Done-for-You Support Automations That Actually Work
Yes, businesses reduce customer support costs with custom automations that triage tickets and draft responses.
The system connects to your existing help desk, reads incoming tickets, classifies them, and drafts replies using your knowledge base. It handles repetitive inquiries so your agents can focus on complex customer issues. It is not a chatbot; it's a production system that works alongside your human team.
We built a triage system for a 12-person support team handling 500 tickets per day. The automation classified incoming emails into 8 types and routed them to the correct agent queue. The build took 16 days and reduced their average first-response time from 6 hours to under 45 minutes.
This can deflect 30-50% of inbound tickets and cut agent response time in half.
What Problem Does This Solve?
Many support teams start with the built-in automations in their help desk, like Zendesk Triggers or Intercom Rules. These are great for simple keyword matching, but fail with any nuance. A rule that spots the word 'refund' can tag a ticket, but it cannot distinguish between a customer asking for a refund and one asking about the refund policy.
A common next step is a dedicated chatbot platform, but these introduce their own problems. They often require you to build and maintain complex decision trees that break easily. More advanced AI bots can be black boxes, making it impossible to debug why a customer was given a strange answer. They also struggle to integrate with internal databases and perform actions, like issuing a credit from your billing system.
Consider a SaaS company using Intercom for support. 40% of their tickets are password reset requests. Their bot can find the 'how to reset your password' article, but it cannot trigger the reset link for a specific user. An agent still has to open the admin panel and do it manually. The bot added a frustrating step instead of solving the problem because it couldn't authenticate a user and write to the production database.
How Does It Work?
We begin by connecting to your help desk API, whether it is Zendesk, Intercom, or Help Scout. We pull the last 3 months of ticket history to identify the top 5-7 ticket types that are prime candidates for automation. Using this data, we fine-tune a classification model with the Claude API to achieve a baseline accuracy of over 95% on your specific ticket content.
We build a FastAPI service that listens for webhooks from your help desk. For each new ticket, the service sends the content to the Claude API for classification and entity extraction. Based on the classification, it triggers other actions. For a 'billing question', it calls the Stripe API to fetch subscription data. For a 'bug report', it uses the Jira API to create an issue, pre-filling it with user details from the ticket. The entire workflow executes in under 500ms.
The system then drafts a response for your agent. We create vector embeddings from your entire knowledge base using Supabase pgvector and store them. When a ticket comes in, we find the 3 most relevant documents and feed them to the Claude API along with the ticket context. This generates a safe, accurate draft that the agent can review and send in one click, directly within their existing help desk interface.
The entire service is deployed on AWS Lambda, ensuring it scales with your ticket volume while keeping costs low, often under $50/month for 10,000 tickets. We provide a monitoring dashboard on Vercel that tracks classification accuracy and API health. Any processing error triggers an immediate Slack alert via a webhook, so we know about problems before your agents do.
What Are the Key Benefits?
First Response in Seconds, Not Hours
The system auto-triages and drafts a reply in under 2 seconds. This allows agents to resolve more customer conversations during their shift.
Pay for the Build, Not Each Seat
A one-time project engagement with a flat monthly maintenance fee. No escalating per-agent SaaS bill that penalizes you for growing the team.
Your Code, Your Cloud, Your Control
You get the complete Python source code in your company's GitHub repo and a detailed runbook. There is zero vendor lock-in.
Alerts Before Your Agents See a Problem
The monitoring dashboard tracks API health and model accuracy. You get a Slack alert if classification accuracy dips below 90% or an external API fails.
Works With Your Help Desk, Not Against It
The automation integrates with the Zendesk, Intercom, or Help Scout APIs. Agents work inside their existing tools, enhanced with AI-powered drafts and tags.
What Does the Process Look Like?
System Audit (Week 1)
You provide read-only API access to your help desk and knowledge base. We deliver a report identifying the top 3-5 automatable workflows and a firm project scope.
Core Logic Build (Weeks 2-3)
We write the core classification and integration logic in Python. You receive access to a private GitHub repository with the initial code for your review.
Deployment and Testing (Week 4)
We deploy the system in your cloud account and connect it to a sandbox environment. You and your team test the full workflow with sample tickets.
Go-Live and Monitoring (Post-Launch)
We switch the webhooks to your production help desk. For the first 30 days, we monitor system performance daily and hand off a final runbook and monitoring dashboard.
Frequently Asked Questions
- How much does a done-for-you support automation cost?
- Pricing depends on the number of unique ticket categories to classify and the number of external systems to integrate with. A system that only classifies tickets is simpler than one that also reads from Stripe and writes to Jira. Most builds take 2-4 weeks. We provide a fixed-price proposal after the initial system audit so you know the full cost upfront. Book a discovery call at cal.com/syntora/discover to discuss scope.
- What happens if the automation classifies a ticket incorrectly?
- The system assigns a confidence score to every classification. Any ticket with a score below 95% is automatically flagged for human review and is not used for auto-drafting a reply. This prevents errors on ambiguous tickets. The runbook includes instructions for providing corrected examples to the system, which improves its accuracy over time.
- How is this different from using a tool like Gorgias?
- Gorgias is a help desk with excellent e-commerce automations for pulling Shopify data. A custom build is for logic that a pre-built platform cannot handle. This includes multi-step workflows across non-e-commerce systems, like checking a proprietary user database or interacting with a custom internal API. We build the exact workflow your business needs from scratch.
- What if the AI drafts an inappropriate or 'hallucinated' response?
- We use several guardrails to prevent this. The AI is strictly prompted to only use information from your verified knowledge base documents. We also implement checks to flag responses that contain sensitive keywords related to pricing, legal promises, or security. Most importantly, every AI-generated draft requires a human agent's approval before it is sent to a customer.
- Is it secure to give you access to our customer data?
- We only require temporary, read-only API access during the initial build. The final production system is deployed entirely within your own cloud account (e.g., your AWS). You control all credentials and API keys. Customer data is processed in your environment, not ours, so it never leaves your control.
- What does my team need to do during the project?
- Your involvement is highest in week one: providing API keys and participating in a discovery session to map your support workflows. After that, we handle the entire build. Your team will participate in a final testing session before the system goes live. As a done-for-you service, we act as both the engineer and project manager to minimize the time required from your team.
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