AutoML+

Still manually developing every model?

Anyone builds production AI now. iTuring AutoML automates everything: models, explanations, documentation. Deploy fast. Deliver impact.

Trusted by leading banks and insurers. Built for regulated industries with comprehensive documentation support.

Deploy compliant, production-ready models in weeks - not months

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Banks and insurers using iTuring Auto ML report measurable impact in operational efficiency, compliance, and growth.

How Automation Handles Your AI Lifecycle

Connect

AI agents clean and prepare your data (Any source, any format. Automated data treatment)

Select

Intelligent feature selection runs automatically (FCDS algorithm identifies optimal features)

Build

ML model development at scale (Algorithms compete. Hyperparameter optimization)​

Deploy

Production ready in a few clicks (Java, PMML, or API with audit documentation)

Minimum Risk. Maximum Control. Proven Results.

No Vendor Lock-in

Export models as Java, PMML, or API

Regulatory Compliant

Built-in audit trails and documentation

Expert Override

Manual control when you need it

Existing Infrastructure

Works with your current systems

Choose Your Speed: Instant or Customized

Automated Mode

Single-click transforms raw data into production models. Perfect for business analysts and time-pressed teams.

Expert Mode

Manual control over feature selection, sampling, missing treatment, outlier handling, transformations, and hyperparameters.

Every Algorithm. Every Use Case. Full Regulatory Support.

Feature

Time to Deploy

Algorithm Support

Regulatory Docs

Expert Control

2-6 hours

8 major algorithms

Automated

Full override

Manual

3+ months

Limited by team

Manual creation

Complete

Other AutoML

2-4 weeks

3-5 algorithms

Limited support

Restricted

Limited Time Offer

Frequently Asked Questions

What is Auto ML and how does it work?

Auto ML (Automated Machine Learning) automates your machine learning pipeline, from data preparation and feature engineering to model selection, tuning, and deployment. iTuring Auto ML enables rapid, audit-grade model deployment with built-in compliance support.

iTuring Auto ML typically delivers measurable ROI both in reduced project timelines and total cost of ownership compared to expanding in-house data science teams. Pricing details available on request.

Yes. iTuring Auto ML is designed for regulated industries, supporting audit trails, automated documentation, and explainability features as required by compliance teams.

iTuring supports major machine learning algorithms including XGBoost, Random Forest, Neural Networks, Logistic Regression (L1/L2), Stochastic Gradient Boosting, Light Gradient Boosting, and Naive Bayes for classification, regression, and anomaly detection.

No, Auto ML augments your team by automating routine tasks. Your data scientists can focus on strategy, business insights, and advanced model interpretation while Auto ML handles the repetitive work.

Full implementation typically takes 30-60 days, including data integration, team training, and first deployment. The pilot program enables model building within a few days of setup.

Yes. iTuring connects to SQL databases, NoSQL systems, Blob Storage, S3, HDFS, JSON files, and CSV uploads through pre-built connectors.

iTuring is built specifically for regulated industries, with comprehensive audit trails, documentation, and override by domain experts. It offers enterprise-grade security and industry-specific templates.

Our team will be happy to help you calculate ROI and build the business case to provide a clear view of average payback periods and total cost of ownership, supported by case studies.

Each client receives dedicated implementation and support, including a solution architect, data scientist, and customer success manager.

Yes. iTuring meets major financial services security requirements and can be deployed on-premise or in a private cloud. All data processing is encrypted at rest and in transit.

Tarika Bhutani

Senior Director – Sales and Marketing Operations

Tarika is a market development leader driving global growth through strategic partnerships and go-to-market initiatives.

 

She focuses on expanding enterprise adoption of AI solutions across international markets, working closely with partners and clients to enable data-driven transformation.

 

Her work centres on scaling enterprise AI through partner-led growth and direct customer engagement, supporting organisations in implementing impactful, data-driven solutions worldwide.

Vipin Johnson

Vice President – Customer Acquisition

Description Goes Here

Rajnish Ranjan

Vice President, Head – Data Science

Rajnish brings over two decades of experience leading data-driven transformation across Fortune 500 organisations.

 

His career spans senior roles at HSBC, Zafin, Cisco, TCS, Nielsen, iQuanti, Symphony, Supervalu, and Harman, delivering measurable cost savings, operational efficiencies, and revenue growth.

 

With experience across banking, retail, telecom, pharma, CPG, and digital marketing, he leads cross-functional teams at iTuring.ai to deliver advanced analytics, machine learning, and AI solutions.

Aishwarya Hegde

VP Operations & Content Head

Aishwarya has been instrumental in building iTuring.ai from inception and continues to manage core operations across the organisation. Her responsibilities span project operations, financial planning, and evaluating future expansion opportunities.

 

Prior to iTuring.ai, she worked with Market Probe and WNS Research & Analytics, delivering high-impact decision support and actionable analysis for IBM with a record of zero errors.

 

Aishwarya holds a postgraduate degree in Data Science and Machine Learning from Manipal University.

Bryan McLachlan

Managing Director – Africa

Bryan has 30 years of experience driving innovation and growth across technology, banking, insurance, and retail.

 

Prior to iTuring.ai, he held executive leadership roles at Instant Life, AIG, Nedbank, FNB, and TransUnion. He focuses on enabling enterprises to adopt AI and machine learning within trusted, governed, and risk-managed frameworks.

 

Bryan holds a Master’s degree in Commerce from the University of Johannesburg.

Mohammed Nawas M P

Co-Founder, VP Product Development

Nawas brings 20 years of experience in designing and delivering cloud-native software and data systems. He has held senior technology roles at HCL, Radisys, Kyocera, and Mindtree, leading large development teams and complex product builds.

 

At iTuring.ai, he oversees product roadmap and customer delivery, applying cloud-first thinking, deep systems expertise, and a focus on building robust, scalable AI solutions that challenge industry norms.

 

He is a graduate of Rajiv Gandhi Institute of Technology.

Amit Kumar

Amit is a technology architect with over 18 years of experience designing data-intensive systems and enterprise analytics platforms. He has built highly scalable products across open architecture models and virtualised infrastructure, aligning deep technical detail with business requirements for AI and ML solutions.

 

Prior to iTuring.ai, he held senior technical roles at Radisys and Aricent. Amit leads platform architecture with a focus on governance, lineage, and traceability.

 

He holds a First Class with Distinction BTech in Computer Science from Cochin University.

Valsan Ponnachath

President, COO and Co-founder

Valsan brings over two decades of global leadership across sales, professional services, and product operations in technology and SaaS enterprises.

 

Prior to iTuring.ai, he held senior executive roles at Fiserv, Cisco, and Sun Microsystems, most recently serving as Senior Vice President at Fiserv overseeing global system integration and international professional services. Based in California, he leads iTuring.ai’s growth in the Americas.

 

Valsan holds an MBA from the University of Nebraska and a BE in Computer Science from Bangalore University.

Suman Singh

Founder & CEO

Before founding iTuring.ai in 2018, Suman led analytics at Zafin and Fiserv as CAO and General Manager Analytics, delivering enterprise-scale solutions still running in production.

 

His work includes fraud detection systems saving clients over $19M, patented Customer Relationship Score methodology, and price optimisation recognised by the INFORMS Edelman Award (2014). He has authored multiple research papers and pioneered the data-to-value approach.

 

Suman holds a Master’s in Statistics from CCS HAU and a Bachelor’s in Agricultural Engineering from BHU.