections, an isolated segment, logging, no outbound traffic
- Metrology and process control: no intervention in the control loop, recommendations go to the operator, responsibility stays with the staff
- Finance: a conservative ROI calculation with a fixed baseline and a measurement methodology agreed before the start
- Procurement: a complete package — founding documents, Astana Hub resident status, a staged contract structure with acceptance certificates by results
Implementation roadmap
- Data and process audit (2–3 weeks): site visit, inventory of data sources, calculation of the potential effect for each area, pilot selection
- Pilot (8–12 weeks): one shop/process stage/documentation domain — a working model on real data, A/B assessment of the effect, a report for management
- Roll-out (4–12 months): scaling to the fleet/plant, integrations, staff training, SLA support and model-drift monitoring
FAQ
Frequently asked questions
What chief engineers, security and procurement teams usually ask — with straight answers.
Next step
Request a plant data audit
Tell us about your task — we'll come back with a data audit plan and an effect estimate within two business days.
- 1A 30-minute call: the task, the data, who signs off on security
- 2Data audit or a retrospective analysis on an anonymised export
- 3An 8–12 week pilot with success criteria agreed before the start
NDA from the first contact
Reply within one business day