Ertas for AI Automation Agencies

AI automation agencies can cut costs by 90%+ by switching from per-client API pass-through to fine-tuned local models. Ertas enables agencies to run a single base model with per-client LoRA adapters, replacing unpredictable per-token API bills with fixed infrastructure costs — while delivering better results and true data sovereignty.

The Challenge

AI automation agencies building chatbots, voice agents, and workflow automation for clients face a brutal margin problem. Every client engagement means another OpenAI or Anthropic API key, another line item of variable per-token spend that scales with usage — not with value delivered. An agency managing 10–20+ clients on GPT-4 or Claude can easily burn through AU$3,000–5,000 per month in pure API pass-through costs, and those costs are completely unpredictable. A single client's spike in usage can wipe out the margin on an entire account. Tools like Make.com, n8n, Voiceflow, and Stammer.ai make it easy to wire up AI-powered workflows, but they all funnel inference through the same commercial APIs, leaving agencies with zero control over their largest variable cost.

Beyond cost, the differentiation problem is even more existential. When every agency is reselling the same GPT-4 or Claude API behind a slightly different prompt template, there is no moat. Clients eventually realise they can cut out the middleman and call the API themselves. Meanwhile, client data — customer conversations, proprietary business context, sensitive operational details — flows through third-party infrastructure with every API call. Enterprise clients increasingly push back on this, demanding to know where their data is processed and stored. Agencies that cannot answer "your data never leaves our infrastructure" are losing deals to competitors who can.

The Solution

Ertas transforms the agency model from API reseller to custom AI provider. Instead of maintaining separate API subscriptions for each client, agencies deploy a single performant base model (7B–14B parameters) and attach per-client LoRA adapters fine-tuned on each client's specific data — their tone of voice, product catalogue, FAQ corpus, and conversation history. The result is a bespoke AI experience for every client, running on infrastructure the agency controls, with inference costs that are fixed and predictable. A single Mac Studio or modest GPU server can serve dozens of clients simultaneously through Ollama, replacing thousands of dollars in monthly API spend with a one-time hardware investment.

The white-label delivery model becomes trivially simple. Each client gets their own adapter loaded at inference time, with Vault ensuring strict data isolation between tenants. Client data never leaves the agency's infrastructure — or the client's own infrastructure if they require on-premise deployment. Fine-tuned models outperform generic foundation models on domain-specific tasks because they have been trained on the actual data that matters, not prompted to approximate it. Agencies can iterate on adapters in Studio without touching client-facing systems, A/B test new adapter versions, and roll back instantly if quality dips. The variable API cost line disappears from the P&L entirely, replaced by a fixed infrastructure budget that improves margin on every new client added.

Key Features

Studio

Per-Client Fine-Tuning

Studio lets agencies create and manage LoRA adapters for each client from a shared base model. Upload a client's conversation logs, product data, or knowledge base, configure a fine-tuning run, and produce an adapter that captures that client's specific domain and tone — all without writing training scripts or managing GPU infrastructure directly.

Hub

Base Model Selection

Hub provides access to hundreds of open-weight models optimised for different tasks — conversational, instructional, multilingual, code-capable. Agencies can benchmark base models against client requirements, compare parameter sizes and quantisation levels, and select the right foundation for each engagement tier.

Cloud

Multi-Tenant Deployment

Cloud enables agencies to deploy a single base model with dynamically loaded per-client adapters, handling routing and adapter switching at inference time. Scale from 5 to 50 clients without proportional infrastructure growth — each new client is just another lightweight LoRA adapter, not another model instance.

Vault

Client Data Isolation

Vault enforces strict tenant boundaries across every client's training data, adapter weights, and inference logs. Each client's data is encrypted at rest and in transit, access-controlled by API key, and completely invisible to other tenants — meeting the data sovereignty requirements that enterprise clients demand before signing.

Example Workflow

An AI automation agency in Melbourne manages chatbot and voice agent deployments for 15 small-to-medium business clients across real estate, dental, and trades industries. Their current setup routes all inference through GPT-4 via Make.com and Voiceflow integrations, costing AU$4,200 per month in API fees — with three clients alone accounting for AU$1,800 due to high conversation volumes. The agency decides to migrate to Ertas. They start with their highest-spend client, a real estate agency whose chatbot handles 12,000 conversations per month about property listings, inspection bookings, and pre-qualification questions. The agency exports 6 months of conversation logs (45,000 message pairs) from their existing system and uploads them to Vault as a JSONL training set. In Studio, they select a Qwen 2.5 7B base model from Hub, configure a LoRA fine-tuning run with rank 16 and 3 epochs, and launch training on Cloud. The resulting adapter scores 92% on a held-out test set for response accuracy — compared to 78% from their carefully prompt-engineered GPT-4 setup. They export the adapter as GGUF and deploy it alongside Ollama on a Mac Mini M4 Pro (AU$2,800 one-time cost) sitting in their office. After migrating all 15 clients to individual LoRA adapters on the same base model, their monthly AI inference cost drops to AU$14.50 for Ertas plus electricity and internet — a 99.6% reduction. The hardware pays for itself in 3 weeks.

Compliance & Security

Local deployment means client data never leaves the agency's infrastructure or the client's own premises. This satisfies data sovereignty requirements for enterprise and government clients under the Australian Privacy Act and GDPR. Agencies can provide written guarantees that no client data is transmitted to third-party AI providers, a requirement increasingly included in enterprise procurement RFPs.