What is a proprietary artificial intelligence model?
Proprietary artificial intelligence model A model that is trained or tuned with your own data to solve a specific problem in your business. Generic models like GPT, Claude, and Gemini have extensive knowledge, but they don't know your industry jargon, your organization's internal document format, or your specific data patterns. The dedicated model fills this gap.
Also, some organizations prefer to run the AI model on their own infrastructure due to data sensitivity, legal requirements, or the high costs of mass API usage. The dedicated model provides this possibility.
When do you need a dedicated model?
Our first question is always, do you really need a dedicated model? In many projects, it is sufficient and much cheaper to use generic models together with a proprietary knowledge base (RAG). The dedicated model is the right choice when:
The problem is quite specialized and general models are not accurate enough.
The output should have a very specific and fixed format, tone or structure.
The data is very sensitive and should not leave the organization.
The volume of usage is so high that the cost of a public API is not justified.
It is not a linguistic issue; Such as numerical prediction, image classification or anomaly detection.
Dedicated Netanik modeling services
Fine-tuning existing models
In fine-tuning, a ready-made and powerful model is retrained with a set of your own samples to specialize in a specific task; Like writing responses with a brand tone, classifying requests or extracting information from specific documents. This method gives a much more accurate result than the general model with less data and cost than building the model from scratch.
Model design from scratch
For issues like sales forecasting, fraud detection, image quality control on a production line or credit scoring, custom machine learning models are designed and trained with your historical data.
Performance optimization
A good model should be fast and inexpensive. With methods such as quantization, choosing the right size model and optimizing the execution infrastructure, the response time and hardware cost are reduced.
Deployment on your server
The model can be deployed on the organization's internal servers (on-premise) or your private cloud so that no data leaves the organization. A secure programming interface (API) is also provided for other organization systems to use the model.
Steps to build a dedicated artificial intelligence model
1. Accurate definition of the problem and success criteria
It is clear what exactly the model should do and by what criteria it is considered successful.
2. Data preparation
The training data is collected, cleaned, labeled and divided into training and evaluation sets. Data quality is the most important factor of model quality.
3. Training and evaluation
The model is trained and evaluated on never seen data to determine its true accuracy.
4. Deployment and monitoring
The model is deployed in the operational environment and its performance is continuously monitored; Because as the data changes over time, the accuracy of the model may decrease and retraining is required.
Exclusive model along with other AI solutions
The dedicated model can be the main engine of a Smart chatbot, one Autonomous agent or system data analysis be In many architectures, a combination of dedicated models for specialized tasks and generic models for general tasks provides the best balance between accuracy and cost.
Fine-tuning or RAG; Which one is right for you?
One of the most common questions in projects Proprietary artificial intelligence model It is whether the model should be trained or just connected to the organization's knowledge. The answer depends on the type of need:
RAG is appropriate when: The model must respond to information that changes frequently, such as prices, inventory, and documentation, and it is important to cite the source of the response.
Fine-tuning is suitable when: The model must learn a specific skill, format, or tone, such as writing a report with a fixed structure or accurately categorizing requests.
Combination of both: In many professional systems, the fine-tuned model is used for skill and the RAG for up-to-date knowledge.
open source models; Complete freedom and control
The rapid growth of open source language models has created a new possibility for organizations: to run powerful models on their own server, without dependence on external services and without sending data outside. Open source models can be fine-tuned, optimized and fully deployed in internal infrastructure. Community tools and documents Hugging Face It is the common standard of this field. The choice between the open source and commercial models depends on the required accuracy, privacy requirements and hardware budget, and is determined during the project analysis phase by testing on your real data.
Evaluation of proprietary artificial intelligence model
A model whose accuracy is not measured is not reliable. Each proprietary AI model is evaluated against clear criteria before deployment: accuracy on data not seen in training, comparison with a generic baseline model, checking for common errors, and testing in boundary situations. For language models, in addition to automated benchmarks, human evaluation by experts in your field is also performed. The evaluation results are reported transparently so that you know in what cases the model is reliable.
Maintaining and updating the model
The business world does not stand still; New products are added, customer behavior changes, and data is constantly changing. A model that is accurate today may become less accurate in a few months. Continuous performance monitoring and periodic retraining are part of the lifecycle of any dedicated AI model. Also, with the supply of newer basic models, it is possible to upgrade the model with a lower cost and more accuracy.
Frequently asked questions about the proprietary AI model
How much data is needed to train a dedicated model?
It depends on the method. For fine-tuning, several hundred to several thousand high-quality samples are usually enough; Building a model from scratch requires much more data.
Is our data used to train general models?
no Your data will only be used for your own proprietary model and will not leave the organization's infrastructure if deployed internally.
What hardware is needed to run the model on our own server?
It varies depending on the size of the model and the volume of use. Once the problem is defined, recommended hardware specifications and optimization options are provided.
Who owns the trained model?
The trained model, codes and documentation are fully delivered to you.
How long does it take to build a dedicated artificial intelligence model?
An initial feasibility test usually takes a few weeks. The time of the complete project depends on the volume and quality of the data, the training method and the deployment method.
Does the dedicated artificial intelligence model understand the Persian language well?
One of the advantages of fine-tuning is improving the performance of the model on the Persian language and specialized terms of your field; Especially when high-quality Persian educational data is available.