Engineering the Future
of
Energy, Automation
& Intelligence with
Schneider Electric
A strategic technology partnership driving the future of energy and industrial intelligence. XFactr.AI partners with Schneider Electric across digital engineering, AI, industrial automation, energy technologies and next-generation connected solutions. XFactr.AI is one of the preferred technology and innovation partners behind ground-breaking programmes, delivering through architect-led, ownership-driven models with 150+ engineers.
Industrial Automation
Engineers supporting enterprise technology initiatives
Engineering capability areas across the technology landscape
Engagement models: offshore development, capability teams, staffing
Enterprise delivery across distributed engineering centres
Schneider Electric & XFactr.AI: A Strategic Technology Partnership
A long-term collaboration focused on enabling digital innovation across energy, automation and industrial technology domains.
Working alongside Schneider Electric teams, XFactr.AI provides engineering capability across the technology stack — from embedded and connected systems at the industrial edge, through cloud and data platforms, to applied AI at enterprise scale. The relationship spans multiple technology domains and is sustained by dedicated engineering teams rather than project-by-project staffing.
As a digital transformation partner, our role is continuity: the same engineers who understand an industrial protocol also understand the data model above it and the analytics layer above that. That vertical understanding is what makes OT/IT convergence practical rather than theoretical.
This page describes engineering capability and partnership scope only. Specific programmes, product roadmaps, architectures, internal platforms and delivery metrics are governed by confidentiality agreements and are not published here. Approved, jointly reviewed technology stories will be linked from this page as they are released.
Engineering Excellence at Enterprise Scale
More than 150 engineers supporting multiple technology domains — organised so that capability depth in one area reinforces delivery in the others. Enterprise programmes rarely fail inside a discipline; they fail between disciplines.
Digital Engineering
- Enterprise applications
- Modern software platforms
- Cloud-native solutions
- API ecosystems
- Quality & test automation
AI & Data Intelligence
- AI engineering
- Data platforms & pipelines
- Analytics & visualisation
- Machine learning solutions
- Generative AI & computer vision
Industrial Technology
- Industrial IoT
- Edge computing
- Device connectivity
- Automation ecosystems
- Embedded & firmware
Energy Technology
- Energy intelligence
- Connected systems
- Digital power solutions
- Smart infrastructure
- Sustainability analytics
Three ways to engage. One standard of ownership.
Critical programmes need accountability, not availability. We match the model to the risk in the work — and every model carries the same architect-led delivery standard.
Offshore Development Model
A dedicated, ring-fenced engineering centre owning a product or platform end to end. Used where the work is business-critical, long-running and cannot tolerate rotating staff.
- Full architect-led ownership of scope, design and quality
- Dedicated infrastructure, security posture and access controls
- Continuity of team across releases and technology generations
- Roadmap accountability, not ticket throughput
- Scales into new modules without re-onboarding
Time & Materials Capability Teams
Domain-specific engineering pods that plug directly into existing product organisations, flexing up and down as roadmaps shift.
- Fast mobilisation against a named technology domain
- Embedded in customer ceremonies and release cadence
- Transparent capacity and effort reporting
- Scales across capability areas without new commercials
- Common route into a full offshore development model
Staffing Solutions
Precise skill augmentation where a programme needs one hard-to-source capability rather than a whole team.
- Screened specialists across all twelve capability areas
- Rapid replacement and bench continuity
- Managed onboarding, compliance and access
- Direct reporting into customer engineering leads
- Converts into pods as scope grows
Built to last longer than a project
The value in an enterprise partnership compounds. These are the characteristics that make a multi-year engagement different from a sequence of statements of work.
Ownership-driven delivery, not resource supply
On every critical programme an XFactr.AI architect sits at the centre of the stakeholder map — accountable for design decisions, delivery quality and release readiness, and reachable by every team that depends on the outcome.
Architect-Led
Delivery Pod
SINGLE ACCOUNTABLE OWNER
Immerse
DOMAIN DEEP DIVES
Architect
OWNERSHIP ASSIGNED
Build
SHARED RELEASE CADENCE
Demonstrate
INDUSTRY CONFERENCES
Scale
SAME OWNING TEAM
From Edge to Enterprise Intelligence
Industrial value is created at the machine and realised in the enterprise. Everything in between — connectivity, protocol translation, edge processing, data movement, modelling — is engineering work, and it is the work we specialise in.
Industrial IoT & Connected Systems
Supporting connected industrial ecosystems where thousands of heterogeneous devices must become one coherent data source. Industrial estates are not greenfield — the engineering challenge is making decades of installed equipment legible to modern platforms.
Edge Computing & Gateway Engineering
Compute placed close to operations, where latency, bandwidth cost and network isolation make cloud-only architectures impractical. Secure edge solutions carry the same lifecycle expectations as any industrial asset: provisioning, updates, observability and decommissioning.
AI-Powered Energy Intelligence
Turning continuous operational and energy data into decisions. Industrial AI solutions earn adoption when they are explainable to the engineer on the floor — not when they are merely accurate in a notebook.
Industry 4.0 Engineering
Smart manufacturing solutions that connect operational technology to enterprise systems without forcing either side to abandon its constraints. OT/IT convergence is a negotiation between uptime and agility, and it is engineered, not declared.
Technology Capability Landscape
Six layers, one continuous ascent from connected devices to enterprise intelligence. Select a layer to see what we engineer there.
Enterprise Intelligence
Where operational signal becomes an enterprise decision — dashboards, alerts, planning inputs and the reporting that boards and regulators actually read.
Innovation Areas
Five areas where enterprise energy and industrial organisations are moving from experimentation to production — and where our engineering investment is concentrated.
Smart Energy Systems
Digital solutions supporting modern energy ecosystems — metering intelligence, connected power systems and smart energy management platforms built for distributed infrastructure.
Industrial Automation
Engineering capability across connected industrial environments, from control-adjacent software to the supervisory and analytics layers that sit above it.
Edge Intelligence
Real-time processing closer to industrial operations, where decisions cannot wait for a round trip to the cloud and connectivity cannot be assumed.
AI & Analytics
Transforming industrial data into actionable intelligence through machine learning, computer vision and generative AI applied to genuine operational problems.
Digital Platforms
Building scalable enterprise-grade technology platforms — API ecosystems, microservices and cloud-native foundations designed for a decade of change.
Quality at Industrial Scale
Automated validation and verification for systems where a defect reaches physical equipment, not just a screen.
A ring the size of a wedding band, carrying a hospital-grade sensing brief.
Movano Health set out to build a wearable that women would actually wear every day and that regulators, clinicians and data scientists could take seriously. That combination jewellery on the outside, instrumentation on the inside defined every engineering decision in this biometric wearable development programme.
The platform we delivered has four coupled parts: the ring itself, iOS and Android companion applications, a set of backend services, and the cloud infrastructure beneath them. The ring senses continuously and holds data locally. The mobile application collects it over Bluetooth Low Energy, decodes and visualises it, and synchronises the results upward. The cloud stores the longitudinal record, runs analytics, and returns insight the wearer can act on.
Where most healthcare mobile app development projects begin at the screen, this one began at the sensor. Sampling strategy, packet structure, clock drift and battery budget were settled before a single dashboard was drawn — because in connected health solutions, the interface can only ever be as honest as the signal behind it.
Power measured in microamps
A ring has no room for a large cell. Continuous photoplethysmography, temperature and motion sensing had to fit inside a budget that still delivered multi-day wear between charges.
Data that cannot be lost
Sleep happens away from the phone. The ring buffers overnight and reconciles later, so a night out of range never becomes a gap in the wearer's record.
Signals that must hold up
Heart rate, SpO₂, HRV, respiration and skin temperature had to remain defensible under motion, cold hands and varying finger sizes not just in a lab.
Trusted by Leading Enterprises
Featured Technology Stories
Approved, jointly reviewed stories describing our engineering approach within each domain. More will be published to this experience center as they clear review.
Industrial Edge Intelligence
How we support next-generation industrial connectivity, gateway engineering and secure edge-to-cloud architecture.
View story →AI-Powered Energy Analytics
Our work across intelligent data platforms, predictive insight and analytics applied to energy and operational data.
View story →Connected Industrial Systems
Engineering approaches for IoT-enabled industrial ecosystems, device connectivity and protocol-level integration.
View story →Why Enterprises Choose XFactr.AI
Large industrial organisations do not need more vendors. They need a partner whose engineers stay long enough to understand the domain and are capable enough to work across every layer of it.
Deep Engineering Expertise
150+ engineers across AI, cloud, data, IoT, embedded and digital engineering — depth in each discipline rather than generalists spread thin.
Enterprise Delivery Experience
Experience supporting complex global technology ecosystems, with the governance, security and release discipline that environment demands.
Domain Understanding
Strong capabilities across energy, automation and industrial technology — we speak the language of the plant as well as the platform.
Innovation Mindset
Helping enterprises move from experimentation to scalable production solutions, where most industrial AI programmes stall.
Explore Our Capabilities
Every capability below contributes engineers to enterprise partnerships and is available independently.
AI & Data Engineering
Machine learning, deep learning, computer vision, NLP and generative AI, with the data platforms beneath them.
Enterprise AI Applications
Enterprise AI implementation services taking models from proof of concept into governed production.
Industrial IoT Solutions
Industrial IoT consulting services, device connectivity and IoT platform development at scale.
Edge Computing Solutions
Industrial edge computing platform engineering, gateways and edge-to-cloud architecture.
Offshore Development Model
Ring-fenced, architect-led engineering centres owning critical products end to end.
Cloud Engineering
Cloud-native architecture, industrial cloud solutions and managed platform services.
DevOps & MLOps
CI/CD, DevSecOps, containerisation and the operational discipline that keeps models in production.
Digital Engineering
Digital engineering services across modern web, mobile, API and microservice ecosystems.
Industrial Automation Solutions
Automation ecosystems, embedded systems and smart manufacturing solutions.
Energy Management Solutions
Smart energy management platform engineering and connected power systems solutions.
Industrial AI Solutions
AI solutions for the energy industry, predictive maintenance AI and industrial data analytics.
Quality Engineering
Test automation, validation and verification for systems that control physical equipment.
PLM & Engineering Applications
Product lifecycle and engineering application support for complex manufacturing organisations.
What enterprise teams ask us
What engagement models does XFactr.AI offer?
What does ownership-driven delivery actually mean?
How does your team stay close to a customer's engineering reality?
What does XFactr.AI do for Schneider Electric?
How large is the XFactr.AI engineering team?
What industrial IoT solutions do you provide?
Can you support enterprise AI implementation in industrial environments?
How do enterprise engagements typically start?
Why does this page not name specific programmes or products?
Bring us the layer of your programme nobody wants to own.
The seam between operational technology and enterprise systems is where most industrial transformation programmes lose time. It is where we do our best work. Tell us where yours is stuck and we will tell you plainly what it takes.
Offshore development model · capability pods · staffing solutions. How we work →
A focused capability assessment against one technology domain, with a named architect attached.