| 0

ALGO8 AI’S

iVISION WHITEPAPER

FEBUARY | 2 0 2 1

| 1

iVISION: Visual Intelligence

Platform – Powered by Deep

Learning

Intelligent Visual Analytics solution for

process-driven industries

| 2

I. EXECUTIVE SUMMARY

The advent of Industry 4.0 has been a major disruptor in the current industrial ecosystem, and

AI/ML (Artificial Intelligence/ Machine Learning) solutions are a major contributor in this transition

process. AI/ML based data driven technologies are key to cyber-physical systems that the process

industry desperately needs in the modern-day scenario. In support, advancement in physical

sensor technology, the technologies behind Industrial Internet of Things ( IIoT) networks have

become very sophisticated. As a result, AI-systems have become capable of accessing a highly-

detailed data-level view of input and output of processes. AI is becoming more and more

capable of intelligent data processing not just for the human understanding, but also for enabling

the machine to learn and analyze the patterns within the process.

This whitepaper explores the capabilities of visual intelligence and how its product suite delivers

value in the industry. Our technology brings a multi-facetted solution, which not only drives bottom

line revenues and operational excellence, but also adds value by streamlining managerial and

planning processes for large and distributed industrial setups.

In this whitepaper we identify the current problems faced by the process-driven industry, and how

visual intelligence can help firms navigate the changing landscape of large-scale operations.

| 3

II. INTRODUCTION

With the advent of Industry 4.0 and the ideation of creating ‘smart factories’, a lot of

manufacturing industries feel the need of including artificial intelligence and machine learning

technologies to discover new opportunities. Computer vision plays a huge role, being a part of the

complex approach to smart factories and business setups.

Smart visual technologies can help

Its is forecasted that the global machine vision market to grow at a CGAR of 6.5% between

2019 and 2025. The market is projected to reach USD 14.7 billion by 2025 from a value of USD

9.9 billion in 2019.

With the growing technological advancement, companies are paying attention to what AI

technologies are offering. Computer vision being one of the most adopted technologies with `20%

of the companies using worldwide.

Some of the benefits of Computer Vision in the market includes:

• Time-efficiency. Not only does a fully automated system operate much faster, but it can

also work 24/7 if needed.

• Accuracy. Adoption of CV-based solutions allows manufacturing companies to achieve a

significantly higher level of accuracy within the accepted tolerance. Combining specific

equipment and advanced CV algorithms allows achieving near-perfect precision levels in

production and quality control.

9.914.7

0

10

20

30

2019 2025

Mar

ket

in U

SD (

Bill

ion

)

Year

Global Machine Vision Growth Market

| 4

• Repeatability. When it comes to repetitive work, CV-driven solutions are more effective

when it comes to monotonous tasks. A fully automated system speeds up production time

and reduces the cost of production on many levels (e.g., there is no need to train or retrain

personnel).

• Reduced costs. Apart from reduced labor costs (as fewer staff members are needed to

control the process), there is less room for mistakes or deviation from the standards, thus

the overall quality of the product is better, and there is less waste.

• Post-pandemic value. It seems like social distancing is here to stay, so reducing the

number of staff members on the production lines will help employers maintain sanitation

norms and help employees stay healthy.

With this comes the onus to choosing the right technological partner that would provide the right

solutions to the industrial problems faced by you company. Algo8 AI’s flagship product, iVision,

helps industries achieve the required digital transformation with computer vision as a service and

solution provider.

It is an intelligent visual analytics platform that leverages the combined benefits of AI, Industrial-

IoT, and the vast stores of historical process data in order to deliver visually based monitoring

and maintenance capabilities for industrial manufacturing. It also generates optimization insights

to improve industrial workflows based on artificial intelligence and machine learning algorithms.

The intelligent visual analytics platform itself is organized in a flexible, user friendly, and secure

interface, which enables convenient data handling and sharing platforms, and collaborative

workflows. This is to make these technologies and their benefits accessible to all end-users within a

firm in an accessible and low-code environment.

iVision is an AI/ML based product that leverages equipment as well as environmental OT data

from historical data sources and live streams too. The intelligent system will learn from the historic

data and continue to learn and improve upon itself throughout its lifecycle. It uses deep machine

learning based predictive analytics to give unique insights and prognosis of process and business

operations that help streamline workflows, and minimize the number of moving parts in any given

system.

| 5

The data-driven AI-platform is aimed at decreasing capital expenditure (CAPEX), operational

expenditure (OPEX) and achieve operational excellence for businesses across sectors.

iVision can do everything that the human eye does, just way better and way faster.

It's a one-stop computer vision solution to minimize the manual effort of monitoring and inspection.

It provides a range of capabilities that can heighten efficiency and drive your operations to

match your business goals.

A. THE PROBLEM

Utilizing digital technologies can bring a lot of benefits and competitive advantages to industries

everywhere. New digital-first business models are becoming things of priority with the rapid

adoption of advanced technologies. Still, there exist certain barriers preventing industries from

adopting digital technologies, such as lack of clear understanding of technologies and partners to

help implement these technologies. There has been some adoption of simpler point-solutions

related to IIoT, drones, Chatbots, etc. which are applicable for very specific problems within the

whole process. Regardless of success, they can’t address the complexities related to the process

as a whole.

Besides the pacing of how new technologies are being adopted and deployed for large-scale

applications, there are lots of holes and inefficiencies in the way that current industries function in

terms of asset management, inventory management, operational awareness, etc. and emerging

AI-technologies offer new and innovative ways of addressing these problems.

Limited Visibility on Operational Conditions Causing Unwanted Downtimes and

Inefficiencies:

Traditional maintenance methodologies are reactive in nature i.e., they take place as a response

to certain issues or machine-failures or as part of a times-based spare parts replacement

| 6

schedule, based on running hours irrespective of part conditions recommended by OEM (Original

Equipment Manufacturers). Of course, some organisations have adopted condition monitoring of

some critical assets but still due to lack of expertise in the field of AI, businesses are still unable to

exploit the benefits in predicting the problem in advance or right time before failure. Mostly, we

wait for things to break before we fix them. Sure, maintenance procedures help, but they too are

minimally guided. They follow pre-planned maintenance cycles which are more based on

logistical factors such as availability of assets or personnel, rather than depend on monitoring of

asset-health and its components. Inventory levels are not adjusted and replenished according to

real-time operational demands and can result in understocking of components, again leading to

unplanned downtimes, or overstocking, which is also an excess expense on the organization.

Limited View of Operational Data and Interoperability:

Current enterprise-level data management tools lack advanced data integration and

consolidation capabilities; even though process-driven industries have very vast stores of historic

data around asset maintenance and operations, without intelligent tools, the datasets can’t be

efficiently analysed to draw insights about where operations are lagging and how to address the

speedbumps. Due to a lack of a comprehensive data-management solution, different teams within

the same organization often use different data hosting tools, leading to difficulties in

interdepartmental coordination and collaboration for projects and manufacturing facilities.

Lack of Intelligent Decision Facilitation Tools:

Companies have not been able to unlock the true potential of Artificial Intelligence (AI)

related applications and System of Intelligence (SoI) related solutions. A major

shortcoming that industries face today is the lack of intelligent decision-support tools to

help guide operations; this is primarily due to limited visibility of real-time conditions of

critical & super-critical assets, and operations in general, and we at Algo8 AI believe that

there is a lot more to be gained from adopting AI-technologies at larger scales, with the

goal to provide solutions for enterprise-level deployment. Manufacturing plants have

great amount of data, however, traditional heuristics and rule driven models do not

leverage the data to create the intelligence that can potentially disrupt industrial

operations.

| 7

Adoption of New Technologies Tends to be a Demanding Process:

Large-scale deployments of new technology within existing industrial setups can be a highly

challenging task, spanning long period of time and multiple cumbersome steps. Often, a complete

overhaul of on-board systems is required for the adoption of new systems into existing setups;

which means that organizations are forced to completely get rid of their existing mechanical or

computer infrastructure, and replace it with brand new gear. Now, both the uninstallation and

installation processes can be very long and consuming, which contribute to extended periods of

non-operations leading to capital and operational losses.

B. THE SOLUTION

With AI-enabled monitoring of industrial assets, we can now have completely automated systems

to reliably track operational conditions round the clock. Such systems increase the safety of assets

because they can identify things like leaks, vibration, abnormal noise, anomaly and strain on the

machine in advance and give enough time to take corrective action in planned manner. The system

could trigger an alarm, issue notifications to the relevant personnel, or even trigger measures like

a trip switch to protect the asset from further damage, if no corrective action is taken in time. This

not only ensures that asset-health is maintained intelligently, but also improves the safety for

employees by minimizing accidents as well downtime due to unknown reasons.

Since AI is becoming more capable of performing human-level tasks such as observation and

recommendation, it enables large-scale automation of process-driven tasks and intelligent

workflows; it is pushing us towards the next iteration of Industry 4.0 with the advent of smart

industries. Large-scale adoption of AI-tech in the renewable energy sector especially will allow to

target a larger market and enable operations to expand more cost-effectively.

The AI techniques applicable to achieve bottom line results are:

• Machine Learning

• Deep Learning

| 8

• Predictive Analytics

• Data Consolidation & Warehousing

• Data Visualization & Dashboarding

AI platforms use data and algorithms with a Process Engineering context to design, develop and

deploy AI products that drive and significantly enhance YTE (Yield, Throughput, Energy) in

operation excellence of manufacturing industries.

Some of the critical functions that stand to draw major benefits from AI-tech adoption are:

Supply Chain:

Starting from procurement requirements, vendor matching to delivery optimization, supply chain is

crucial for any organization that is in the field of manufacturing & production. This segment also

includes warehousing & storing operations which is one of the key sectors in the field of supply

chain.

Process:

This segment refers to the use cases applicable to the actual process of production that the

industry belongs to. It is highly applicable for initiatives aimed at improving operational

performance and efficiency.

Assets Reliability and Performance Management:

This segment refers to the functionalities that are directly concerned with the operational health of

equipment, predictive maintenance strategy and condition monitoring, Proactive Maintenace

based on Machine Diagnostics, automated spares-tracking and inventory management.

Compliance and Safety:

| 9

For industries such as manufacturing & process, safety & environment compliance is one of the

major areas of concern and even a minor inconvenience/mishap can prove fatal. This is also

applicable for production related operations where there are compliance standards and strict

consequence for any deviation from these standards.

Energy Efficiency:

Process industry segment is highly energy dependent for its day-to-day operations and

optimizing the energy consumption and accurately determining the energy requirements will

prove to be highly beneficial for the industry and in a larger picture for the environment as well.

1. CHALLENGES

Solutions are available in different scales solving operational challenges of different scales using

different techniques. But the lack of an end-to-end solution for large-scale deployments remains.

Distributed Industrial Assets:

Several industries depend on large networks of distributed assets such as railway lines, and

transmission towers for the telecom and energy companies. Due to the lack of intelligent and

integrated monitoring solutions for critical assets such as these, traditional methods depend on

manual inspections. For spread-out assets, manual maintenance procedures pose several

additional logistical challenges such as transporting large maintenance teams and equipment, and

with limited visibility the planning process also begins only once personnel are physically able to

inspect the assets. At this point they may realize the need for additional personnel or resources,

making another maintenance excursion inevitable. Similar scenario could be anticipated in Oil &

Gas fields, Wind Energy and Solar Energy Farms, Oil and Gas Supply lines, remote, non-

accessible and unsafe places.

| 10

Data Silos:

Although there is a vast amount of data present in the applicable industry segments that can be

used to develop solutions using analytical techniques but there are certain barriers that exist which

is preventing in successful application of the necessary data. One such major challenge are data

silos which refers to data being stored in isolated systems and in a non – integrated manner.

There is a shortage of integrated systems to utilize data and records available to improve

visibility and better identify scope of improvement.

To develop robust & integrated solutions, data silos must be omitted, and integrated systems are

integral for successful deployment of AI solutions in these industry segments.

Increased Digitalization of Operations:

With the increase in digitalization of operations, there is more digital presence and footprint, thus,

providing more ground for cyber-attacks and threats.

Entry Barrier:

Industries, especially large process-oriented ones, are firmly set in the traditional methods and

procedures that they have followed for years. Bringing about drastic changes in the workflow

and adoption of new technologies faces a lot of resistance for this reason; people are not

convinced that the amount of work needed to readjust their workflows will be worth the effort, or

they simply don’t want to stray from the familiar.

2. OUR SOLUTION / HOW IT WORKS

Developed by seasoned data scientists in the country, backed with years of experience and

having a process engineering background. Thus, product is backed with capabilities both from a

technical & domain point of view.

| 11

The primary modules within iVision are as follows:

I. OPERATIONAL COMPLIANCE

Efficiently monitor industrial operations, and enable improved visibility and control for floor-

personnel

• Pipeline monitoring • Shopfloor inspection • Ash-dyke monitoring • Predictive maintenance

II. PRODUCT QUALITY CONTROL

Custom defect detection and quality control module to ensure minimum wastage, recalls, and

liabilities

• Metal defects

• Fabric defects

• Bottling defects III. SAFETY

iVision detects non-compliance of safety protocols, performs social distancing checks, PPE

detection, smoke and fire detection etc. and issues timely notifications

• Fire and smoke detection

• Social distancing checks

• PPE detection

• Behaviour monitoring

IV. SECURITY

Maintain air-tight security for your industrial-setups. Safeguard your employees and critical assets

from theft, pilferage, and unauthorized access

• Theft detection -

• Restricted-entry zones -

• Shopfloor control -

• Automated Number Plate Reading -

| 12

V. Inspection

Automated inspection of machines and industrial zones to minimize maintenance costs and to avoid

industrial lapses and accidents

• Corrosion detection

• Equipment health

• Weaving defects

• Process inconsistencies

The architecture of the product can be interpreted in the following layers:

I. Data Connection Layer & Services:

• Operational Technology (OT) data with sources such as IoT, PLC, DCS, SCADA, Control

Systems, Historians, Manual Inputs, Vibration Analysis Software, Risk based Inspection

software, LIMS (Laboratory information management system) etc.

• Information Technology (IT) Data Sources including data sharing platforms, ERPs, SAP PM

and MM modules, CMMS etc.

• External Data sources such as benchmarking documents (Steam tables, Performance curves,

Equipment data sheets, drawings process specifications etc.), OEM data and Manuals,

Standard and Recommended Practices etc.

II. Data Management Services:

Data Ingestion: This service includes, movement of data from multiple sources to a

particular destination in our system. This ensures that our system uses only specific format of

data which will be ideal for analysis while the source data exists in multiple formats coming

from various sources such as RDBMS, other types of databases, S3 buckets (?,pl elaborate),

CSVs, (?,pl elaborate) or from some specific streams.

ETL (?,pl elaborate) Pipeline: This particular is concerned with extracting, transforming and,

loading data from an input source to an output location such as a database, data

warehouse etc. for reporting, analysis, and data synchronization.

| 13

Data Aggregation: In this process, data is gathered and presented in a summarized format.

Our solution can collect data from multiple data sources with the goal to comb these

datasets into a summary for data analysis.

Data Modelling: The process of creating a data model for information to be stored in a

database. Basically, it is a representation of Data objects at a concept level the

associations between different data objects, and the rules.

Storage: Our solutions enable users to store external and internal data on a distributed

platform, which maintains flexibility and scalability for big data initiatives in process-driven

sectors.

III. Analytical Engine:

Traditional Analytics: Predecessor to advanced analytics techniques such as ML based

learning, predictive analytics, the solution has the capability to perform traditional

analytics on top of data collated and stored. This filters out the relevant data &

information from the superset of data that was gathered via connection with multiple data

sources.

ML Layer: Post completion of the traditional analytics, advanced analytical practices are

established to derive hidden correlational insights that are relevant for improving visibility

around availability of spares, inventory status, procurement decisions etc.

Streaming Analytics: it captures real-time operational information by means of a

minimal, yet extensive IoT network integrated with the existing industrial infrastructure.

Algo8 AI’s solutions are meant to integrate with and build on top of existing client

infrastructure.

IV. Security & Access Management Services:

| 14

Alerts & Notification: The solution will be capable of issuing timely notifications to the

relevant personnel based on configurable role-based notifications. If any shortcomings or

issues are detected in the inventory or operations management, then immediate alerts are

sent to the critical employees in an automated manner.

Admin Control & Configuration: It also has advanced data security features, such as

secure access, master admin control across the entire distributed network, and permission-

based viewing, editing, and sharing of critical files.

Sharing Services: With a Federated multi-database management system (FM-DBMS) in

place, the computational load can be effectively distributed across the whole system

preventing overloading of localized assets and servers.

V. User Experience:

Actuators, Controllers: The operational components that run the operations on the

shopfloor are connected to our solution. As a result, the solution has fully automated

control to monitor and fine-tune operations. (people will not allow it I think, may have

discussion?)

Mobile/Web Application: The end-users will be equipped with a user-friendly

application, with flexible access via mobile as well as desktop devices.

Inferential Input: The solution accepts user-inputs and incorporates them in a feedback

loop for training the model. One of the key features of this platform is its capability of

continuous learning with time & increasing flow of data. This aspect helps in achieving

operational excellence through improved efficiency.

| 15

3. OPERATIONAL ADVANTAGES

1. Operational Advantages & Gains: Based on facial recognition, thermal vision, computer vision, and powere by our proprietary AI/ML algorithms, iVision is capable of contactless visitor tracking and management, social distancing detection, PPE & mask detection, and operations monitoring to help everyone embrace the new normal in the workplace, in a safe manner. The product also enables fully automated, round the clock, and air-tight security of a premises and prevents theft and pilferage of resources. 2. Business Advantages: Scalable AI product applicable to multiple industry sectors across geographies. 3. Capital Benefits: Capital cost reductions are a result of improved efficiencies and more streamlined workflows due to the optimized industrial ecosystem that iVision establishes. 4. Deployment and Scaling: iVision is meant to integrate with and build on top of existing client infrastructure. This makes our product that much easier to deploy, since only minor additions are needed and offers cost-effective and simple scaling operations. 5. Industrial Research: We have a vast network of partner research institutes and innovation labs that are constantly engaged in R&D activities to develop AI/ML solutions for large-scale applications in the age of Industry 4.0. Strategic Benefits: Client and partner organizations see AI/ML capabilitiy development to drive

their operations better. Additionally, the employees of the organization build first hand

experience in working with AI platforms, which is going to be essential as industrial technologies

continue to grow and evolve.

Differentitation: Very few players are building smart visual-analytics solutions on top of OT data and the existing solutions (approximately 6-7 worldwide). These are big players and average cost of the projects is higher as compared to ROI delivered. 1. Most of the competitors offer point-solutions, or AI products geared towards a particular set of applications. The edge that our offerings provide over the competition is that they enable end-to-end solutions, which are scalable to the enterprise-level in an efficient and cost-effective manner. 2. Flexibility in terms of data connection is another great feature of iVision as it can connect and leverage both historical & real time data. On top of this, it takes into consideration real time OT data using IIoT techniques additional to the IT data present in the MIS/ERP systems. 3. We build our products in collaboration with industry-professionals and strategic groups, which means our inputs come directly from the end user. This ensures that we address the problems that are genuine pain-points for the end-users, and this helps us drive our customers' operational efficiency up.

| 16

4. We have indigenuous capability to support development & deployment, thus, provide cost effective solutions. 5. iVision has continuous learning capabilities; that means that the more it is utilized, there is more historical data for it to access, and this helps in increasing the efficiency and accuracy of the AI/ML models further. 6. Additionally, our solution is highly cost-effective, and can be scaled-up for larger-scale applications in

an efficient and streamlined manner.

Existing asset management solutions are based on IT data, which is easier to access but is not

nearly as comprehensive and directly applicable as operational technology (OT) data. Our

solutions are built and trained around OT data which enables comprehensive understanding and

control of industrial operations. This is made possible by iVision; it processes real-time OT by

means of an IoT network integrated with the existing industrial infrastructure, and historical

operations-data as well to update and continuously tune its predictive analytics model.

iVision is Algo8's flagship Predictive Maintenance module with potential failure predictions and

early warning systems. It is easy to deploy and use, practically plug n play, low code, and is

compatible with a multitude of machines/operations. For the energy & resources sector, existing

deployed use case and capabilities are developed on top of actual operational data and not just

IT data, thus giving a complete view and control over shopfloor operations and "ground"

problem.

Predictive Analytics:

The solution will leverage the established data connections and analyze historical patterns &

trends to come up with predictive insights & relevant recommendations which will be useful for

spares procurement, availability status, maintenance activities etc. With a holistic view of

operations and access to optimization recommendations, industries become capable of an

advanced asset management processes such as spare life-cycle management and just-in-time

ordering. Moreover, the product can prioritize components based on availability and expirations,

and closely track the relevant metrics close to the expiry of assets for timely replacement.

Supports Entire Lifecycle of Data:

| 17

With the advent of big data & Industrial Internet of Things (IIoT), data lifecycle management has

become very relevant in this context. Having an integrated system that will offer visibility for the

entire life cycle of the data is essential to ensure efficient use & optimized results, minimizing

errors.

• Data Creation: iVision platform can be connected to various data sources such as IoT

sensors, OEM databases for identifying data points and collecting data related to the

same. This data can be further processed using methods such as integration, scrubbing &

extract-transform-load (ETL) to ensure that it is suitable for analyzing. Finally, archiving or

deleting data at the end of its useful life ensures that it does not consume more resources

than necessary.

• Data Analysis: In order to convert the gathered data into meaningful insights iVision

platform offers support for analysing the processed data. It is backed by advanced

machine learning techniques such as predictive analytics, deep learning for analysing the

data and answer the questions pertaining to performance improvement and gap analysis.

• Data Information Management: iVision supports in Data Governance i.e., data

availability, usability, integrity and security. Having an integrated platform enables the

key stakeholders to access any relevant data or information as and when required.

In addition to this, it has features which ensure that data accessibility is limited to

authorized personnel only and it minimizes the scope of misuse of the data. Thus, ensuring

security of the data maintains the integrity of use of the data.

Having a one stop connected system in place for the entire lifecycle of data helps in

developing a comprehensive platform for the oil and gas industry and this offers a

competitive advantage over our contemporaries.

Modular & Customizable to Fit Industrial Requirements:

The industries we are targeting with this product have some similarity in its operations, for ex.,

there will be a process in its core operation what will be one of the major sources of operational

data. But this comes with a disadvantage that every process is different in its individual operation

and functioning and though, there are similarities, but every challenge cannot be the same.

| 18

Having a solution that is flexible with integrating complex processes proves to have a competitive

advantage being able to cater to a wide range of industry and processed alike.

Our solution has the flexibility to be deployed in both the infrastructures, cloud-based as well as

on-premise, enabling our clients to deploy the solution based on their specific requirements and

capacity.

Streamlined Deployment and Scaling

The iVision solution is meant to integrate with and build on top of existing client infrastructure. This

makes our solution that much easier to deploy, since only minor additions are needed and offers

cost-effective and simple scaling operations.

Minimal Hardware Intervention:

The sensors we’ll be using are mostly non-intrusive in nature. The machines/assets being monitored

are not disturbed during installation, operation or maintenance of the sensors which primarily

monitor operational environmental data. As a result, operations remain virtually untouched and

need be halted at any point of functioning on account of the additional IoT web around it. It is

essential that regular military and defense operations not be hindered by any of our system

components, and we design our solutions accordingly; to deliver maximum results with minimum

intervention.

Collaborative Workflows:

One of the key innovative features of the iVision platform is its end-to-end solution delivery

capability and due to this capability, it loops in all the concerned departments and relevant

stakeholders. Our platform is flexible with integration with various data sources and solution can

designed & deployed for individual departments as well as for the organization.

This flexibility is key for industries we are targeting under iVision platform as there are multiple

operations running parallel and generating huge amount of data that needs to be utilized in an

integrated manner.

| 19

Technology Stack Compatibility:

An open-source platform can be defined as a platform which is flexible for collaboration by

authorized partners as and when deemed necessary. This offers an advantage as it provides an

opportunity for creative collaboration and developing potential partner.

An open-source platform is highly compatible across various databases, software, and this

flexibility offers an advantage over using a closed source platform.

Operational Advantages & Gains:

Predictions around potential failure events which have otherwise direct & indirect impact on

bottom line business results through unwanted downtimes and production losses. Operational

benefits of predictive analytics-driven model include heightened asset performance, optimized

process parameters, longer up-times, and overall better visibility and control of operations.

Capital Benefits:

Capital cost reductions are a result of improved asset-life due to the sustainable industrial

ecosystem that iVision establishes. Essentially, the assets are maintained and managed better, so

they last longer, and the replacement requirements are reduced, thus saving major industrial costs.

Blend of Process Engineering and Machine Learning Capabilities:

One of the key features of our product as well as the team is the blend of domain and technical

capabilities. Domain expertise is due to a strong academic background in the field of chemical &

industrial engineering. This expertise when combined with years of rich analytical experience

gives us an edge over competitors in this field.

Strategic benefits

| 20

Client and partner organizations see AI/ML capability development to drive their operations

better. Additionally, the employees of the organization build first-hand experience in working

with AI platforms, which is going to be essential as industrial technologies continue to grow and

evolve.

Process Engineering + Machine Learning:

iVision platform is built to address the operational issues such as performance gaps, anomaly

detection and operational inefficiencies. This requires adequate knowledge of the processes

involved and as described in iVision team profile and capability, it is developed by a team

possessing both process as well as technology expertise.

This expertise is replicated in the iVision platform and enables it to understand and imitate any

process. Having this unique blend of process and technical capability is one of the key innovative

features of iVision. In addition to this, oil and gas industry is a highly process driven industry

offering vast amount of data and using this unique capability of iVision will be highly beneficial in

detecting anomalies and performance gaps in this sector.

AI-Research and Development Ecosystem:

The vision behind creating iVision platform is not limited to industry applications but also include

creating an entire ecosystem of AI/ML based data science practices. We plan to achieve this in

collaboration with clients and SMEs by providing training & support on such practices.

This further improves our value proposition through mutual knowledge transfer i.e. our capabilities

are strengthened from a process & operations point of view and we enable engineers &

managers to initiate data driven thinking & solution design and application.

We have a vast network of partner research institutes and innovation labs that are constantly

engaged in R&D activities to develop AI/ML solutions for large-scale applications in the age of

Industry 4.0.

| 21

4. USE CASE 1 – Volumetric Analysis of Ash Dyke and Automating drone analysis

Challenge

An Ash Dyke / Ash Pond is used for the management of coal ash; the coal ash is made into a

slurry and filled in a pond like structure. It can be problematic if the coal ash slurry happens to

mix with nearby water sources such as groundwater or a river. The water source gets

contaminated and becomes unusable.

Causes for such problems are mainly overtopping/over-filling of the dyke, and conditions of

upstream and downstream slopes (lining quality degradation of ash dyke embankments)

Approach

The system will monitor the status of the ash pond (using important stats such as Area, Volume,

Elevation, and distance), perform time window analysis to monitor changes in any particular spot

over time, and also intimate the end-user about any anomaly observed

The flight path of the drone will be automated using the GPS coordinates of the existing flight

path(s). The GPS coordinates can be fed into the drone controller for creating predefined paths

and the drone uses the in-built GPS system to fly along the coordinates provided. These

coordinates can be changed regularly in the drone controller if required

| 22

5. USE CASE 2 – Zero contact screening

Product Features

i. Real-time symptom screening system.

ii. User Interface to see live video feed (UI version).

iii. Deep learning-powered engine to screen covid-19 symptoms.

iv. User Interface to display the result in form of a green/red on the screen.

v. UI less version which only triggers responses as a red/green LED indicator.

vi. Cloud Hosting of the application.

6. USE CASE 3 – Product Quality Control – Textiles

| 23

7. USE CASE 4 – Product Quality Control – FMCG

8. USE CASE 6 – Operations Monitoring – Shop floor activity tracking

| 24

9. USE CASE 7 –

Mask Detection

Helmet Detection

| 25

10. CONCLUSION

Algo8 AI products are meant to provide transparent visibility over operations to improve planning

and managerial decision-making, by consolidating and analyzing vast stores of data and making

it accessible via advanced reporting and visualization tools. iVision is an end-to-end solution,

geared towards delivering operational benefits and streamlined workflows for departments

across a company. These include, but are not limited to, Maintenance and inspection, Production &

Operations, Inventory, Upper management, and associated departments such as finance, accounts,

HR etc.

Buy iVision.

Root cause failure analysis is a process after failure of any machine/asset/equipment. This

feature needs to be included somewhere to make this product complete. May be discussed

further.