Not a generic chatbot: an enterprise AI model adapted with your ERP data, your documents and your approved outputs, running on your own servers.
Custom-trained AI is a general-purpose language model adapted to your company's terminology, processes, documents and past decisions. The model knows what concepts like “customer account,” “offset entry” or “limit allocation” mean in your organization, and grounds its answers in your own sources instead of guessing.
ErucaAI does this in two layers. First, it connects your data and builds a semantic index inside your organization, so the model finds the right document and record for every question. Then it turns the AI outputs your users approve into training data and updates the model regularly through fine-tuning. Before a new version goes live, it is compared with the previous version in shadow mode, on real work.
The result: an AI that never leaves your data center, has predictable costs, and knows your business a little better every month.
ERP tables, file servers, email archives and the ErucaStudio knowledge library are crawled and indexed on-premise.
For every question, the model finds the relevant documents and records, grounds its answer in those sources and links to them.
Open-source language models are retrained on-premise with your approved examples; the model weights stay with you.
Every answer a user corrects or approves becomes labeled data for the next training run.
Every model version is on record; a new version is measured side by side with the old one on the same work, and rollback is one click.
Outputs such as entries, forms or summaries are checked against your rules; output that breaks a rule never reaches the user.
Together we choose the open model, such as Llama, Qwen or Mistral, that fits your workload and hardware.
Accuracy, response time, cost and user satisfaction are tracked per task.
Every step ends with a measurement; you move to the next step only when the data justifies it.
Credit file summaries, regulatory questions and customer correspondence, without data leaving your organization.
Q&A and summaries over technical documents, maintenance records and quality reports.
Recommendations and classification based on the product catalog, order history and customer requests.
An internal knowledge assistant for regulations, procedures and archive documents.
General tools answer with what they learned from the internet, and your data often goes to the vendor's servers. A custom-trained model is adapted to your data, runs on your own servers, answers only from records the user is authorized to see, and shows its sources.
You don't need a large dataset to start. Semantic search works with your existing documents from day one; fine-tuning usually needs a few hundred to a few thousand approved examples, which accumulate naturally during daily use.
The model weights, training data and indexes sit on your servers and belong to you. You can keep using the model even after the contract ends.
No. ErucaAI starts on CPU on an ordinary server with 32–64 GB of memory. A GPU investment is decided only when shadow measurements justify it.
Connecting data, building the first model and shadow measurement for a single business problem usually take 4–8 weeks. At the end of the pilot, you decide with accuracy and cost figures in hand.
Pick a real business problem; in a short pilot we connect your data, measure in shadow mode and review the results together.
Describe a real business problem; we'll define the pilot scope together.
Our team will get in touch with you shortly.