Executive Summary
Faclon Labs is a deep-tech IoT and AI company driving digital transformation and Industry 5.0 for manufacturing enterprises across India and globally. Its two flagship products, I/O Sense and I/O DeepSense, help manufacturers optimize utility management, predict equipment failures, reduce production anomalies, and make faster data-driven decisions on the shop floor.
As customer demand shifted toward a SaaS model with dynamic scaling, Faclon Labs partnered with Teleglobal International to migrate its core applications to AWS. Teleglobal executed the full migration to Amazon Elastic Kubernetes Service (Amazon EKS), enabling Faclon Labs to scale in days instead of weeks while significantly cutting cloud costs and improving platform performance.
This engagement is featured as a published case study on aws.amazon.com.
| 30-40% Cloud cost reduction | 20-25% Faster response times | 50%+ Performance gains (select cases) | Days Scaling vs. weeks before | On AWS Published case study |
- Applications now scale automatically without manual infrastructure changes
- New customers and production lines onboarded in days, not weeks
- Sensitive manufacturing data stays within a secure, governed AWS environment
About Faclon Labs
Founded in 2016 and headquartered in Mumbai, Faclon Labs is a deep-tech company specialising in IoT and AI solutions for manufacturing enterprises. The company’s mission is to accelerate digital transformation and Industry 5.0 adoption across India’s industrial sector and beyond.
Key customers include JSW, ABG, Adani, Escorts, Ultratech Cement, ITC, and Vedanta — some of India’s largest industrial conglomerates.
Faclon Labs’ two core products serve distinct but complementary manufacturing needs:
- I/O Sense: utility management platform with reporting, alerting, visualisation, and statistical analytics to help plants track and optimise energy and resource consumption
- I/O DeepSense: AI and ML-powered decision intelligence platform covering production anomaly detection, cost optimisation, inventory demand forecasting, equipment failure prediction, and production strategy recommendations
Both products are used by plant managers, operations teams, and data engineers to make faster, better decisions on the shop floor.
The Challenge
India’s manufacturing sector was shifting from fixed-cost software licensing to a SaaS model. Faclon Labs needed to adapt its infrastructure to support this shift, while also solving three practical problems that were holding back growth.
- Dynamic Scaling for Manufacturing Workloads
Manufacturing plants operate in shifts. I/O Sense and I/O DeepSense experienced sharp spikes in demand at shift changes, when large numbers of workers and systems accessed reports and data views simultaneously.
Faclon Labs’ existing cloud provider could not handle this kind of dynamic scaling. Capacity had to be adjusted manually, which was slow and unreliable. The team needed infrastructure that could anticipate and respond to demand automatically.
- Slow, Manual Customer Onboarding
When a new customer wanted to add a production line or plant to the platform, Faclon Labs had to manually evaluate and provision compute capacity. This process took weeks. In a competitive SaaS market, that delay was a problem for both sales and customer satisfaction.
- Rising Cloud Costs Without Efficiency Controls
Without right-sizing capabilities or intelligent auto scaling, the team was over-provisioning infrastructure to ensure performance, which pushed cloud costs higher than necessary. Faclon Labs needed a way to serve growing demand without costs growing at the same rate.
The Solution
Faclon Labs engaged Teleglobal International, an AWS Partner, to plan and execute the migration of I/O Sense and I/O DeepSense to AWS.
Teleglobal began by developing a detailed migration timeline and running a proof of concept to validate the approach. Once confirmed, the team executed the full migration to Amazon Elastic Kubernetes Service (Amazon EKS).
Migration Execution
Teleglobal used AWS Application Migration Service to migrate the platform’s server workloads, including:
- Node-RED: flow-based IoT data processing
- InfluxDB: time-series data storage for sensor and machine data
- MongoDB: document database for application data
- Redis: in-memory caching for real-time data access
- Other platform services and supporting instances
All workloads were transitioned to Amazon EKS, giving Faclon Labs a containerised, Kubernetes-based infrastructure with automatic scaling built in.
Why Amazon EKS
Amazon EKS was the right choice for Faclon Labs’ shift-pattern workloads for three reasons:
- Automatic scaling: EKS scales application pods up and down in response to real-time demand, including sharp spikes at shift changes, without manual intervention
- Kubernetes-native architecture: containerising I/O Sense and I/O DeepSense made it straightforward to onboard new customers by simply deploying new workloads within the existing cluster
- Cost efficiency: EKS architecture best practices and right-sizing capabilities allowed Faclon Labs to match compute consumption to actual demand rather than provisioning for worst-case peaks
AWS Activate Program
Faclon Labs also joined the AWS Activate program, which provides startups with cloud credits, technical support, and business mentorship. This allowed the team to experiment with different infrastructure configurations and optimise the architecture without significant upfront costs during the migration phase.
AWS Services Used
| Service | How It Was Used |
| Amazon EKS | Container orchestration for I/O Sense and I/O DeepSense workloads – automatic scaling, high availability, and simplified customer onboarding |
| AWS Auto Scaling | Automatic capacity adjustment based on real-time demand, handling shift-pattern spikes without manual infrastructure changes |
| AWS Application Migration Service | Server migration of Node-RED, InfluxDB, MongoDB, Redis, and other platform instances to Amazon EKS |
| AWS Activate Program | Cloud credits, technical support, and business mentorship enabling infrastructure experimentation during migration |
Results
The migration to Amazon EKS delivered measurable improvements across performance, scalability, and cost within the first months of production operation.
| Metric | Result |
| Cloud cost reduction | 30 to 40% reduction anticipated, increasing as optimisation continues |
| Response time improvement | 20 to 25% average improvement, exceeding 50% in some cases |
| Customer onboarding speed | New production lines and plants onboarded in days, previously took weeks |
| Infrastructure scaling | Automatic, on-demand scaling without manual intervention |
| Recurring workload handling | Time-based computation requirements handled automatically by EKS |
| SaaS readiness | Platform now fully supports a scalable SaaS delivery model for enterprise customers |
“We no longer need to manually adjust compute capacity or infrastructure when customers onboard a new production line or plant, because Amazon EKS scales on demand. What used to take a couple of weeks to evaluate and scale can now be completed in a few days.”
— Utkarsh Narain Srivastava
Co-Founder and Chief Technology Officer, Faclon Labs
Teleglobal International’s Role
Teleglobal International, an AWS Partner, led the end-to-end migration engagement for Faclon Labs. Key contributions included:
- Developing a detailed migration timeline and architecture plan for moving I/O Sense and I/O DeepSense to AWS
- Executing a proof of concept to validate the Amazon EKS approach before full migration
- Executing the full server migration using AWS Application Migration Service
- Guiding Faclon Labs on AWS service selection to ensure the architecture matched the company’s scalability and cost requirements
- Ensuring a smooth transition with no disruption to existing manufacturing customers
This engagement is published as a case study on aws.amazon.com, recognising Teleglobal International’s delivery as an AWS Partner.
What’s Next
With its SaaS platform now running reliably at scale on AWS, Faclon Labs is expanding its AI capabilities:
- Vision AI solutions to enhance worker safety and detect surface-level defects in manufacturing environments
- Continued AWS AI and ML service evaluation to extend the intelligence of I/O DeepSense
- Ongoing cloud cost optimisation through further right-sizing and EKS architecture tuning
Responsible AI and governance frameworks to support ethical AI deployment in industrial environments