Ertas + OpenClaw for Customer Support

The Challenge

OpenClaw is a natural fit for customer support automation: it can monitor support channels (email, Slack, messaging platforms), classify incoming tickets by category and urgency, draft responses, escalate complex issues, and generate shift handoff summaries. Support teams that deploy it see immediate productivity gains.

Those gains plateau quickly with generic cloud models, because customer support is deeply domain-specific. The difference between a "billing inquiry" and a "subscription management request" depends on your product's specific billing architecture. The difference between a "bug report" and a "feature request" depends on what your product actually does and what users expect it to do. A generic GPT-4o or Claude makes reasonable guesses based on general language understanding, and reasonable guesses leave a meaningful share of tickets miscategorised.

Miscategorised tickets cascade into downstream problems: wrong team assignment, incorrect priority, inappropriate auto-responses, and frustrated customers who have to repeat their issue to the correct team. Part of the time saved by automation goes into fixing misrouted tickets.

The cost problem compounds the accuracy problem. Every ticket OpenClaw processes generates API tokens: the incoming message, the classification prompt, the response generation, the escalation check. A support team processing 200 tickets per day generates significant API spend, and for SaaS companies with thin margins that spend can eat deep into the labour savings from automation.

The Solution

Ertas addresses the accuracy and cost problems together. Fine-tune a model in Ertas Studio on your actual support ticket history: your taxonomy, your product terminology, your resolution patterns, your communication style. Export it from Hub as GGUF, run it locally with Ollama, and point OpenClaw at the local endpoint.

A model fine-tuned on a few thousand categorised tickets from your system learns the specific boundaries between your ticket categories, boundaries that are hard to convey fully in a system prompt. Response drafts improve because the model has seen many examples of good responses for each ticket type. Measure the gain on your own data: hold back a set of recently categorised tickets, compare the fine-tuned model against your current setup, and look at where each one goes wrong.

Inference runs on hardware you control, so there is no per-token API bill. The running cost is the hardware you size for your ticket volume, plus your Ertas Studio plan for training. That makes OpenClaw economically workable for high-volume support operations where API costs would be prohibitive.

Key Features

Example Workflow

A B2B SaaS company with 3,000 active customers deploys OpenClaw to augment their 8-person support team. The team currently handles 180 tickets per day across email and Slack, with an average first-response time of 2.4 hours and a Tier-1 resolution rate of 45%. The company exports 15,000 resolved tickets from the past 12 months, each with category labels, priority assignments and the resolution messages that closed them, removes customer names and contact details, and uploads the dataset to Ertas Studio, where Llama 3.1 8B is fine-tuned with LoRA. The team holds back 1,000 recent tickets and compares the fine-tuned model's classifications and drafts against prompt-engineered GPT-4o on the same taxonomy, reviewing the categories where each model goes wrong. The model is exported as GGUF and runs on a Mac Mini M4 Pro with Ollama, and the OpenClaw agent monitors both email and Slack support channels. It classifies every incoming ticket, assigns priority, drafts a response, and either sends it automatically (for categories the team has signed off for automation) or queues it for agent review. The support team tracks first-response time and Tier-1 resolution against its baseline and spends more of its time on complex Tier-2 and Tier-3 issues that require human judgement. The monthly running cost is the Ertas Studio plan and the hardware, with no per-token API bill as volume grows.