Build a Custom AI Agent for Your Call Center
Yes, AI agents can autonomously handle customer service inquiries for a small business. They triage common questions, letting human agents focus on complex support issues.
Syntora specializes in engineering multi-agent platforms, an architectural approach that can autonomously handle customer service inquiries by routing tasks to specialized agents. This expertise involves building systems with FastAPI and Claude tool_use, orchestrating workflows with Gemini Flash, and deploying on platforms like DigitalOcean App Platform to manage document processing and workflow automation.
The complexity of such a system depends on the variety of inquiry types, the necessary data sources, and the integrations required. For instance, handling basic FAQ responses is simpler than needing to access order history from a CRM or product details from an ERP. Syntora has experience with multi-agent platforms, having built an orchestrator using Gemini Flash function-calling to route tasks to specialized agents for document processing, data analysis, and workflow automation with human-in-the-loop escalation, deployed on DigitalOcean App Platform with SSE streaming. This architecture demonstrates how similar patterns could be adapted to automate customer service workflows.
What Problem Does This Solve?
Most small businesses start with a built-in helpdesk chatbot from a platform like Intercom or Drift. These tools rely on keyword matching and rigid decision trees. They fail when a customer uses synonyms or asks a multi-part question, defaulting to "let me get a human" and creating more work for your team.
A regional pet supply store with 8 support reps used one of these chatbots. A customer asked, "My dog food order hasn't shipped and I want to add a chew toy to it. Can you help?" The chatbot saw "shipped" and linked to the shipping policy. It saw "add toy" and linked to the product page. It completely missed the intent and escalated, wasting a rep's time on a simple, two-part request.
Trying to use a generic AI wrapper from a marketplace is another common failure. These models are black boxes that can't perform actions like updating an address in your database. They answer questions based on public data, not your internal systems, leading to incorrect responses and a loss of customer trust. They can't truly resolve an issue.
How Would Syntora Approach This?
Syntora would begin by analyzing your recent support tickets to understand common inquiry patterns. This data, potentially clustered using the Claude API, helps identify the most frequent, repetitive tasks suitable for automation, aiming to address a significant portion of your ticket volume.
The core of the system would be a custom FastAPI service built in Python. This service orchestrates the logic, with specific functions developed for each identified intent. For example, an 'order status' request could trigger an API call to your CRM, while a 'return request' would check relevant policy dates. The system would use the Claude API for natural language understanding but rely on deterministic code for executing actions, which helps prevent hallucinations. Structured logging with tools like structlog would track every decision.
The FastAPI application would be containerized and deployed on a suitable cloud environment, such as DigitalOcean App Platform or AWS Lambda, chosen based on your infrastructure and scaling requirements. It would connect to your existing helpdesk via webhooks or API integrations. An incoming inquiry would trigger the system to process the request, draft a reply, and update the ticket status.
Supabase would be used to store a detailed log of every interaction, including the AI's confidence score and the final resolution. This data would feed into a dashboard to monitor successful automations and escalations. If an escalation rate for a specific intent consistently exceeds a predefined threshold, Syntora would review and refine the system's logic. The source code for the delivered system would be provided to your GitHub repository.
What Are the Key Benefits?
Resolve Tickets in Seconds, Not Hours
The system provides an initial response in under 90 seconds, 24/7. Your customers get answers instantly, while your team's average first-response time plummets.
One Fixed-Price Build, Zero Per-Seat Fees
You pay once for the system build. There are no monthly SaaS fees that increase as your team grows or your ticket volume increases. Just low, predictable hosting costs.
You Get the Full Source Code
The complete Python codebase is delivered to your GitHub repository. You own the asset and can have any developer modify or extend it in the future.
Real-Time Monitoring Catches Errors
A Supabase dashboard tracks every inquiry, confidence score, and escalation. We set up alerts that notify us if the system's accuracy drops so we can fix it.
Connects Directly to Your CRM and ERP
The system integrates with your real systems of record, like HubSpot or an internal order database. It can look up, update, and create records to solve customer issues.
What Does the Process Look Like?
Week 1: Scoping and Data Analysis
You provide read-only access to your helpdesk and any relevant documentation. We deliver an analysis of your top 10 ticket types and a final project scope document.
Weeks 2-3: Core System Development
We build the core FastAPI service and connect it to your systems. You receive a private staging link to test the system with sample inquiries.
Week 4: Deployment and Live Testing
We deploy the system to production and run it in a 'silent mode' for 3 days, logging its proposed responses. You receive a daily report for review before we go live.
Post-Launch: Monitoring and Handoff
For 30 days post-launch, we monitor performance and make adjustments. You receive the full source code, a system runbook, and a final performance report.
Frequently Asked Questions
- How is the project cost and timeline determined?
- Cost depends on two factors: the number of systems we need to integrate with and the number of distinct actions the agent must perform. A simple triage system takes 2-3 weeks. One that modifies orders might take 4-5 weeks. We provide a fixed-price quote after the discovery call at cal.com/syntora/discover.
- What happens if the AI agent gives a wrong answer or fails?
- The system is designed to fail gracefully. For any inquiry where its confidence score is below 90%, or if an API call to an external system fails, it automatically assigns the ticket to a human agent with an internal note explaining why it escalated. It never sends a low-confidence response to a customer.
- How is this different from using a service like Ada or Forethought?
- Platforms like Ada are closed ecosystems. You are limited to their pre-built integrations and UI. Our approach gives you a standalone service you own completely. We write custom code to integrate with any proprietary internal database or legacy system, which is something pre-built platforms cannot do. You are not locked into their platform.
- Does the system support multiple languages?
- Yes. The Claude API foundation is multilingual. We can configure the system to detect the language of an incoming inquiry and respond in that same language. The initial build includes one primary language; adding others is a small, scoped add-on project that typically takes 2-3 days per language.
- How much of my team's time is required during the build?
- We need 2-3 hours from one of your subject matter experts during the first week for data review and scoping. After that, we only need 30 minutes per week for a progress check-in. The process is designed to minimize disruption to your team, as we work independently after the initial kickoff.
- How do you handle sensitive customer data?
- The system is deployed within your own cloud infrastructure and does not store PII long-term. API keys and credentials are encrypted and managed through AWS Secrets Manager. All data processing is done in-memory, and only non-sensitive metadata is logged for monitoring purposes.
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