Artificial Intelligence
“AI First” is not just a buzzword for us – we passionately believe that the software platforms that power Enterprises of the Future will be Intelligent at their Core. We also believe that the Intelligence is migrating towards the Edge, with more and more applications being able to “reason” and take “intelligent” decisions at the point of action. This, in our opinion, will fundamentally alter how commerce will get conducted. Our teams are well geared to help you on this transformational journey.
STRATEGY SUPPORT
- ML Strategy including roadmap definition
- ML Landscape definition
- Opportunity Identification
- Business Case Development
CORE MODEL BUILDING
- Identify the right framework for model development
- Explore various models and select the best one
- Model Tuning
ENGINEERING SERVICES
- Data Analysis
- Data Pipeline Build
- Model Testing
- Model Management
- Model Auditing
ANNOTATION SERVICES
- Image Annotation, detection and segmentation
- Natural Language Annotation
- Speech Annotation
- Enterprise annotation tool
We have experienced teams across the AI technology spectrum from SAS and MATLAB all the way to open source technologies like Spark, R and Python.
Data & Insights
We can help you with all your data implementation and migration needs, right from POC-driven Consulting to architecture, in-house framework, implementation, hosting and maintenance. We can walk the whole journey with you, or just take a step or two. Our framework for analyzing the current data landscape, accelerators for data processing, a glossary of key industry-wise metrics, and certified techno-functional experts on the team will accelerate the whole process.
Consulting
- Data & Insights Maturity Assessment
- “Right Fit” architecture recommendation
- Data Modernization – Study and Recommendations
Data Engineering Services
- DWH / Data Lake Implementation
- Data Modernization / Migration
- Analytics Workbench
- Data Management Services
- Managed Services
BI / Visualization Services
- Gap Assessment & Tool Recommendation
- Reports & Dashboard Development | BI Migration
- Administration & Maintenance
Modern Data Architecture
- Study and Architecture Recommendation & Blueprint
- Serverless data platform design and implementation
- Stream and Batch Processing
We are technology agnostic and work with multiple tools
- Infrastructure – GPUs, CPUs, VMs, traditional databases, Hadoop, NoSQL databases
- Services – Frameworks for Machine Learning and other SaaS based solutions for analysis and schematization
- Tools – Machine Learning frameworks, development tools, DevOps tools
Blockchain
Having been one of the early adopters of this technology, we can help you de-mystify the Blockchain phenomenon and help you unlock the potential of this cutting-edge technology. We look at Blockchain to improve and enhance existing processes and technologies in the short term. In the long term, we believe it can make fundamental changes to the way things work.
Our key services
Blockchain based proof-of-concept for establishing business value
Platform, infrastructure, consulting for Blockchain development
Blockchain application functional, scalability and performance testing
Infra implementation for private blockchains – on premises and cloud
IoT implementation through Blockchain smart contracts
Iterative blockchain application development
Blockchain based analytics and visualization
DApps, Client API and SDK development
Intelligent Automation – RPA
Robotic Process Automation (RPA) is a powerful agent for automating manual processes and helping organizations free up their biggest asset – people. Organizations can thus transform their workforce to focus on higher value tasks that involve judgement, creativity and soft-skills. A well-managed RPA implementation has the potential to transform an organization, with an orchestrated symphony of processes, where intelligent operations lead to an impactful customer experience and higher operational efficiencies.
Functions that include repetitive, standardized and transactional processes are ripe for RPA. Industries embracing RPA include Insurance, Healthcare, Human Resources, Financial Services and Customer Support.
For a successful RPA implementation, as much as it is important to understand available tools and technologies, it is important to grasp the operational process and identify the right path to making them as automated as possible. Our experience in working with a global clientele over the years has enabled us to understand the processes, identify gaps and explore automation opportunities with clear definition of ROI to the business.
Feasibility Analysis
-
Process discovery (including pipeline)
-
Identifying core process (including sub- processes)
-
Identifying processes amenable to RPA (including prioritization)
-
Tool evaluation and recommendation
Automation Design & Deployment
-
Determining complexity (and need for custom scripting)
-
Recommend automation solution
-
Creating a roadmap for RPA implementation
-
Incremental implementation of RPA
Ongoing
Maintenance
-
Helping Organizations become self-sufficient
-
Setting up governance across stakeholders
-
Measuring productivity gains
-
Measuring ROI and continuously evolving automation charter
Process
re-imagination
-
Bring real time Operational & Business Intelligence
-
Improving Process Maturity
-
Drive cost efficiencies
-
Simplifying organization for better employee & customer experience
IOT
Over a million new IoT devices are connected to the Internet daily, and these numbers are accelerating. By the year 2020, experts predict there will be as many as 50 billion IP-enabled IoT devices. We notice an increase in hardware manufacturers creating new connected devices, applications and business processes – which is resulting in varied products, services and workflows.
The success of connected devices, or connected environment, catering to real time and efficient decision making is dependent on accessing, storing and processing of data.
- Connect edge hardware, access points and data networks to other parts of the value chain
- Design to store and process the data being generated from sensors, devices, gateways, machines, website, applications, customers, partners and other sources.
- Architect a massively scalable, real-time event processing engine
- Make the platform data format and product agnostic
- Automate the environment to handle ongoing management tasks and data visualization
- Generate a comprehensive and integrated perspective on customers
DevOps
n today’s world, the speed at which software is produced and distributed to customers often determines the amount of value delivered. However, delivering software at a fast pace is not the only goal. If speed is not balanced with the right level of quality, systems crash. Frequent crashes will eventually slow businesses.
In the cycle of build, test, deploy and support, we work on software continuously for new functionality. All the parts are changing all the time. We can help you accelerate this cycle by intelligent automaton of the processes, leveraging open source tools and our experienced teams. Use data collected through the entire cycle to visualize work, evaluate problems & risks and make the necessary changes.
- Development
- Source / Version control systems
- Build
- Testing
- Continuous Integration
- Deployment
- Collaboration
- Release Management
- Containerization
Explainable AI
It is about explaining the useful parts of an algorithm’s working that will improve human trust, such as:
Audit the data used for training the ML models to ensure that the “bias” is understood.
Understand the decision paths for the “edge cases” – False Positives & False Negatives.
Understand the robustness of the models – specifically in relation to adversarial examples
- Need & Scope Definition
- Help define an Explainable AI strategy that will help us pick the right candidates to maximize impact.
- ROI and Use Case preparation
- During Model Building
- Understand and define the type of explanations expected out of the model
- Design the architecture of the learning method to give intermediate results that pertain to these explanations.
- Post Fact (Once the model is built)
- Detect “bias” in the data used for training the model.
- Use methods like LIME to provide “local” interpretability of features to outputs.
- Game theory based models like SHAP (Shapley Additive Explanations) to interpret target models.
- Understanding “robustness” of neural networks using adversarial techniques.
