Building Your Custom AI Model: The Step-by-step Process
From Data to Deployment—How We Build AI That You Own.
The Step-by-step Process
01
Data Collection and Cleaning
- We begin by working closely with your enterprise to gather the data needed to train your custom AI model.
- In most cases, enterprise data is unstructured and may require extensive cleaning.
- Our team specializes in preparing datasets that are accurate, consistent, and relevant for model training.
02
Data Augmentation
- For organizations with limited data, we use data augmentation techniques to generate synthetic data that enhances the volume and diversity of the training set.
- This helps in avoiding overfitting and ensures the model generalizes well, even on limited datasets.
03Model Selection
Model Selection
& Fine-Tuning
- Using cutting-edge techniques like LoRA and QLoRA, we fine-tune a pre-trained open-source model that fits your use case.
- Fine-tuning is a critical step where we ensure that the model aligns with your specific tasks, delivering accurate, domain-specific outputs.
04
Reinforcement Learning with Human Feedback
- Our specialty lies in RLHF, where we continuously train the model using feedback from your team.
- This allows the AI to align more closely with your business processes, enhancing accuracy and reducing errors.
05
Evaluation & Deployment
- Once trained, we rigorously evaluate the model using both automated tests and expert reviews.
- Only after ensuring that it meets all of your business requirements do we move to deployment.
- The model is then deployed securely on your preferred infrastructure—on-premise or via a virtual private cloud.
Why This Approach Works
Our comprehensive approach ensures that your AI is built on a foundation of clean, enriched data, and is fine-tuned for your specific needs.
This ensures that the model doesn’t just solve generic problems but addresses the challenges that matter most to your business. And the best part—you own it.
