Standardized weekly and monthly manufacturing reports give teams a consistent view of performance across regions. We built an enterprise data warehouse combining **SAP ERP data and manual sources, using Azure tools and Power BI.**
Digitized Beckers’ energy-savings model into mobile apps that help people choose energy-efficient paints for their specific environment. The recommendations show the potential environmental and cost savings. iOS and Android apps launched in three months.
Modernized Sandvik’s enterprise systems on Azure, adding integrations, analytics and automated infrastructure management. New applications and analytics improved system performance by 30–40%, according to the case study.
Standardized weekly and monthly manufacturing reports give teams a consistent view of performance across regions. We built an enterprise data warehouse combining SAP ERP data and manual sources, using Azure tools and Power BI.
Processes 10 billion+ messages a day with sub-second latency and scales linearly with vehicle count, with scooter-to-cloud sync, OTA updates, a companion app, real-time anomaly detection and a petabyte-scale data lake.
Built a cloud-based, event-driven platform connecting Pandora’s ERP, warehouse and transport systems. It gives global fulfilment teams more timely stock information and supports reliable delivery across markets.
Production data sits in disconnected systems per plant, supply chain visibility stops at the warehouse wall, and ERP backbones built a decade ago are running out of support runway just as the business needs them to do more. Here is where we see the gaps, and how we close them.
Production and product data sit in separate systems per plant, with inconsistent, plant-by-plant reporting.
Inventory, stock and supply chain positions arrive through delayed, manual reporting.
Legacy SAP PI/PO and middleware estates, often undocumented, face hard platform deadlines.
Safety, robotics, WMS and CPQ initiatives run separately, so data does not flow between them.
A prototype is not a product. Models need governance, retraining and a safe path to the field.
Telematics, service logs and dealer insights are collected but rarely feed AI models.
Real-time LiDAR segmentation and the edge platform around it. Every point in the scene gets a name - not “something ahead”, but a person, a vehicle, a cable. Shipping in Sandvik's SceneIQ.
Dust, vibration and occlusion are the normal case.
Safety cannot wait for a round trip to the cloud.
One embedded GPU. Every feature costs watts.
Pre-process, infer on the embedded GPU, post-process - every frame. Only a production compiled build goes live.
An inference engine that reports facts and an edge agent that owns policy, updates and evidence - over one uplink.
The machine flags low-confidence frames, a specialist judges them, and the approved model goes back out.
Model, on-device supervisor, platform and review tooling — joined the machine's own bus and proved on real hardware.
A clear, phased approach: Diagnose, Design, Deliver, Scale
Indicative durations, recalibrated after discovery
Assess gaps, map data assets, tech stack and skills, and prioritise use cases with MoSCoW.
Output: opportunity register, architecture blueprint
Co-create priority use cases, data flows, governance and KPIs, then sequence them.
Output: detailed design docs, pilot charters
Define the stack, build MLOps pipelines, measure and analyse, integrate priority partners.
Output: production-ready AI services
Standardise, operationalise pilots, expand the ecosystem and improve continuously.
Output: an internal AI Center of Excellence model
Pick an operation to see what it runs on.
Outcome: Faster troubleshooting, first-call resolution
These engagements demonstrate Tarento's ability to deliver enterprise-grade retail systems that scale across geographies and channels.
A dashboard that displays machine data is only part of the solution. The broader value comes from using that information to improve operational decisions and actions.
Spanning connectivity, intelligence, human–machine interaction and advanced engineering. A machine doesn't just execute instructions - it shares its condition and performance data with maintenance, production and business systems.
Connects equipment and sensors to capture operating data.
Finds patterns and supports better operational decisions.
Provides shared computing and data- processing capabilities.
Performs physical tasks and enables more flexible production.
Connects shop-floor operations with business processes and IT.
Supports human–machine interaction
Two platforms at the centre: iVolve for ERP and integration migration, DataVolve for master data and multi-site reporting.
SAP-certifiedModernizes SAP PI/PO, SAP CPI, webMethods or BizTalk landscapes - without a manual rebuild.
Automated discovery of legacy integration landscapes
AI-assisted interface mapping that reuses working logic
Any-to-any migration to SAP Integration Suite through one framework
Data engineeringConsolidates fragmented, multi-site manufacturing data into one governed source of truth.
Standardizes master data across multi-site ERP — no more per-plant spreadsheets
Supports harmonization across material lifecycle and cross-site throughput
Extends into analytics and reporting once the data is trustworthy
Estates with hundreds of active interfaces and no clean documentation are not treated as a blocker to a fixed timeline.
iVolve reuses working integration logic and automates discovery and validation, with payback periods as short as 14 months on a five-year return.
500+ engineers across the Nordics and India, blending cost and quality, with 24/7 support and Agile, DevOps and cloud-native delivery.
MLOps, governance and OTA pipelines that turn proofs of concept into production-ready AI services, plus recovery of stalled programmes.
An ERP or integration migration isn't a one-time project. Whether you are scaling AI pilots or moving off legacy middleware, start with a conversation grounded in what has actually been delivered.
Assess your SAP or middleware landscape, however undocumented, and define the right target and path before any rebuild begins.
Walk through iVolve, DataVolve and the manufacturers behind them, and what applies to your landscape.
No generic transformation deck. A conversation about what has been migrated and measured.
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