Manufacturing AI solutions that start on the shop floor
We build AI solutions for manufacturing plants where the data lives in PLCs, a Dynamics or SAP ERP, a quality system and a dozen spreadsheets. Cameras become inspectors, telemetry becomes early warning, and inventory signals become purchase orders your planners approve rather than type.
Why manufacturing data is hard to use, and why that is the opportunity
A plant produces more data per hour than most banks, and uses less of it. Machine counters sit in PLC memory, quality results in a LIMS, stock levels in the ERP and the real picture in a supervisor's head. Smart factory AI only pays off when those sources are joined, and that joining is engineering work, not model work. We treat the integration as the project and prove the data path on one line before any model is tuned.
Where we deliver: in India, we design for plants adopting GST e-invoicing and the DPDP Act 2023 for workforce data; in Saudi Arabia, for industrial programs under Vision 2030 with controls aligned to NCA ECC-2:2024 and the SDAIA PDPL; in UAE, for free-zone and mainland manufacturers preparing for the 2026 e-invoicing rollout under UAE PDPL; and in United States, for plants that map their controls to the NIST Cybersecurity Framework. Our offices are listed on the locations page.
Six AI in manufacturing use cases we build and run
Each card is a workflow we have engineered for plants, not a category label.
Visual quality inspection and defect detection
Cameras over the line classify surface, assembly and label defects with YOLO-family detectors trained on your rejects. Results post to the quality system with the frame attached. We start with one defect class and widen only when precision holds across shifts and lighting.
Learn more →Safety monitoring and PPE workflows
Existing IP cameras watch for missing helmets, vests or gloves, crane danger-zone entry, fire and smoke, with evidence clips and a ticket for the supervisor. This is VisionGuard AI, processed on site so footage never leaves the plant.
Learn more →Predictive indicators from equipment telemetry
Vibration, current, temperature and cycle counts stream into a time-series store where models flag drift against each asset's own baseline. The output is a work order with a reason, not a dashboard of red dots. See our predictive analytics and optimization pattern.
Production and downtime dashboards
OEE, downtime reasons and scrap by line, shift and SKU, calculated once in a governed data layer so planning, finance and operations stop arguing about whose number is right. Definitions are agreed with your team before any chart is drawn.
Edge analytics near manufacturing assets
Inference on gateways and industrial PCs for lines where a cloud round trip is too slow or the network is unreliable. Models are versioned centrally and pushed to the edge, with local buffering when the link drops.
Learn more →06Integrated inventory and ERP intelligence
Consumption, open orders and supplier lead times from Dynamics 365 Business Central, SAP or Oracle feed a forecast that proposes reorders. Approval stays with your buyer; the agent prepares the case.
Learn more →The engineering that connects camera, controller and ERP
Manufacturing AI succeeds or fails at the interfaces. Our teams bring computer vision, industrial IoT ingestion, edge deployment, lakehouse data platforms and application development in one group, so the person tuning the defect model also understands the OPC UA tag it needs and the ERP table it posts to. We design to the Dynamics 365, SAP, Oracle, MES and historian estate already on the floor.
A worked example: from component shortage to approved purchase order
A first engagement follows one painful problem all the way through. Here is how a repeated line stoppage caused by a missing component becomes an ERP-connected forecasting and procurement workflow.
- 01
Detect the shortage pattern
Downtime logs and stock movements show the same bracket running out mid-week on two lines. We quantify lost hours and agree this one component as pilot scope.
- 02
Connect the ERP and shop-floor data
Bills of material, open purchase orders and supplier lead times come from Business Central; actual usage comes from line counters. We reconcile the two so the forecast starts from real numbers.
- 03
Build the consumption forecast
A demand model per component and line, tested against the last 12 months, with error reported in units and days of cover your buyer can act on.
- 04
Define reorder and approval rules
Safety stock, minimum order quantities, approved suppliers and who signs off at which value, taken from your procurement policy and written down together.
- 05
Deploy the procurement agent
An agent drafts the requisition, checks lead time against the forecast, attaches the justification and routes it for approval. Nothing is sent without sign-off. See agentic process automation.
- 06
Publish the MIS view
Days of cover, open requisitions, forecast error and avoided stoppages appear on plant and finance dashboards built on the same governed data layer.
- 07
Monitor and recalibrate
Forecast accuracy, agent acceptance rate and shortage downtime are reviewed monthly. When the evidence holds, we add the next component family.
How we measure value on a production line
Success is counted in defects caught before shipment, downtime hours avoided, forecast error in days of cover and how often operators act on an alert rather than silence it. The baseline comes from your records before the first model runs, and we measure under real conditions: night shift, changeovers, a dirty lens. If a defect class stays below the agreed precision after two tuning cycles, we recommend a camera change or drop the class.
Governance for plants that cannot stop
We keep inference on site, write to OT networks only through approved gateways, and log every automated action with its approver.
The frameworks depend on the market: NCA ECC-2:2024 and the SDAIA PDPL for Saudi plants, UAE PDPL and UAE Information Assurance standard where a facility is designated critical, the DPDP Act 2023 and CERT-In guidance in India, and the NIST Cybersecurity Framework in United States. Camera analytics that identify individuals run only with consent notices and an approved retention policy. Every proposal passes your security review before it touches a live line.
Why manufacturers work with Constient
Because the people who train the model also wire the gateway and write the ERP integration, so a plant gets one accountable group rather than three vendors pointing at each other when an alert misfires. We have delivered infrastructure monitoring platforms for multi-site industrial operators in the Gulf, high-availability engineering for Dynamics estates and vision pipelines for safety and quality. Accuracy and savings are measured on your line during the pilot, never quoted in advance. If you need Industry 4.0 consulting before any build, we start there, vendor-neutral.
Frequently asked questions
How does a manufacturing AI engagement with Constient start?
With a half-day discovery on one line or one problem: the downtime cause, the defect type or the component that keeps running out. We map the systems involved, check data access and agree a measurable pilot with a baseline from your own records. Most pilots are scoped to run 8 to 12 weeks on real production data before any decision to expand.
Can you work with our existing cameras, PLCs and ERP?
Usually yes. Vision runs on existing IP cameras provided placement and lighting are adequate, which we check during the site survey. Telemetry comes through OPC UA, Modbus or your historian. For the ERP we have built integrations with Dynamics 365 Business Central, SAP and Oracle; where a contract blocks direct access we use exports or read-only replicas.
Does video or production data leave our plant?
Not by default. Video inference runs on site and only event clips, metrics and model updates cross the network. Telemetry can stay in a plant data center or move to your cloud tenant in your region. We document data flows for your security team and align them with the SDAIA PDPL, UAE PDPL or India's DPDP Act 2023 as applicable.
How is a manufacturing AI project priced?
Discovery and pilot are fixed-scope and fixed-price. Production rollout is either a fixed-price build with a support agreement or a dedicated engineering team billed monthly, depending on how many lines and plants are in scope. VisionGuard AI is licensed per capability, with pricing confirmed in the proposal after the site survey.
Can the procurement agent place orders on its own?
It can, but we recommend it does not at first. The agent prepares the requisition with forecast, lead time and justification, and your buyer approves or edits it in the ERP. Thresholds can be relaxed once acceptance rate and forecast accuracy have been stable for an agreed period, and every automated action stays logged with its approver.
Build a manufacturing technology roadmap around one measurable problem
Bring the line stoppage, the defect or the shortage that costs you most. We will map the systems and propose a bounded pilot.