Ertas for Customer Support

The Challenge

Customer support teams are drowning in ticket volume while struggling to maintain quality. Generic AI chatbots promise relief but consistently disappoint — they hallucinate product features that don't exist, give confidently wrong troubleshooting steps, and escalate to human agents at rates so high that the automation barely moves the needle. The root cause is simple: these models have never seen your product documentation, your internal runbooks, or the thousands of nuanced edge cases your senior agents have learned to handle.

The problem deepens as products grow more complex. Every new feature, pricing change, or integration adds another surface area where a generic model will fail. RAG pipelines help but introduce their own reliability issues — chunking artifacts, retrieval misses, and context window overflows that degrade answer quality unpredictably. Support leaders need AI that genuinely understands their product at the depth of a trained agent, not a system that pattern-matches against retrieved snippets and hopes for the best.

The Solution

Ertas transforms your best support knowledge into a purpose-built AI model. With Studio, support operations teams can fine-tune models directly on resolved ticket histories, internal knowledge bases, and curated Q&A pairs exported as JSONL datasets. The resulting model doesn't just retrieve relevant documentation — it has internalized your product's logic, terminology, and troubleshooting decision trees, producing responses that match the quality of your top-tier human agents.

Ertas Cloud deploys these fine-tuned support models as always-on inference endpoints that integrate directly with your helpdesk platform via REST API. Because the models are compact (7B-14B parameters) and optimized with GGUF quantization, response times stay under 500ms even at peak ticket volume. As your product evolves, Studio makes it straightforward to retrain on fresh ticket data and roll out updated models with zero-downtime deployments — keeping your AI agent's knowledge as current as your latest release notes.

Key Features

Example Workflow

A SaaS company's support team exports 100,000 resolved Zendesk tickets as a JSONL dataset, filtering for tickets with high CSAT scores and verified resolutions. The dataset is uploaded to Vault, which scans for and redacts customer PII. In Studio, the team selects an instruction-tuned Llama 3 8B model from Hub and runs a LoRA fine-tuning job focused on the company's troubleshooting and account management domains. After validation against a held-out set of 5,000 tickets, the model achieves 87% auto-resolution accuracy — up from 34% with their previous RAG-based chatbot. The model is deployed via Cloud as a REST endpoint integrated with their Zendesk webhook, handling tier-1 tickets autonomously and escalating only when confidence scores drop below threshold. Monthly support costs decrease by 35% while average first-response time drops from 4 hours to under 10 seconds.