Your customers are telling you what they need. You are not listening fast enough.
AI for CX teams at mid-market companies. Automate 60%+ support tickets, predict churn before it happens, and personalise every touchpoint. Deployed in 3-4 weeks.
The Problem
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 weekly, most of them routine: order status, password resets, refund policies. First response time has crept past 6 hours. You hired two more agents last quarter and the queue still grows 20% quarter over quarter. Complex issues that need a human sit behind 50 routine requests. Agents burn out, attrition runs 40% annually, and you pay 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 catches you off guard. The signals were there: login frequency dropped, tickets shifted from feature requests to complaints, NPS fell from 8 to 4. Nobody connected them because the data lives in CRM, helpdesk, product analytics, and billing separately. By the time your account manager notices, the customer has already signed with a competitor.
You have thousands of customer conversations and learn nothing from them: Your support agents process 3,000+ customer interactions monthly across email, chat, phone, and social. Each one contains signal: frustration about a feature, confusion about pricing, praise for a specific agent. Nobody analyses patterns across all of them. You run quarterly CSAT surveys with a 15% response rate. The sentiment data is buried in those conversations, and product decisions happen without it.
Personalisation is a slide in your strategy deck, not something customers actually experience: Every customer gets the same onboarding, the same email sequences, the same support experience regardless of plan or usage. A power user on your highest tier gets the same check-in as someone who has not finished setup. You know personalisation matters, but doing it manually across 5,000+ customers is not realistic. Everyone gets the generic version, and the customers who matter most feel treated like a number.
Our Approach
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 find the highest-impact AI interventions for your CX operation, build them, and deploy them with your team trained and results measurable from week one.
Phase 1 — CX operations audit (Days 1-3): Full CX operation map: support channels, ticket categories, escalation paths, response times, resolution rates, churn patterns, feedback loops. We pull data from your helpdesk, CRM, and analytics to see where volume clusters, which tickets eat the most agent time, what drives churn, and where sentiment shifts before cancellation. Interviews with agents, managers, and product teams capture what really happens. Output: prioritised gap analysis showing where AI cuts cost or saves revenue. Deliverable: CX operations audit with ticket volume analysis, churn driver identification, sentiment gap assessment, and prioritised AI opportunity map
Phase 2 — Model design & integration planning (Days 4-7): For each AI system, we design architecture, define data inputs, and set accuracy thresholds. For support automation: we categorise ticket types, identify the 60-70% that can be auto-resolved, and design human handoff flows. For churn prediction: we map 15-25 behavioural signals like ticket frequency, sentiment trends, login patterns, feature usage decline, and billing changes. For sentiment analysis: multi-channel ingestion and taxonomy. You approve everything before we build. Deliverable: Model design document with data requirements, automation logic, churn signal mapping, integration architecture, and human handoff protocols
Phase 3 — Build, train & test (Days 8-18): Each AI system is built and trained on your historical data. Support automation trains on 6-12 months of resolved tickets, tested for response accuracy and tone. Churn prediction is backtested against known churned accounts to verify it catches signals 30+ days in advance. Sentiment analysis is calibrated against your actual conversations, including Hindi, Hinglish, and regional languages if relevant. 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
Phase 4 — Deployment & enablement (Days 19-25): Production deployment with parallel testing for the first week. AI handles queries alongside your human team so you can validate quality before full rollout. Agents learn to monitor automated responses and use churn risk scores for outreach. CX managers get dashboards for deflection rates, sentiment trends, and resolution quality. Full handover: documentation, runbooks, and a 30-day measurement plan to track ROI from day one. Deliverable: Production deployment, team training sessions (recorded), monitoring dashboards, escalation runbooks, and 30-day measurement plan
Deliverables
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 gains, 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 that scores every account with 30-45 day advance warning and reason codes
- Sentiment analysis engine that processes 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) on 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
Who This Is For
Right for you if: You have 5-40 support agents handling 500+ tickets per week, and ticket volume grows faster than you can hire. First response time is over 4 hours, and CSAT is falling.. Your monthly churn rate is above 3% and you cannot explain why specific customers leave. The signals exist in your data, but nobody connects them across systems.. You need measurable results in weeks, built on top of your current helpdesk and CRM. Long transformation programmes and new platforms are off the table.. You spend $40,000+ annually on support and suspect 50-60% of agent time goes to 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, AI automation does not justify the cost. 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. If your support runs on shared email inboxes and spreadsheets, start with our automation service to build the infrastructure first..
Use Cases
D2C / E-Commerce: A D2C brand doing $10M in annual revenue had 12 support agents 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 sales, the queue hit 3x normal volume, and temporary hires took weeks to train. CSAT had dropped to 3.4 out of 5. — Built an AI support automation layer integrated with their Freshdesk instance, Shopify backend, and WhatsApp Business API. It 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 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, the automation layer handled 3x volume with zero additional hires. Annual support cost reduced by $22,000.
B2B SaaS: 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 useful. Product usage data, support ticket history, and billing information lived in three separate systems with no unified view. — 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 onboarding milestone completion. 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% were 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.
Fintech / Lending: A digital lending platform with 15,000+ customer interactions monthly across phone, email, and in-app chat had no systematic way to gauge 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 manually reviewed tickets and caught only 40% of flagged cases on time. — Deployed a multilingual sentiment analysis engine (English, Hindi, Hinglish) that processed all customer interactions in real time. The engine 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 find that 30% of negative interactions came from a confusing EMI calculation display. A UI fix reduced related complaints by 55% within 6 weeks. CSAT improved from 3.6 to 4.1.
Results
A CX engagement timeline
D2C e-commerce — support automation & churn prevention: 62% ticket deflection, $22,000 annual cost reduction, CSAT from 3.4 to 4.3. A D2C brand doing $10M in annual revenue had 12 agents handling 4,500+ tickets per month across email, WhatsApp, and Instagram DMs. Most tickets were order tracking, return status, and refund policy queries. First response time was 11 hours. CSAT was 3.4 and falling. We categorised 8 months of tickets and found two-thirds could be fully automated. We built the automation layer on their existing Freshdesk instance, integrated with Shopify for real-time order data and WhatsApp Business API. By day 30, the system auto-resolved 62% of inbound tickets. First response time dropped to under 3 minutes for automated queries. During the next sale, ticket volume tripled and the AI absorbed it without additional hires. Engagement cost: $8,500. Annual support cost reduction: $22,000. CSAT climbed from 3.4 to 4.3 within 60 days.
Frequently Asked Questions
How long does an AI for CX engagement take?
3-4 weeks for a standard engagement covering support automation, churn prediction, and sentiment analysis. Timeline depends on channel count, data sources, and integration complexity. Every engagement follows the same structure: audit, design, build, deploy. We do not run open-ended projects.
What helpdesk and CRM platforms do you work with?
Freshdesk, Zendesk, Intercom, Zoho Desk, HubSpot Service Hub, Salesforce Service Cloud, and Gorgias. For channels: WhatsApp Business API, Instagram DM, email, and in-app chat. If you use a different platform, we evaluate API capabilities during the audit. Your systems need APIs and at least 6 months of historical ticket data.
What percentage of tickets can be automated?
For most mid-market companies, 50-70% of support tickets are routine and automatable. The exact number depends on your ticket mix. Order tracking, password resets, refund policy questions, and status updates automate well. Complex billing disputes, product defect investigations, and multi-issue complaints still need humans. We identify the exact split during the audit.
How accurate is the churn prediction model?
75-85% on backtested data, depending on data quality, volume, and signal count. We validate the model against your historical churned accounts before deployment. If your data cannot support a reliable model (fewer than 100 churn events in the training period, or critical data missing) we tell you during the audit rather than deploying something unreliable.
Will customers know they are talking to AI?
Yes. We do not recommend disguising AI as human agents. It damages trust when customers find out. The AI identifies itself, handles what it can, and hands off to a human when it cannot. Customers care about speed and accuracy, not whether a human typed the response. Our clients see CSAT go up after deploying AI because response times drop from hours to minutes.
Can the system handle Hindi, Hinglish, and regional languages?
Yes. Hindi and Hinglish are standard. For other regional languages (Tamil, Telugu, Marathi, Bengali) we calibrate the models during the build phase using your actual customer conversations. Accuracy is lower for languages with less training data, and we are upfront about those limitations during the audit.
What does it cost?
$6-12K for a standard engagement covering support automation, churn prediction, and sentiment analysis. Depends on channel count, integrations, and data complexity. We scope and price after the audit, not before, because the audit determines what is feasible and valuable. No recurring platform fees. You own everything we build.
Do we need to replace our current helpdesk or CRM?
No. We build on top of your existing stack. The AI layer integrates with your helpdesk, CRM, product analytics, and communication channels via APIs. We do not sell software or require platform migrations. If your tools have API access, we can work with them.
