Artificial Intelligence

Build ML models that can learn and act autonomously

We empower your digital applications with human intelligence and decision-making capabilities using AL, ML, NLP & IPA

Artificial Intelligence Services

Machine Learning (ML)

We build, train, deploy and manage custom machine learning models for businesses that can access data in different formats from multiple locations using open-source programming languages, libraries, and frameworks.

Natural Language Processing (NLP)

We empower your apps, portals and chatbots with human language interpretation and simulation capabilities by building a structural knowledge base, content categorisation, context extraction and sentimental analysis.

Computer Vision (CV)

We develop applications that can derive value from images and videos at a scale using deep learning & neural networks for image & face recognition, object detection, object tracking & content-based image retrieval.

Cognitive Platforms

We design platforms that encompass machine learning, reasoning, natural language processing, dialogue and narrative generation capabilities for data analysis, adaptive user experience, and human-like response to the stimulus.

Intelligent Process Automation (IPA)

We apply automation across your enterprise’s repetitive, unstructured processes and error-prone manual workforce tasks with the right mix of RPA + OCR + AI virtual agents with a 360-degree auditing mechanism.

AI Package Implementation

We let you leverage off-the-shelf AI as a Service (AIaaS) like – productised AI platforms (AWS Sagemaker & Azure ML Studio),consumable ML models, speech APIs, vision APIs, OCR, and CV engines to get benefitted from day 1.

Model Lifecycle Management

We maintain your production ML model’s trustworthiness and accuracy through model maintenance, retraining, diagnostics monitoring & versioning, and facilitate continued rapid iteration even after deployment.

AI Governance

We establish guardrails to secure AI solutions and the implementation processes starting from protecting data sources, encrypting the transit data, and keeping the model fairness to settingup organization-level policies & workflows.

Pillars of Artificial Intelligence

Managed

Establish data governance, versioning pipeline management and periodic model validation

Resilient

Set up accuracy thresholds, automatic alerts, and perform regression test for ML

Performance

Choose high-perform ance storage & compute infrastructure and apply optimization rules

Measurable

Decide on benchmarks and measure data volume, errors, model accuracy against it

Continuous

Learn and evolve by absorbing the pros and limitation of algorithms, datasets & tools

Customer FAQs & Our Responses

Our enterprise wants to explore AI use cases, but we are not sure we have consumable clean data and infrastructure. How can you address this?

Data availability and quality are the foundational steps for any AI project. Hence, we start the AI engagement with a pilot project where we select a quick win use case and then analyze your data sources, formats, and accuracy. Lack of the right infrastructure isn’t a problem for AI projects as we recommend going with on-demand cloud infrastructure that scales dynamically yet at the 30% cost of owning the infrastructure.

We want to embed AI capabilities into our existing application ecosystem rather than developing as a new stand-alone solution. How can you help us achieve it?

We have proven experience in embedding AI into the existing client ecosystem using APIs, microservices and cloud architecture patterns. We let your applications use consumable cloud storage, computing, and network infrastructure. Also, integrate with productized speech APIs, vision APIs, ready-to-use algorithms, ML studios and NLP platforms.

Our AI solution is biased and facing trust & transparency issues. How can you fix it?

Bias can happen at the data level, algorithm level and resource level. We re-evaluate your datasets to make sure to include a wide variety of data then we break down your algorithms by the number of true positives, true negatives, false positives, and false negatives. Afterwards, we cleanse your data, retrain your models with it and establish an AI governance framework that can act as a compliance mechanism and improve the trust & transparency of the AI lifecycle.

We have in-house data scientists to design ML models. Can you act as a data quality partner and also manage MLOps?

We worked as a data management partner earlier and our resources are familiar with working for cross-functional agile pods in a hybrid environment. We can act as an extended data arm to manage your data sources, integrate them, build pipelines, normalize data, and make it ready to feed into your algorithms. Also, we maintain the accuracy of your production models through continuous monitoring and training them with new data.