Model Development: Custom AI Models Optimized for Your Business Needs

We develop custom AI models by selecting, fine-tuning, and optimizing the best models for your specific use case, ensuring high performance and cost-effectiveness.

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What it is?

Model development is the process of selecting, training, and optimizing AI models to meet specific business needs. At Zangoh, we ensure that you get the best model by evaluating various open-source and closed-source options, fine-tuning them with the latest techniques, and setting up comprehensive benchmarks. Whether it's prompt engineering, fine-tuning, or advanced training like RLHF or DPO, we ensure that your models are performant and tailored to your goals.

Key Benefits

Comprehensive Model Selection

We benchmark several open-source and closed-source models to find the best one for your specific use case.

Advanced Prompt Engineering

By applying techniques like few-shot prompting and prompt tuning, we optimize model performance to meet your business objectives.

Fine-Tuning Expertise

Using advanced methods such as SFT, PEFT,LoRA, and QLoRA, we refine models to improve accuracy and domain alignment.

RAG and Beyond

We implement RAG (Retrieval-Augmented Generation) to enhance model performance with real-time, relevant data, further evolving models through RLHF and DPO when needed.

Custom Benchmarking

We establish leaderboards using generated evaluation data to rigorously assess model performance across various tuning stages.

Cost-Performance Optimization

After benchmarking, we optimize models for the best balance between cost efficiency and performance..

Our Process: Finding and Optimizing the Best Models for Your Business

Zangoh’s model development process is focused on delivering custom AI models that are highly optimized for your business requirements. Our approach ensures that each stage of model development is driven by performance and tailored to your use case.

Model Selection and Benchmarking: We evaluate various open-source and closed-source models, benchmarking them against both standard LLM benchmarks and custom-generated domain-specific benchmarks. This helps us identify the best model to take forward.

Prompt Engineering: We optimize models using prompt engineering techniques such as few-shot prompting and prompt tuning to boost performance without requiring large datasets.

RAG Setup: We implement Retrieval-Augmented Generation (RAG) to integrate real-time data, enhancing the model’s ability to provide accurate and relevant responses.

Advanced Training: For more evolved use cases, we employ advanced training techniques such as RLHF (Reinforcement Learning with Human Feedback) and DPO (Direct Preference Optimization) to improve model alignment with business goals.

Benchmarking and Leaderboards: We set up leaderboards using generated evaluation data, comparing models at various stages (basic, prompt-tuned, fine-tuned, RAG, FT+RAG) to find the best-performing one.

Cost-Performance Optimization: Once the best model is identified, we focus on balancing cost efficiency with performance to ensure that your AI systems are scalable and effective.

Frequently Asked Questions

How does Zangoh select the best model for my business needs?

We benchmark several open-source and closed-source models against standard benchmarks and custom-generated domain-specific data. This helps us identify the best-performing model for your specific use case.

What is prompt engineering, and how does it improve model performance?

Prompt engineering involves crafting and refining prompts to optimize the model’s output. We use techniques like few-shot prompting and prompt tuning to enhance performance without needing extensive training data

What fine-tuning techniques does Zangoh use?

We use state-of-the-art techniques such as SFT (Supervised Fine-Tuning), PEFT (Parameter-Efficient Fine-Tuning), LoRA (Low-Rank Adaptation), and QLoRA to fine-tune models for your specific domain and use case.

What is RAG, and how does it benefit my AI models?

RAG (Retrieval-Augmented Generation) integrates real-time data into the model, enabling it to provide more accurate and contextually relevant responses. This is particularly useful for applications that require up-to-date information.

What is RLHF, and how does it improve model performance?

RLHF (Reinforcement Learning with Human Feedback) uses human input to guide the model’s training, improving its ability to align with desired outcomes and business goals.

How does Zangoh ensure that models are cost-effective and scalable?

After benchmarking and fine-tuning, we optimize models to balance performance with cost, ensuring that your AI systems are scalable and efficient in the long term.

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+91-97525-99372
401 Atulya IT Park Indore MP India 452014