Your plant floor generates the data. Your decisions don't reflect it yet.
Sensors, PLCs, MES systems, and ERP platforms generate enormous operational signal — but most of it disappears in data silos, manual exports, or Excel files that reach leadership a week too late. We connect OT and IT, build the analytics infrastructure, and deploy the AI models that turn operational data into production decisions your teams can actually act on.
downtime from predictive maintenance
Industry 4.0 AI by 2030
integration as their top barrier
our global manufacturer case study
Demand planning and production scheduling ML, quality control analytics, assembly line OEE optimization, warranty analytics, and supply chain visibility — integrated with SAP, Oracle, and Siemens Opcenter environments.
Batch process analytics, yield and waste reduction models, recipe optimization, real-time sensor fusion from OSIsoft PI and InfluxDB historians, regulatory traceability, and energy consumption analytics across continuous production operations.
Asset health monitoring and predictive maintenance for rotating equipment, IoT data integration at scale (MQTT, OPC-UA), grid and pipeline analytics, fleet performance optimization, and operational efficiency dashboards for field and plant operations.
What's reshaping industrial
data and AI right now.
Supply chain volatility, digital-native competition, and the cost of unplanned downtime are accelerating data investment. Here's what's driving the urgency for operations and technology leaders.
Post-pandemic supply chain disruption revealed a structural dependency on static ERP-based demand models that can't process real-time signals. Gartner's 2024 supply chain survey found that 72% of manufacturers experienced at least one significant forecast-driven inventory failure in the prior 12 months. AI-driven demand sensing — incorporating external signals, customer order patterns, and leading indicators — is now a competitive differentiator, not a research initiative.
IDC estimates unplanned equipment downtime costs manufacturers across sectors an average of $260,000 per hour. The payoff from predictive maintenance is well-documented — a McKinsey review of deployed implementations found 15–30% reductions in unplanned downtime, 10–25% reductions in maintenance costs, and 20–40% increases in equipment lifespan. The barrier isn't the model — it's getting clean, continuous sensor data out of historian systems and into an ML pipeline.
Leading manufacturers are deploying GenAI applications that were unimaginable two years ago: operator copilots that answer maintenance questions against equipment documentation, quality AI that diagnoses defect root causes from inspection images and production logs, and procurement assistants that analyze supplier risk in real time. The World Economic Forum's 2025 report on Industry 4.0 identified conversational AI for operations as the fastest-growing manufacturing AI use case by deployment volume.
OT and IT have never
spoken the same language.
Your plant floor runs on Siemens PLCs, OSIsoft PI historians, and Rockwell MES. Your enterprise runs on SAP, Oracle, and Snowflake. Between them: years of manual exports, tag mapping spreadsheets, and data that's hours — or days — stale by the time it reaches an analyst.
We build the integration layer — OPC-UA, MQTT, FHIR-equivalent protocols for operational data — that makes your sensor data available to AI systems in real time, without disrupting plant operations.
Explore Data Engineering & IoT IntegrationEvery challenge you're facing.
Exactly how we solve it.
Here's what we hear from VPs of Operations, Supply Chain, and Technology every week — and the specific capability we bring to each one.
Equipment failures that weren't predicted cost more than maintenance — they cost production slots, SLA penalties, and rush logistics. The sensor data to predict most failures already exists in your historian. The ML pipeline to use it doesn't.
We extract sensor data from your historian, build anomaly detection and remaining useful life models tuned to your specific asset types, and integrate automated alerts into your CMMS — so maintenance becomes scheduled, not reactive.
ERP statistical forecasting works when demand is stable. It fails when there are promotions, market shifts, supply disruptions, or new product launches. The result: overstock on slow movers, stockouts on fast ones, and production schedules that get revised weekly.
We replace or augment ERP forecasting with ML models that incorporate external signals, customer ordering patterns, and leading indicators — continuously retraining as conditions change, and feeding directly into production and inventory workflows.
You know your direct suppliers. But most disruptions come from tier-2 and tier-3. By the time a sub-supplier issue becomes visible in your ERP, the production impact is 6–8 weeks away and the mitigation window has closed. Real-time supply chain intelligence requires data beyond your four walls.
We build supply chain visibility platforms that aggregate supplier data, logistics feeds, geopolitical signals, and financial health indicators into a unified risk model — with dashboards that show lead time exposure, supplier concentration risk, and disruption probability in real time.
Defects identified downstream — during final inspection, at the customer, or in warranty claims — cost 5–10x more to resolve than defects caught in-process. Most manufacturers have inspection data but no ML model connecting in-process variables to final quality outcomes.
We connect in-process sensor readings, inspection data, and material inputs to final quality outcomes using ML — identifying which process variables drive escapes and triggering real-time alerts when parameter combinations historically correlated with defects appear.
Plant performance data is locked in MES exports and shift reports. By the time it reaches a VP of Operations, it's 24–72 hours old. Real-time OEE, yield, and throughput visibility requires connecting MES, historian, and ERP data into a single operations intelligence layer.
We build real-time operations dashboards in Power BI or Tableau — connected live to your MES, historian, and ERP — showing OEE, yield, throughput, and scrap rate by line, shift, and SKU, updated in minutes, not days.
Finding data engineers who understand OT data, or ML engineers who know production analytics, takes months. Your IoT initiative or predictive maintenance project has a launch window — and a six-month recruiting timeline doesn't fit it. You need vetted talent that integrates into your team in weeks.
We source vetted LATAM engineers with manufacturing and industrial domain experience — OT integration, IoT pipelines, ML for production analytics — at 40–60% below US market rates, in US time zones, ready to embed in your team within 2–4 weeks.
From gut-feel planning
to a self-learning forecast engine.
A global manufacturer was running production schedules based on intuition and historical averages — leading to costly overruns and missed demand signals. We built a sales forecasting and demand planning model that learns continuously and feeds directly into their operations workflow.
"We stopped scheduling around what happened last year and started scheduling around what the model tells us is going to happen next quarter."Read the full case study
What makes us different
on an industrial floor engagement.
Generic data consultancies don't speak OT. Industrial automation vendors can't build ML models. System integrators handle ERP but not AI. We sit at the intersection — and it's exactly where manufacturing AI engagements need someone to stand.
We speak PLC, SCADA, and historian — not just cloud APIs. Our engineers have extracted data from Siemens, Rockwell, and Honeywell environments using OPC-UA, MQTT, and historian connectors. You don't need to explain what a tag is.
OT data integration, cloud data engineering, ML model development, and operator-facing deployment — as one continuous engagement. No handoff between an OT integration firm and a data science team. The context stays in the room.
Industrial AI fails when operators don't trust or use it. We build production-floor-ready deployments — simple alert interfaces, explainable recommendations, and training programs delivered through NuScienta — that create actual behavior change on the line.
As an IBM Silver Partner and Microsoft-aligned firm, we deploy industrial AI on IBM watsonx and Azure IoT/Fabric — platforms with the enterprise security, on-premise deployment options, and scale that industrial organizations require.
Not ready for a full engagement?
Start with a defined pilot.
Every manufacturing data initiative starts with understanding where the gaps are. Our fixed-scope starter programs give you real outputs — and a clear path forward — before any long-term commitment.
We audit your OT/IT landscape, map available sensor and production data, identify your highest-ROI AI use cases (predictive maintenance, demand forecasting, quality analytics), and deliver a prioritized roadmap with realistic cost estimates and implementation sequencing.
See what's includedA scoped proof-of-concept targeting one critical asset class — pumps, compressors, motors, or HVAC. We extract historian data, build anomaly detection models, integrate alerts into your CMMS, and validate performance vs. your current maintenance approach.
See what's includedWe build an ML demand forecast model for one product family or business unit — benchmarked against your current ERP forecast. You get a working model, accuracy comparison, and an integration plan for production rollout before committing to a full deployment.
See what's includedWe build a real-time OEE, yield, and throughput dashboard in Power BI — connected live to your MES and historian. Delivered in 6 weeks, ready to extend to additional lines, facilities, or metrics after initial deployment.
See what's includedLet's talk about what your
operational data should be doing.
Whether you're reducing unplanned downtime, replacing gut-feel production planning, building supply chain visibility, or deploying real-time ops intelligence — we've done it. Let's find out where to start.
Manufacturing & industrial AI —
the questions we hear most.
From VPs of Operations at discrete manufacturers to CIOs at industrial conglomerates — here are the questions that come up in every first conversation.