Customer support automation that handles the routine, so your team handles the exceptions.

Proof

• 01

  AI ticket classification and routing
  Inbound tickets are classified automatically on arrival using a model trained on your historical ticket corpus: issue type, urgency, customer tier, and required expertise. High-confidence tickets route straight to the correct queue; the 10-20% below threshold go to a review queue a team lead confirms with one click. Routing rules live in a configuration UI, so support managers change logic without code changes. It works inside your existing help desk, Zendesk, Freshdesk, Intercom, HubSpot Service Hub, or Salesforce Service Cloud, with no migration.
• 02

  Self-service chatbot and FAQ automation
  AI chatbot deployed in your website support widget and optionally your mobile app. Authenticated users self-serve on their own data: order status, subscription and billing history, password resets, refund status. FAQ automation runs a RAG pipeline over your knowledge base, so answers come from your documentation with a citation link, not hallucinated generic responses. Containment typically starts at 25-45% of queries resolved without a ticket, improving to 40-65% as coverage expands.
• 03

  First-response automation
  Automated first responses go out within seconds for high-confidence classifications, and they contain the specific answer, not a generic acknowledgement: an order status query gets the live status and tracking link. Medium-confidence tickets get an acknowledgement with a reference number and a realistic resolution estimate. Sensitive categories, complaints, refunds, legal mentions, negative sentiment, are never auto-answered; they flag for human review in priority queues. All responses use your brand-approved templates, with configurable send timing.
• 04

  Order and account query automation
  Order and account queries are the highest-volume automation category for e-commerce and subscription businesses, because the answers already exist in connected systems. Integrations to Shopify, Stripe, PayPal, Chargebee, and Recurly pull order status and normalize carrier tracking into plain language, fetch refund status, and read subscription and billing detail. Authentication is verified before any account data is returned, customers only see their own records, and every lookup is logged to an audit trail.
• 05

  Escalation and priority workflows
  Escalation logic combines rules and AI signals to surface tickets before they become complaints: repeat contacts, high-value accounts, trigger keywords, and sentiment scoring calibrated on your ticket history. SLA breach prevention alerts at 70% of the window and upgrades priority at 90%. When a ticket escalates, the agent receives a compiled context card: conversation history, account summary, escalation reason, and steps already taken. Agents resolve escalated cases faster with the full context package than when they start cold and ask the customer to repeat themselves.
• 06

  Support platform integration
  Automation integrates into your existing support platform rather than replacing it, so agents keep their current tool. It works inside Zendesk, Freshdesk, Intercom, Salesforce Service Cloud, or Help Scout. Beyond the help desk, the layer connects to your OMS, CRM, and subscription platform for the data answers require. Integrations use OAuth 2.0 with read-only scopes wherever possible, and a webhook event bus keeps classification and routing fast, typically well under a second.

• 01

  Ticket analysis and automation mapping

  Before development begins, we analyze a sample of your historical tickets, typically 2,000 to 10,000, to build an objective picture of your ticket mix. The output is an automation opportunity map: categories ranked by volume, estimated automation rate, required data sources, and achievable confidence. Order status might be 94% automatable; billing disputes 0%. We start with the highest-volume, simplest-to-automate categories, and the analysis happens before pricing, so the estimate reflects your actual scope, not a template.

• 02

  Integration with your backend systems

  Support automation without backend data integration produces generic responses: it can answer FAQs but cannot tell a customer where their order is. We integrate the systems the answers live in, Shopify, WooCommerce, Stripe, Chargebee, Salesforce, and HubSpot: your OMS for orders, your billing platform for subscriptions, your CRM for customer data, and unified carrier tracking. Every integration ships with retry logic and circuit breakers, so a backend outage degrades gracefully to an agent instead of erroring at the customer.

• 03

  Classification and routing model

  The classification model is trained on your labeled historical tickets, not a generic model that has never seen your product terminology and complaint patterns. We agree performance targets with you before deployment and calibrate them per category against your own data. Automated-response categories target high recall; escalation detection gets the strictest target, because missing an escalation is worse than over-escalating. The exact thresholds come from your ticket history, not a template number. Multilingual pipelines handle mixed-language ticket bases, and the model retrains during the first year on newly resolved tickets as your product and terminology evolve.

• 04

  Evaluation and confidence calibration

  The confidence threshold that separates automated response from human review is the most important operational parameter in the system, and it is calibrated empirically per category, not set arbitrarily. We tune for high precision on automated responses at the highest recall each category supports, and the numbers we commit to come from your data, not a brochure. A production dashboard tracks containment rate, automation accuracy, and missed escalations, the most critical metric, with drift indicators that trigger retraining. It is built for support operations managers, no data science background required.

1. Week 1
   01

   Support audit and scope

   We analyze your current ticket volume, category distribution, and resolution patterns. We identify which query categories are automatable at high confidence and which need human judgment. You leave week 1 with a written scope and a fixed-price quote.

2. Weeks 2-3
   02

   Intent model and routing design

   We design the intent classification model and routing logic. Thresholds are set per category, not globally. High-volume, high-confidence categories go to automation. Complex or low-confidence tickets route to human agents.

3. Weeks 4-8
   03

   Build, integrate, and tune

   We build the automation layer, connect it to your helpdesk (Zendesk, Intercom, Freshdesk), and run accuracy tests against your historical ticket data. The first automated workflow goes live around week 4. QA runs in parallel with every sprint.

4. Weeks 8-12
   04

   Deploy and monitor

   Production deployment with monitoring activated. Automation rate, deflection rate, and CSAT tracked from day one. Post-launch support is included: intent tuning and model adjustments as real-world queries reveal gaps. Multi-channel builds extend this phase to weeks 12-16.

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