Services Provided
AI ML Development
UX Designing
AI Consulting
Impact
70% reduction in average first response time
92% user satisfaction rate with AI responses
$80K estimated annual savings in support ops
Automating Customer Support with a GenAI Chatbot
The Problem
The client’s support team was overloaded with repetitive queries—such as FAQs and onboarding help—which made up 60% of total tickets. With no unified knowledge base, average response times stretched to 3–5 hours, leading to inconsistent answers, lower customer satisfaction, and rising support costs due to manual handling.
Solution
Datoin developed and deployed a domain-trained GenAI chatbot that operated 24/7 across web, mobile, and helpdesk platforms. Trained on past support tickets, product documentation, and internal knowledge, the chatbot used a fine-tuned GPT-based model to deliver instant, accurate responses. It integrated seamlessly with tools like Intercom and Slack, included smart escalation to human agents for complex queries, and continuously improved through real-time learning from user interactions.
Services Provided
AI ML Development
UX Designing
AI Consulting
Impact
50% drop in inbound support calls
35% improvement in follow-up appointments
Enhanced patient trust and engagement
How a GenAI Assistant Boosted Chronic Care Outcomes
The Problem
A chronic care clinic faced low patient follow-up rates and inefficient care coordination. Patients often felt confused about their next steps, and support staff were overwhelmed with repetitive questions about treatment, lifestyle changes, and medication schedules.
Solution
Datoin implemented a GenAI-powered Virtual Care Companion to support patients between clinic visits by delivering real-time, personalized guidance. Integrated with the clinic’s EMR and trained on medical guidelines, FAQs, and patient notes, the assistant answered common queries, simplified care plan instructions, provided timely reminders, and nudged patients to complete follow-ups—while seamlessly escalating complex cases to clinical staff when needed.
Services Provided
AI ML Development
UX Designing
AI Consulting
Full Stack Develepment
Impact
40% faster content production cycle
Consistent brand voice across all assets
Significant reduction in outsourcing/content ops cost
Scaling Content with Generative AI
The Problem
Product content creation was slow, manual, and inconsistent across channels—leading to content fatigue and delayed go-to-market. Marketing teams couldn’t scale high-quality, personalized content for different personas or campaigns, resulting in lower engagement and missed growth opportunities.
Solution
Generative AI models were trained using historical product data, past marketing content, and performance analytics to create high-converting copy and visuals automatically.
The system now generates tailored product descriptions, social media captions, email subject lines, and ad creatives—aligned with brand tone and optimized for different buyer personas and platforms.
By learning from what has worked in the past (click-through rates, conversion metrics, engagement levels), the AI continuously refines its outputs, generating content that is more likely to resonate with specific audiences and contexts.
Services Provided
AI ML Development
UX Designing
AI Consulting
Full Stack Develepment
Impact
BEFORE
22.56% Customer Chrun Rate
AFTER
16% Customer Chrun Rate
Customer Satisfaction/Churn
The Problem
Delaying or no actioning to retain customers with churn risk. Enterprise spends a lot of money retaining customers without insights backed by customer information. Sales teams don’t have clarity on dealers’ health and performance, which leads to a disconnect between the customer and the business and also impacts the customer’s lifetime value.
Solution
A retention stage is very important in the customer life cycle because according to market theory, it is always expensive to attract new customers than retaining existing ones. Thus, a churn prediction system that can predict accurately ahead of time, whether a customer will churn in the foreseeable future and also help the enterprises with the possible reasons which may cause a customer to churn is an extremely powerful tool for any marketing team.
ML model to predict ahead of time if and when the dealer will churn in the foreseeable future using data features based on order and payment behavior, complaints, etc and also predicting the root cause of the churn.
Services Provided
AI ML Development
AI Consulting
Full Stack Development
Impact
19% Increase in Sales Growth
Less than 6% Variance Between Target and Actual Revenue
6 Weeks to realize ROI
Sales Forecasting
The Problem
The scenarios of product demand, collection, and competition scenarios are endless, and the rule-based approach to setting targets could not address a significant variation in data which led to frequent overestimation or underestimation of targets as the sales targets were not based on area-specific deep analysis of their area.
The goal is to improve the top-line, improve dealer relationships, and achieve sales targets.
Solution
Using an AI-based approach to predict the monthly sales trends for each sales area and factor in the growth or slump. ML identifies patterns in the historical sales data, transactions, collections, complaints, visits, trade promotions data, market trends, customers’ buying behavior, and external parameters. The predicted sales gives insights to sales managers to set realistic goals, create effective sales strategies, and manage their team and resources wisely. ML uses historical data to understand demand, the performance of sales executives, the health of the dealer to come up with the right sales target for the area.
Services Provided
AI ML Development
AI Consulting
Full Stack Development
Impact
BEFORE
6.15% Increase in Sales Growth
AFTER
10.10% Increase in Sales Growth
Demand Forecasting
The Problem
The company has a 20,000 dealer network, to which it supplies its products, which in turn sell it to the end customers. The company has an established network of more than 100 depots across the country. These depots hold product inventory and are fulfill dealer orders. If the product is available the order is successfully fulfilled but in case a product is not available or not available in sufficient quantity, the order is rejected. 7% of orders were rejected due to insufficient stock at depots.
The primary objective was to reduce the loss of sales due to insufficient stock.
Solution
Using an AI-based approach to forecast the demand of products for a particular depot instead of conventional rule-based methods. ML uses historical data to understand the demand of products in the area served by the depot. The predicted demand gives insights to depot managers to stock appropriate product quantity. In addition to the loss of sales, the inventories are also optimized to reduce overstocking.
