AI Support Agents That Actually Know Your Product
Ertas Studio lets customer support teams fine-tune AI models on real ticket history and product knowledge — creating support bots and agent-assist tools that resolve issues accurately without per-ticket API costs.
The Challenges You Face
Generic Chatbots Give Generic Answers
Off-the-shelf AI chatbots do not know your product's specific features, known issues, or resolution procedures. They provide plausible-sounding but wrong answers that frustrate customers and create more work for human agents who have to clean up the confusion.
API Costs Scale with Ticket Volume
Every AI-assisted ticket interaction costs money when powered by commercial LLM APIs. As ticket volume grows — especially during product launches or outages when support demand spikes — AI costs spike proportionally, precisely when budgets are most strained.
Knowledge Bases Are Hard to Keep in Sync with AI
Your product changes constantly — new features, updated procedures, deprecated workflows. Keeping a prompt-engineered AI system synchronized with evolving product knowledge requires constant maintenance that nobody has bandwidth for.
Tone and Escalation Policies Are Company-Specific
Every support organization has its own communication style, escalation triggers, and resolution procedures. Generic AI cannot learn these nuances through prompts alone, leading to responses that feel off-brand or that fail to escalate when they should.
How Ertas Solves This
Ertas Studio lets you fine-tune a model on your actual support ticket history — the real questions customers ask, the real answers that resolved their issues, and the real tone your best agents use. The result is an AI that understands your product's specifics, follows your escalation procedures, and communicates in your brand's voice.
Training is visual and straightforward. Export resolved tickets as question-answer pairs, upload the JSONL to Studio, and click train. When your product updates, add the new resolution examples and retrain — the model stays current without elaborate knowledge-base synchronization.
The trained model exports as a GGUF file for self-hosted deployment. Whether it powers a customer-facing chatbot or an agent-assist tool that suggests draft responses, every interaction runs at the cost of compute — not per-token API fees. Your Black Friday support surge does not come with a surprise AI bill.
Key Features for Customer Support Teams
Ticket History Training
Fine-tune on your actual resolved tickets. The model learns from your best resolutions — correct troubleshooting steps, appropriate tone, proper escalation decisions — not from generic internet text.
Easy Model Updates
When your product changes, add new resolution examples and retrain. Studio's visual workflow makes model updates as routine as updating a knowledge base — no ML engineering required.
Volume-Proof Deployment
Self-hosted GGUF models handle any ticket volume at fixed infrastructure costs. Support surges, product launches, and seasonal spikes do not increase your AI spend.
Agent-Assist and Bot Modes
Deploy the same fine-tuned model as a customer-facing chatbot that handles routine questions or as an agent-assist tool that drafts responses for human review — flexible deployment for different confidence levels.
Why It Works
- Support teams using fine-tuned models report that AI-suggested responses require minimal editing before sending, compared to generic LLM outputs that require substantial rewriting to match product specifics and brand tone.
- Self-hosted models eliminate the cost-per-interaction economics that make AI-assisted support unviable during high-volume periods — support quality remains consistent regardless of ticket volume.
- Fine-tuned models trained on resolved tickets correctly identify escalation-worthy issues based on the same patterns experienced agents recognize, reducing both over-escalation and missed critical issues.
- Monthly model retraining with new resolution examples keeps the AI current with product changes — a process that takes less than an hour including data preparation and training.
- Teams have deployed fine-tuned support models that resolve routine inquiries without human involvement, freeing agents to focus on complex cases that genuinely require human judgment.
Example Workflow
A SaaS company's support team handles 2,000 tickets per month. The support manager exports 5,000 resolved tickets from Zendesk as question-resolution pairs, removes any sensitive customer data, and formats them as JSONL. She uploads the file to Ertas Studio, selects a 7B instruction model, and starts training.
Twenty-five minutes later, she tests the model with recent ticket examples in the playground. The model correctly troubleshoots the top 10 most common issues using the exact procedures her team follows. She exports the GGUF and deploys it behind a simple API on the company's existing server.
The AI now powers two workflows: a customer-facing chat widget that handles routine questions instantly, and an agent-assist sidebar that drafts response suggestions for complex tickets. When the engineering team ships a major feature update, the support manager adds 100 new resolution examples and retrains — the model stays current without any prompt engineering or knowledge-base rewiring.
Ship AI that runs on your users' devices.
Early bird pricing starts at $14.50/mo — locked in for life. Plans for builders and agencies.