Your support team is drowning in tickets. Your churn rate is a mystery until the revenue is already gone. AI fixes both.
We build AI systems that auto-resolve 60%+ of routine support queries, predict churn 30-45 days before cancellation, analyse customer sentiment across every channel, and personalise interactions at scale. Deployed on your existing helpdesk and CRM in 3-4 weeks.
Your CX team has a capacity problem disguised as a quality problem.
Your support queue is a bottleneck, and hiring more agents will not fix it
Your support team handles 800+ tickets per week, and most are routine: order status, password resets, refund policies. First response time has crept past 6 hours. CSAT is sliding. You hired two more agents last quarter and the queue is still growing because volume rises 20% quarter over quarter. Complex issues that need a human sit behind 50 order-tracking requests. Agents burn out, attrition runs 40% annually, and you are paying to train people who leave within 8 months.
You find out about churn after the customer is already gone
Your monthly churn rate is 4-5%, and every month it surprises you. The signals were there: login frequency dropped, support tickets shifted from feature requests to complaints, NPS fell from 8 to 4. Nobody connected them because the data lives across your CRM, helpdesk, product analytics, and billing platform. By the time your account manager notices, the customer has signed with a competitor.
You are sitting on thousands of customer conversations and learning nothing from them
Your team processes 3,000+ customer interactions per month across email, chat, phone, and social. Each one contains signal: frustration about a feature, confusion about pricing, praise for a specific agent. Nobody is analysing patterns across all of them. You run quarterly CSAT surveys with a 15% response rate. The real sentiment data is buried in those conversations, and product decisions are made without it.
Personalisation is a slide in your strategy deck, not something your customers actually experience
Every customer gets the same onboarding flow, the same email sequences, the same support experience regardless of plan or usage pattern. A power user on your highest tier gets the same check-in email as someone who has not completed setup. Your team knows personalisation matters, but doing it manually across 5,000+ customers is impossible. So everyone gets the same experience, and the customers who matter most feel treated like a number.
Audit. Build. Deploy. Measure. 3-4 weeks.
We work on top of your existing helpdesk, CRM, and communication tools. No platform migration. No six-month roadmap. We identify the highest-impact AI interventions for your CX operation, build them, and deploy them with your team trained and results measurable from week one.
CX Operations Audit
Days 1-3
We map your entire customer experience operation end to end: support channels, ticket categories, escalation paths, response times, resolution rates, churn patterns, and feedback loops. We pull data from your helpdesk, CRM, and analytics platforms to see where volume clusters, which ticket types eat the most agent time, what actually drives churn, and where customer sentiment shifts before cancellation. We interview frontline agents, CX managers, and product teams to document what actually happens versus what the process documents say. The output is a prioritised gap analysis showing exactly where AI will cut cost, improve speed, or prevent revenue loss.
Deliverable: CX operations audit with ticket volume analysis, churn driver identification, sentiment gap assessment, and prioritised AI opportunity map
Model Design & Integration Planning
Days 4-7
For each AI system, we design the architecture, define data inputs, set accuracy thresholds, and plan how it connects to your existing tools. For support automation, we categorise your ticket types, identify the 60-70% that can be auto-resolved, and design response flows that hand off to humans when needed. For churn prediction, we map the 15-25 behavioural signals in your data that predict cancellation: support ticket frequency, sentiment trends, login patterns, feature usage decline, billing changes. For sentiment analysis, we configure multi-channel ingestion and define the taxonomy. You review and approve everything before we build.
Deliverable: Model design document with data requirements, automation logic, churn signal mapping, integration architecture, and human handoff protocols
Build, Train & Test
Days 8-18
We build and train each AI system using your historical data. Support automation is trained on 6-12 months of resolved tickets, tested against real queries to validate response accuracy and tone before going live. Churn prediction models are backtested against known churned accounts to verify they would have caught the signals 30+ days in advance. Sentiment analysis is calibrated against manually tagged conversations so it handles your industry terminology, your customer language, and the subtleties that off-the-shelf tools miss. That includes multilingual support if your customers communicate in Hindi, Hinglish, or regional languages. Nothing goes live until accuracy exceeds our minimum thresholds.
Deliverable: Trained and validated AI models, support automation flows in staging, churn prediction system tested against historical data, sentiment analysis calibrated
Deployment & Enablement
Days 19-25
We deploy to production with parallel testing for the first week. AI handles queries alongside your human team so you can validate quality before full rollout. Your support agents learn how to monitor automated responses, handle escalations from the AI, and use churn risk scores to prioritise outreach. Your CX manager gets dashboards showing deflection rates, sentiment trends, churn risk distribution, and resolution quality scores. You get documentation, runbooks, and a 30-day measurement plan with baseline metrics established so you can track ROI from day one.
Deliverable: Production deployment, team training sessions (recorded), monitoring dashboards, escalation runbooks, and 30-day measurement plan
CX Operations Audit
Days 1-3
We map your entire customer experience operation end to end: support channels, ticket categories, escalation paths, response times, resolution rates, churn patterns, and feedback loops. We pull data from your helpdesk, CRM, and analytics platforms to see where volume clusters, which ticket types eat the most agent time, what actually drives churn, and where customer sentiment shifts before cancellation. We interview frontline agents, CX managers, and product teams to document what actually happens versus what the process documents say. The output is a prioritised gap analysis showing exactly where AI will cut cost, improve speed, or prevent revenue loss.
Deliverable: CX operations audit with ticket volume analysis, churn driver identification, sentiment gap assessment, and prioritised AI opportunity map
Model Design & Integration Planning
Days 4-7
For each AI system, we design the architecture, define data inputs, set accuracy thresholds, and plan how it connects to your existing tools. For support automation, we categorise your ticket types, identify the 60-70% that can be auto-resolved, and design response flows that hand off to humans when needed. For churn prediction, we map the 15-25 behavioural signals in your data that predict cancellation: support ticket frequency, sentiment trends, login patterns, feature usage decline, billing changes. For sentiment analysis, we configure multi-channel ingestion and define the taxonomy. You review and approve everything before we build.
Deliverable: Model design document with data requirements, automation logic, churn signal mapping, integration architecture, and human handoff protocols
Build, Train & Test
Days 8-18
We build and train each AI system using your historical data. Support automation is trained on 6-12 months of resolved tickets, tested against real queries to validate response accuracy and tone before going live. Churn prediction models are backtested against known churned accounts to verify they would have caught the signals 30+ days in advance. Sentiment analysis is calibrated against manually tagged conversations so it handles your industry terminology, your customer language, and the subtleties that off-the-shelf tools miss. That includes multilingual support if your customers communicate in Hindi, Hinglish, or regional languages. Nothing goes live until accuracy exceeds our minimum thresholds.
Deliverable: Trained and validated AI models, support automation flows in staging, churn prediction system tested against historical data, sentiment analysis calibrated
Deployment & Enablement
Days 19-25
We deploy to production with parallel testing for the first week. AI handles queries alongside your human team so you can validate quality before full rollout. Your support agents learn how to monitor automated responses, handle escalations from the AI, and use churn risk scores to prioritise outreach. Your CX manager gets dashboards showing deflection rates, sentiment trends, churn risk distribution, and resolution quality scores. You get documentation, runbooks, and a 30-day measurement plan with baseline metrics established so you can track ROI from day one.
Deliverable: Production deployment, team training sessions (recorded), monitoring dashboards, escalation runbooks, and 30-day measurement plan
AI systems that serve customers. Not dashboards about customer satisfaction.
Audit & Design (Days 1-7)
- CX operations audit with ticket volume breakdown, resolution time analysis, and churn driver identification
- AI opportunity map ranked by cost savings, speed improvement, and revenue retention impact
- Model design with automation logic, churn signal mapping, and integration architecture for your existing stack
Build & Test (Days 8-18)
- Support automation system trained on your historical tickets, auto-resolves routine queries across email, chat, and messaging
- Churn prediction model scoring every account with 30-45 day advance warning and reason codes
- Customer sentiment analysis engine processing all support interactions with real-time trend detection
- Personalisation triggers for onboarding, engagement, and retention workflows based on customer behaviour
Deploy & Enable (Days 19-25)
- Production deployment integrated with your helpdesk, CRM, and communication platforms
- Agent and manager training sessions (recorded) covering escalation workflows and dashboard usage
- CX ops runbook for monitoring automation quality, retraining triggers, and threshold adjustments
- 30-day measurement plan with baseline metrics and target KPIs for deflection rate, CSAT, churn, and resolution time
We build AI for your CX team. These are separate engagements.
We scope tightly so timelines stay honest and results stay measurable. Each of these is available as a standalone service.
Autonomous AI agents that handle multi-step customer workflows end to end
If you want AI that goes beyond answering questions and actually processes refunds, modifies orders, reschedules deliveries, and updates account details without human intervention, that is an agentic AI engagement. Our CX automation handles query resolution and routing. Full autonomous action requires a different architecture and testing framework.
Agentic AIWorkflow automation beyond customer experience (operations, finance, HR)
If you want to automate internal processes like invoice handling, employee onboarding, inventory management, or cross-department workflows, that falls under our broader automation service. Same methodology, different scope.
Business AutomationCustom AI model development, fine-tuning, or bespoke NLP systems
If your CX needs require a custom-trained language model with domain-specific terminology, proprietary knowledge bases, or specialised conversation flows beyond what configurable systems can handle, that is a custom AI development project with its own timeline and data requirements.
AI DevelopmentIs this right for you?
Right for you if
- You have a support team of 5-40 agents handling 500+ tickets per week, and ticket volume is growing faster than your ability to hire. Your first response time is over 4 hours, and CSAT is trending downward.
- Your monthly churn rate is above 3% and you cannot explain why specific customers leave. The signals exist in your data, but nobody is connecting them across systems.
- You want measurable results in weeks, not a multi-quarter CX transformation programme. You need AI that works inside your current helpdesk and CRM, not a new platform to adopt.
- You are spending $40,000 or more annually on your support team and suspect that 50-60% of their time is consumed by queries that should never reach a human.
Not right if
- You handle fewer than 200 tickets per week or have fewer than 3 support agents. At that volume, the investment in AI automation does not justify the return. A well-designed knowledge base and canned responses will get you further.
- You do not have a helpdesk system or your customer data is not centralised. AI needs a data foundation to work with. If your support runs on shared email inboxes and spreadsheets, start with our automation service to build the infrastructure first.
What AI for customer experience looks like in practice.
Problem
A D2C brand doing $10M in annual revenue had a 12-person support team processing 4,500+ tickets per month across email, WhatsApp, and Instagram DMs. Most tickets were order tracking, return status, and refund policy queries. Average first response time was 11 hours. During sale events, the queue ballooned to 3x normal volume, and temporary hires took weeks to train. CSAT had dropped to 3.4 out of 5.
What we did
Built an AI support automation layer integrated with their Freshdesk instance, Shopify backend, and WhatsApp Business API. The system auto-resolved order tracking queries by pulling live shipment data, handled return eligibility checks against their policy rules, and processed standard refund requests with one-click customer confirmation. Complex issues were routed to human agents with full context pre-loaded.
Outcome
Automated resolution rate hit 62% within 30 days. First response time dropped from 11 hours to under 3 minutes for automated queries. Human agents now handle only complex cases, and their resolution quality improved because they are no longer fatigued by repetitive work. CSAT climbed to 4.3. During the next sale event, the system handled 3x volume with zero additional hires. Annual support cost reduced by $22,000.
Problem
A SaaS company with 2,800 active accounts and $1.5M ARR was churning at 5.2% monthly. The CS team of 6 managed accounts reactively: they found out about unhappy customers when the cancellation request came in. NPS surveys had a 12% response rate and told them nothing actionable. Product usage data, support ticket history, and billing information lived in three separate systems with no unified view.
What we did
Built a churn prediction model that unified data from their product analytics (Mixpanel), helpdesk (Zendesk), and billing system (Chargebee). The model scored every account weekly on 22 behavioural signals: login frequency trends, feature adoption depth, support ticket sentiment, payment failure history, and engagement with onboarding milestones. Deployed automated alerts to CS managers when an account crossed the risk threshold, with specific reason codes and recommended intervention playbooks.
Outcome
The model identified at-risk accounts 35 days before cancellation with 79% accuracy. The CS team intervened on 140 flagged accounts in the first quarter, and 58% of those were successfully retained through targeted outreach, plan adjustments, or onboarding assistance. Monthly churn dropped from 5.2% to 3.1%. Retained revenue in the first quarter: $175,000. The model paid for itself in the first month.
Problem
A digital lending platform processing 15,000+ customer interactions monthly across phone, email, and in-app chat had no systematic way to gauge customer sentiment. Complaints about loan processing delays, EMI confusion, and documentation requirements were buried in individual tickets. The compliance team needed to identify escalation-worthy grievances within 24 hours per RBI guidelines, but was manually reviewing tickets and catching only 40% of flagged cases on time.
What we did
Deployed a multilingual sentiment analysis engine (English, Hindi, Hinglish) processing all customer interactions in real time. The system classified sentiment, detected complaint severity, identified regulatory-sensitive topics, and auto-flagged grievances requiring compliance review. Built a trend dashboard showing sentiment shifts by product line, geography, and customer segment. Integrated priority routing so negative-sentiment tickets reached senior agents within 15 minutes.
Outcome
Compliance-flagged grievance identification went from 40% to 96% within the 24-hour window. Average response time for high-severity complaints dropped from 18 hours to 2 hours. The product team used sentiment trend data to identify that 30% of negative interactions were caused by a confusing EMI calculation display. A UI fix reduced related complaints by 55% within 6 weeks. Overall CSAT improved from 3.6 to 4.1.