- About Us
- SAP Business Technology Platform
- SAP S/4HANA Implementation
- SAP Implementation & ERP Consulting Services
- SAP Auditing & Testing Services
- SAP Maintenance & Support
- AI & Data Science
- Machine Learning
- Deep Learning
- Natural Language Processing
- Data Architecture
- Data Pre-Processing
- Reporting & Dashboarding
- Mobile App Development
- Service Now
- Portals & Collaboration
- Internet of Things (IoT)
- Product Engineering
- Sales Force
- Application Development & Management
- Product Development
- Dataware Housing & Bussiness Intelligence
- Staffing And Consulting
AI & Data Science
Bringing the future of Technology
ICS is a leader in Data Science and AI based service offerings. The combination of our SME's, Engineers, Extensive homegrown IP, thorough knowledge of processes, tools and technologies provide us with a decisive advantage in solving our client's most complex problems. Our engineers have helped numerous companies integrate with IBM Watson, Microsoft LUIS, and Amazon Lex, as well as build custom AI Engines with enhanced Deep Learning capabilities utilizing Google’s TensorFlow.
ICS's team of data scientist apply inhouse patented technologies to provide cutting edge solutions for our clients. We pursue industry-standard supervised and unsupervised machine learning methodologies.
Services we offer
- Anomalies Treatment
- Data Reduction
- Feature Engineering
- Statistical Modelling Using Time-Series or Regression
- Non-Linear & Iterative Modelling Using Decision Tree, SVM, ANN, Naïve Bayes
- Ensemble Modelling Using Random Forest & Gradient Boosting
- Cross-Validation & Back-Testing
Our Expertise in Machine Learning
- Convert data in a machine-friendly format
- Adopt insights relating to business problems
- Create new & better variables / predictors
- Seek crowd wisdom to get an optimal solution by neutralizing errors
- Execute algorithmic components stacking
- Manage architecture challenges to deliver an improved outcome
- Prepare data for feature engineering
- Create segmentation and define the target variable
- Train historical data and project probable outcome
- Report insight gains through visualization dashboard
- Ensure model’s quality through accuracy report, and back-testing using the historical data
Deep Learning is now considered the de-facto engine of Big Data analytics. Thanks to significant advancements of the hardware, the data, and the algorithm, it visibly outperforms some traditional machine learning methodologies in terms of time and accuracy. Especially, Deep Learning as backend methodology dramatically powers NLP applications.
Methodologies we use
- Convolutional Neural Network ("CNN")
- Recurrent Neural Network ("RNN")
- Long Short-Term Memory ("LSTM")
- Reinforcement Learning
Our Expertise in Deep Learning
- Deal with a very large dataset and generate the faster and more accurate output
- Overcome the limitation of traditional artificial neural works while encountering unique business problems
- Use Convolutional Neural Network (CNN) to effectively aggregate the data and allow the network to go deeper with fewer neurons (e.g. image processing and image classification)
- Use Recurrent Neural Network (RNN) to capture the temporal dynamic process in which the sequence or order of items is meaningful (e.g. natural language sentence processing and times-series analytics)
- Use Long Short-Term Memory (LSTM)—an advanced version of RNN—with explicit controls on the state or the memory on the network to avoid vanishing gradient problems (e.g. automatic machine translation on the fly)
- Use Autoencoder—mainly for data compression—can help drastically improve classification and anomaly detection by ignoring the error as to focus on "normality" (e.g. fraud detection)
- Use Generative Adversarial Network (GAN) to generate the fake data that would be almost indistinguishable from the real data so as to drastically improve the quality of the original data (e.g. image resolution boosting and style transfer)
- CPU/CUDA programming capabilities to deliver the analytics output "on time" Services we offer
- Define Neural Network Architecture—layers, weight distributions
- Define Loss Function and Optimization Method
- Control Overfitting by Regularization
- Handle Vanishing Gradient Problems
- Generate Outputs with no Feature Engineering involved
- Boost Machine Learning performance involving Natural Language Processing, Image Processing, and Time-Series Analysis
- NoSQL Databases
- Hadoop Ecosystem
- GPU Computing
Natural Language Processing
Natural Language Processing ("NLP") has become a leading force behind the AI revolution. The textual data encompasses the absolute majority of the data, and its inherent business value is being generated by NLP. Most clients do not realize that they already have sufficient magnitude of their in-house textual data, and they fail to achieve the competitive edge involving NLP.
Methodologies we use
- Text Vectorization and Information Extraction
- Part-of-Speech ("POS") Tagging
- Named Entity Recognition ("NER")
- Dependency Parsing
- Word2Vec and Doc2Vec
Services we offer
- Text, Logs and Image Data Processing
- Symbol Extraction & Noise Treatment
- Structure Unstructured Data
- Transform Symbols to Numbers
- Semantic Analysis & Ontologies
- Complete Integration with Machine Learning
Data Architecture is considered the most critical part of the digitizing business. Using data effectively requires the right data architecture, built on a foundation of business requirements. We ensure you benefit from the latest advances while modernizing your existing IT estate towards a powerful and agile digital platform – industrializing where it counts, innovating where it makes the difference.
- Define the logical layers and components of the existing data architecture.
- Understand the atomic patterns.
- Understand composite (or mixed) patterns for data solution.
- Choose a solution pattern and the right implementation tool.
- Devise a Data Strategy, outlining business aims and objectives for improved collection and use of data.
- Design Data Integration, Data Warehousing, and Reporting strategies
Data Integration & Management
- Understand Core business imperatives and target solution space to maximize Data Quality and Data Lineage on historical and inter-date data streams, Metadata Modeling, Data Economics.
- Leverage reusable frameworks and tools to deploy custom solutions
OLAP and BI Data Modeling
- Improve the performance and agility of the Strategic Datastores / OLAP
- Design and develop Data Mining models and applications
ICS follows CRISP-DM Model (Cross Industry Standard Process for data mining) for executing data wrangling and munging. Our Speciality Includes Importing of data from structured and unstructured sources, Web scrapping of public data, image and text extractions, excel rows and columns. Also, we process Big Data’s data using the Hadoop Ecosystem, Apache Spark, RapidMiner and Cassandra.
Data Preprocessing Important to your business
Data Preprocessing is required because of the enormous unformatted real world data. The real-world data is mostly composed of
- Missing Data – There are lots of reasons why there is lots of missing data. Some of them include mistake in a manual data entry or a technical problem with biometrics and much more.
- Noisy Data – One the main reason why there is noisy data is could be due to a technical problem of a gadget or a human error due to manual data entry.
- Inconsistent Data – Inconsistency of data are due the existence of duplication within data, mistakes in a name or code, human error due to manual entry, violation a data and much more
Services we offer
- Data Import - Data Importation from structured and unstructured sources
- Web Scrapping - Web Scrapping of public data, Images and text extractions, excel rows and columns
- Big Data Processing - Big Data Processing using Hadoop Ecosystem, Apache Spark, Rapid Miner, and Cassandra
- Data Scrubbing - Data Scrubbing and Cleaning take care of missing values, inconsistent data, anomalous data, and outliers.
- Data Transformation - Data Transformations like extractions, parsing, joining, standardizing, augmenting, cleansing, consolidating and filtering to create desired wrangling outputs. We even perform feature engineering to obtain new predictors.
Reporting & Dashboarding
It is a usual practice that companies construct models to align historical data and examine reasons behind past successes or failures to gain insight. Marketing, Finance, Sales and Operations typically relies on this kind of analysis majorly for management reporting.
ICS provides sophisticated reporting, dashboards and analytical tools for businesses to explore and interact with information.
Services we offer
- Consolidating facts and figures in a document with data snippets and visuals.
- Identifying the audience and creating the outline based that in mind will be the first step.
- Gathering data, writing the analysis, highlighting the key points
- Reports can be made to showcase high-level facts (for the management) and detailed description (Technical).
Our Expertise in Reporting and Dashboarding
Data Visualization – Organizing and arranging data for visual communication. It can be articulated in Time Series, Ranking, Part-to-whole, Deviation, Frequency Distribution, Nominal comparison, and Geospatial distribution. We provide visualization:
- Displaying data in more sophisticated ways such as infographics, geographic maps, sparklines, heat maps, detailed bar charts, and pie charts
- Creating visually appealing dashboards with all key performance indicators (KPIs) relevant to the business objective.
- Creating Data Dashboards to answer important questions and project real-time data continuously.
- Reporting – Consolidating facts and figures in a document with data snippets and visuals.
- We provide a report with high-level facts for the management and provide a detailed description for Technical Understanding.