Machine learning, generative AI and agentic AI advisory for companies ready to move beyond dashboards into forecasting, automation and AI systems that run at enterprise scale. Built on 19+ years of delivery across Retail, CPG, Banking, Insurance and Hospitality.
Revathy Suran is a data science and AI leader with 19+ years of experience designing and scaling machine learning, generative AI and agentic AI solutions that turn advanced analytics into measurable business results. She has built and deployed AI systems at companies like Signet Jewelers and Mindtree, and co-founded an AI analytics consultancy.
She holds an MIT Professional Certificate in Applied Agentic AI and consults on designing autonomous, multi-step AI systems that use LLMs, tool use and orchestration frameworks to automate complex business workflows.
Her domain experience spans Retail, CPG, Banking & Financial Services, Insurance and Hospitality, industries where a wrong forecast or a missed signal can cost millions.
At Signet Jewelers, deployed ARIMA, SARIMA, Prophet and LSTM forecasting across 2,800+ retail locations to drive inventory planning, staffing and promotional strategy.
Built and deployed a computer vision model on AWS that classifies repair images automatically. It replaced a manual process that covered only 10% of images.
Every engagement starts with a free 30-minute discovery call to confirm fit and scope.
Design autonomous, multi-step AI systems using LLMs, tool use and multi-agent orchestration. We take you from strategy and use-case selection through to an implementation roadmap.
Time-series forecasting (ARIMA, SARIMA, Prophet, LSTM), demand modeling, sales prediction and prescriptive analytics, deployed on AWS with MLOps so it keeps running.
Practical generative AI strategy covering use-case identification, model selection, RAG architecture and integration with your existing workflows. Includes a responsible AI governance framework.
For organizations whose analytics haven't kept pace with their ambitions. Data strategy, platform assessment, a team capability review and a prioritized roadmap to an AI-ready state.
AI models built without domain context fail in production. These are the industries where our solutions have held up under real business conditions.
Sales forecasting, inventory optimization, demand modeling, computer vision for operations, and real-time analytics that combine POS, supply chain and social data.
Predictive models linking satisfaction to financial performance, executive analytics, branch performance forecasting and loyalty program design.
Multi-property stay propensity modeling, loyalty analytics, test-and-control frameworks for offer personalization and member targeting.
Multi-channel fulfillment optimization, ship-from-store tracking, labor planning dashboards and supply chain bottleneck analysis.
Product propensity models, cross-sell recommendation engines, upsell models deployed across 15 countries, and automated ML pipelines.
Forecasting and propensity scores delivered straight into Salesforce or Zoho CRM, where your sellers and service teams actually work.
Every engagement starts with the business outcome. The AI comes second.
A 30-minute call to define the business problem, the data landscape and what success looks like in concrete terms.
A review of your data, its quality and your infrastructure, with an honest view of what's buildable. Week 1.
Build, test and validate against business benchmarks, with regular stakeholder checkpoints. Weeks 2–6.
Production deployment with MLOps, documentation, training and a support window. Final 1–2 weeks.
Tell us about your data and AI challenge. We respond within one business day.