Ertas + OpenClaw for Customer Support

Customer support teams using OpenClaw for ticket triage, response drafting, and escalation management get mediocre results from generic cloud models. Fine-tuned local models learn your product, your taxonomy, and your tone — delivering dramatically better accuracy at zero per-interaction cost.

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.

But the gains plateau quickly with generic cloud models. The core problem is that 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 vs. what users expect it to do. A generic GPT-4o or Claude makes reasonable guesses based on general language understanding — but "reasonable guesses" translate to 70-75% classification accuracy, which means 1 in 4 tickets is 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. The time saved by automation is partially consumed by 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. For SaaS companies with thin margins, this cost often exceeds the labour savings from automation.

The Solution

Ertas solves both the accuracy and cost problems simultaneously. Fine-tune a model on your actual support ticket history — your taxonomy, your product terminology, your resolution patterns, your communication style. Deploy it locally via Ollama and connect OpenClaw to the local endpoint.

The accuracy improvement is dramatic and immediate. A model fine-tuned on 2,000 categorised tickets from your system learns the specific boundaries between your ticket categories — boundaries that are impossible to convey fully in a system prompt. Classification accuracy typically jumps from 70-75% (generic model) to 90-95% (fine-tuned model). Response quality improves because the model has seen hundreds of examples of good responses for each ticket type.

The cost drops to zero per interaction. All inference runs locally. Whether your team processes 50 tickets or 5,000 tickets per day, the compute cost is the same — the hardware you already own. This makes OpenClaw economically viable even for high-volume support operations where API costs would be prohibitive.

Key Features

Studio

Taxonomy-Specific Classification

Studio fine-tunes on your exact ticket taxonomy — categories, subcategories, priority rules, and escalation criteria that are specific to your product and support structure. The model learns boundaries between similar categories that generic models consistently confuse.

Studio

Response Template Training

Fine-tune on your best support responses — the tone, level of detail, troubleshooting steps, and resolution patterns that your team has developed over time. The model generates drafts that match your support style guide without lengthy system prompts.

Cloud

Multi-Product Support

Cloud enables deploying product-specific LoRA adapters on a shared base model. Companies with multiple products or brands get customised support AI for each — different taxonomies, different response styles, different escalation rules — from shared infrastructure.

Cloud

Quality Monitoring

Track classification accuracy, response acceptance rates, and escalation patterns over time. Identify categories where the model underperforms and add targeted training examples for the next fine-tuning iteration. Continuous improvement without continuous API spend.

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. This dataset is uploaded to Ertas Studio, where a Llama 3.3 8B model is fine-tuned with LoRA. The model achieves 94% classification accuracy (vs. 71% from prompt-engineered GPT-4o on the same taxonomy) and generates response drafts that agents accept without edits 62% of the time. Deployed on a Mac Mini M4 Pro (AU$2,800) running Ollama, 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 high-confidence Tier-1 issues) or queues it for agent review. First-response time drops from 2.4 hours to 8 minutes for auto-resolved tickets. Tier-1 resolution rate increases from 45% to 87%. The support team focuses on complex Tier-2 and Tier-3 issues that require human judgement. Monthly cost: AU$14.50 Ertas subscription + hardware amortisation — vs. an estimated AU$850/month in API costs for the same volume on GPT-4o.

Compliance & Security

Local inference means customer data (support conversations, account details, usage patterns) is processed on the company's own infrastructure. No customer data is transmitted to third-party AI providers. This satisfies SOC 2 requirements for data handling controls and simplifies the security review process for enterprise customers evaluating your support AI capabilities.