Triage with AI, Escalate with Purpose
You interrupt your team when an AI system flags an issue as high-value, high-urgency, or an unhandled exception. Everything else is automated, logged, and handled without breaking anyone's focus.
This is not about simple keyword notifications. It is a central routing system that understands the intent behind every incoming email, form submission, or support ticket. The complexity depends on the number of input sources and the nuance of the business rules required to sort signal from noise.
We built a triage agent for a 7-person logistics company that was manually reviewing 150 support emails per day. The AI now handles 90% of them, interrupting the team for only the 15-20 critical shipment issues each day. The system was live in 2 weeks.
What Problem Does This Solve?
Most teams start with email rules or Slack keyword alerts. An admin sets up a rule to forward any email containing "urgent" to a shared channel. This works for a week, until clients learn that using the keyword gets a faster response. Soon, every email is marked "urgent," the channel becomes pure noise, and the team learns to ignore it.
This leads them to dedicated help desk software. These tools offer rule-based routing, but the logic is brittle. A rule that assigns tickets based on the phrase "billing issue" cannot distinguish between a simple request for an invoice copy and a complex dispute over a $20,000 charge. The first can be handled with an automated response; the second requires immediate C-level attention. The tool treats them identically, forcing manual review on every single ticket.
A 12-person insurance agency used a shared Outlook inbox for new claims. They set up rules to flag emails containing "new claim" to interrupt an adjuster. But they also received marketing spam, status update requests, and vendor invoices that included those keywords. Adjusters wasted the first 45 minutes of every day deleting noise and manually assigning the real claims, delaying response times by hours.
How Does It Work?
We connect directly to your source systems via API. For an Outlook shared inbox, we use the Microsoft Graph API. For a web form, we build a dedicated FastAPI webhook endpoint. The first step is to pull a historical sample of 300-500 recent requests and their final resolutions. This becomes the training data we use to teach the AI what 'urgent' actually means for your business.
We use the Claude API to classify and extract key information from each incoming request. The core of the system is a multi-shot prompt that includes 5-10 real examples of requests and how they should be categorized. The AI learns to identify intent (e.g., 'Status Update' vs. 'New Lead' vs. 'Critical Failure'), urgency, and structured data like an Account ID or Order Number. This entire classification step takes about 800ms per request.
Based on the AI's classification, a Python function determines the next action. A 'New Lead' from a company with over 50 employees is routed to a specific Slack channel and simultaneously added to HubSpot via their API. A 'Status Update' request receives an automated reply with a link to a tracking portal. A 'Critical Failure' with a negative sentiment score triggers a PagerDuty alert. Every event is logged to a Supabase table for auditing, with a total processing time under 2 seconds.
The entire workflow is packaged into a container and deployed as an AWS Lambda function, keeping infrastructure costs under $20/month for most clients. We implement structured logging using structlog, piping all system events to Amazon CloudWatch. We configure alerts for any spike in API errors (over a 2% failure rate) or if the processing queue depth exceeds 5 minutes, ensuring issues are caught before they impact operations.
What Are the Key Benefits?
Stop Triage Paralysis in 10 Days
From kickoff to a live production system in two weeks. Your team stops wasting time on manual sorting and focuses on the work that matters.
Pay for the Build, Not Per Task
A single, fixed-price project with low, predictable hosting costs. No per-seat licenses or per-task fees that punish you for growing.
You Own the Code and the Logic
We deliver the complete Python source code to your GitHub repository. The business logic is yours to keep and modify, forever.
Know Instantly When Things Go Wrong
The system monitors its own health and sends an alert if API error rates spike or processing slows. No silent failures.
Connects to Your Existing Tools
We integrate with the systems you already use: Outlook for email, Slack for alerts, and HubSpot or Salesforce for CRM updates.
What Does the Process Look Like?
Discovery and Data Collection (Week 1)
You provide read-only API access to your inbox or CRM and a sample of 200 historical requests with their known outcomes. We deliver a detailed project plan and a proposed classification schema.
AI Logic and Prompting (Week 2)
We build the core classification and routing logic using the Claude API and your data samples. You receive a private demo link to test the system with your own inputs.
Integration and Deployment (Week 3)
We connect the AI to your live systems (CRM, Slack) and deploy it to AWS Lambda. You get a status dashboard showing real-time processing and classification results.
Monitoring and Handoff (Week 4+)
For 30 days, we monitor system performance and fine-tune the prompts. You receive the full source code in your GitHub and a runbook detailing maintenance procedures.
Frequently Asked Questions
- How much does a custom AI triage system cost?
- Pricing is based on a fixed project scope. The main factors are the number of input sources (e.g., one email inbox vs. email, web forms, and API), the number of distinct destinations for routed tasks, and the complexity of the classification rules. A simple email-to-Slack router is much different than a multi-stage lead qualification agent. We provide a fixed price after a 30-minute discovery call.
- What happens if the AI misclassifies something important?
- The system has a fallback rule. Any request the AI classifies with a confidence score below an 85% threshold is automatically routed to a human for manual review. This creates a safe failure state and provides a feedback loop for improving the prompts. The goal is not 100% automation, but 100% safety for the most critical items that require human judgment.
- How is this different from using a help desk tool like Zendesk?
- Zendesk uses rigid, keyword-based rules that cannot understand intent. It can't distinguish an angry customer from a curious one if they both use the word 'billing'. Our system uses the Claude API to understand context, sentiment, and nuance. This results in far more accurate routing and fewer false-positive interruptions for your team.
- Do we need to pay for an expensive API key from OpenAI or Anthropic?
- For most triage workflows processing a few hundred items a day, the Claude API costs are minimal, often less than $50 per month. We help you set up your own Anthropic account so you have full control and visibility over usage. If you choose an optional maintenance plan, this API cost can be bundled into the flat monthly fee.
- What kind of team is this a bad fit for?
- This is a poor fit if your process is not standardized or if you handle fewer than 50 incoming requests per day. The AI needs consistent, repeated patterns to learn from. If every request is a unique edge case or the volume is too low, the cost of a custom build outweighs the time saved. We will tell you this during our initial call if we see a mismatch.
- Can we update the routing rules ourselves after the build?
- Yes. The core routing logic is a straightforward Python match statement in the source code we deliver. The runbook explains how to add or change a rule, for example, routing leads from a new campaign to a different salesperson. A developer with basic Python knowledge can make these changes in under 30 minutes and redeploy the function.
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