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R & D

Areas **Of Operation**

IntelliumAI is an AI-for-Engineering deep tech company based in Bristol-UK helping organisations adopt and scale AI to accelerate the monetisation of their data assets.

About **Intellium AI**

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Our vision is to enable our clients to achieve a multi-fold increase in their ability to rapidly adopt AI using existing assets and workforce.

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Vision

Our USP is the ability to leverage both industry domain knowledge as well as deep AI expertise to unlock AI across your organisation.

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Value Proposition

IntelliumAI is a Bristol-UK based company founded by Kiran Krishnamurthy in 2017, with a unique blend of industry domain knowledge and expertise in cutting edge AI technology.

Kiran’s background is in Aerospace industry and its supply chain and has worked in leading blue chip companies like Airbus, IBM, Dassault Systems over the past 20 years. In the lead up to starting IntelliumAI, Kiran was leading the Data Science team at Airbus Group Innovations transforming Airbus’ Manufacturing operations with Artificial Intelligence.

Kiran noticed a disparity in the market in that the exponential rate of digital data growth was not matched with the necessary Data Science skills to leverage the data leading to a bottleneck in AI-driven productivity and efficiency gains. IntelliumAI was setup to address this disparity with a software solution that democratises AI.

Our **Story**

The emergence of Artificial Intelligence (AI) technologies and the growth in big data has opened up novel and exciting digital transformation opportunities. Yet, very few companies have been successful in adopting and exploiting the full potential of AI and so gain competitive edge.  One of the biggest barriers to this is the shortage of ‘industry suitable’ AI skills, followed by additional barriers such as unclear starting point, AI hype, information overload, fear of unknown, software and hardware complexities, potentially huge financial investment without clear ROI etc.


IntelliumAI, a company based in south west of UK has developed a solution to address these challenges and accelerate adoption of AI both in large OEMs as well as SMEs. Leveraging their expertise in high value manufacturing industry as well as cutting-edge AI technologies, IntelliumAI has developed a cloud-based enterprise AI platform called ‘AiBoost®’ to accelerate the adoption of AI in industry. The platform aims to create ‘citizen data scientists’ within businesses by giving AI ‘power to people’. This enables users with wide range of skill levels to get started on the AI journey in a governed, automated and scalable manner.


Some of the main features of AiBoost® platform are; ‘AI Workbench’: A virtual workbench to standardize and automate end to end AI workflow covering model training, evaluation, inspection and prediction, ‘Rules Factory’: An intelligent algorithm to generate easy to understand rules and insights from large volumes of data and ‘Explainable AI’: A framework for robust, fair and transparent AI to build trust and confidence in the AI based automated decision-making process.


SMEs are especially poised to benefit from the AiBoost® platform as it abstracts the software and hardware intricacies from the end users and allowing them to focus on their core expertise. This results in significant productivity gains at a substantially lower financial investment. Typical advantages of the AiBoost® platform include; accelerated product design cycles, faster manufacturing maturity, reduced manufacturing cost, lower operational costs, increased productivity and faster time to market.

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Energy

Defence

Automotive

Aerospace

Power of AI in your hands with AiBoost®!

MLOps

AutoML

Explainable AI (XAI)

AiBoost® : Democratise AI with Enterprise AI platform

## Problem Statement

Record demand for commercial Aircrafts (prior to the pandemic and hopefully the demand will resume shortly) had led to huge backlog of orders (greater than 7 years in case of single aisle aircrafts). This had resulted into pressure to ramp up the production rate and reduce the delivery lead times.

However, Simultaneously, Aerospace manufacturers were also faced with a challenge to reduce the energy consumption at their facilities. This was a result of both voluntary commitment towards industrial sustainability as well as mandate from the Governments.

Combination of the above two factors had led to the need for investigating novel methods for identifying opportunities that could lead to reduction in energy consumption whilst meeting the business objective of increasing production rate.

We developed an Artificial Intelligence based solution to identify such novel opportunities to reduce carbon foot print in high volume manufacturing environement.

Some of the salient features of our solution are as below.

## Solution Main Activities

- Manufacturing process parameters data captured for both drilling operations as the ‘predictors’
- Corresponding energy consumption data captured as ‘labels’
- Performed data pre-processing and visualisation, feature engineering and subsequent down selection
- Development, evaluation and down selection of various supervised machine learning models

## Key Achievements

- Developed a supervised machine learning model predicting energy consumption corresponding to various drilling operations
- Generated insights on manufacturing process parameter tuning to reduce energy consumption
- Spread awareness about benefits of data analytics techniques applicable to shop floor environment
- Promoted a culture for data curation and data harvesting within the business

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Construction

Transport

Boost industrial energy efficiency & sustainability

Recruitment

AiBoost® to boost energy efficiency and support industrial sustainability

## Problem Statement

At a large aircraft manufacturing company, during Wing Assembly stages, small gaps were seen between the interfacing components. These gaps had to be filled by a manual ‘overhead operation’ called Shimming. This operation was a significant disruption to enable the desired ramp rate. Hence the objective of this activity was to understand the root cause leading to gapping and extract non-intuitive insights to reduce shimming process.

## Solution Main Activities

Collected key product characteristics data for all the interfacing components (i.e. Wing Covers, Ribs etc.), Jig calibration data and Wing deflection
Combined previously isolated ‘historical data set’ with the corresponding ‘events in real life’ i.e. gaping during assembly
Developed supervised machine learning algorithm to identify the root cause characteristics leading to gaping

## Key Achievements

- Predictive data analytics that enabled the aircraft manufacturer to reduce and eliminate cost of non-quality i.e. reduce amount of gap-filling per wing assembly and enable production ramp up
- Prediction algorithm predicting gap filling cases with 95 % accuracy to enable accurate workforce deployment planning
- Spread awareness about benefits of data analytics techniques applicable to shop floor environment
- Promote a culture for data curation and data harvesting within the business

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Boost manufacturing productivity with AiBoost®

Industrial application of Artificial Intelligence

AI for Root Cause Analysis

### Problem Statement
At a large aircraft manufacturing company, during robotic drilling and Wing assembly operation, key product characteristics were measured only at predefined intervals to reduce cycle time. However, any anomaly detected by this approach was 'reactive to the event' and the production had to be halted till solution to fix anomaly was identified and implemented. Hence a more comprehensive, automated and proactive quality inspection process was required to predict and prevent production line disturbance.


### Solution
We employed a holistic approach of tracking and processing every aspect of manufacturing process (IoT data). Supervised machine learning algorithm was developed to deduce insights by correlating historical manufacturing key process characteristics (IoT process data) with corresponding key product characteristics (inspection data).

The algorithm was then able to detect anomalies and make proactive predictions about the product characteristics.

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### Key Achievements
- Unlocked the value stored in the shop floor IoT data
- Promoted a culture of data curation and data harvesting within the business
- Reduced/eliminating inspection step
- Improved yield and production efficiency
- Enabled two-way flow of information, by first generating insights from manufacturing data and then in the feedback loop send information back to the robots and adapt their settings to dynamically adjust manufacturing operations based on real-time conditions

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AI for Industrial Inspection

Intellium AI

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Getting started on your AI journey can be a daunting challenge. IntelliumAI’s products and services simplifies and accelerates AI adoption in your organisation.
We support organisations to identify opportunities for AI adoption and accompany in the journey from proof of concept creation to solution deployment.

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