Frontier Models vs Fine Tuning vs Distillation vs RAG: Which AI Architecture Wins for Marketing Mix Modeling (MMM)?

It is not about the model architecture or model alone, it is all about the data and domain knowledge

AI adoption in Marketing Mix Modeling (MMM) is accelerating rapidly.

But much of the discussion remains superficial and lacks technical depth.

As a former NLP engineer and as a company that began applying AI to MMM as early as 2024 through MMMGPT, I thought I will unpack the different AI architectures and explain why we believe RAG is the most suitable approach for MMM (at least for now).

Frontier Models

A frontier model is a large foundational model trained from scratch on enormous amounts of data and compute.

Examples - GPT, Claude, Gemini etc.

Training one requires:

• Massive datasets

• Huge GPU clusters

• Hundreds of millions (sometimes billions) of dollars

For a niche domain like MMM, building a frontier model makes little sense.

MMM knowledge is relatively small compared to the internet. You do not need a trillion-parameter model to understand adstock, saturation, contribution analysis and budget optimization.

One needs depth, not scale.

Fine-Tuning

Fine-tuning takes an existing foundation model and further trains it on domain specific data.

The flow is like:

General LLM -> MMM Training Data -> MMM Fine Tuned Model

This changes the model's weights and can improve domain understanding.

However, MMM is not merely a collection of concepts and examples.

Much of the value resides in:

• Client decks

• Consulting notes

• Historical projects

• Business context

• Organization specific methodologies

Fine-tuning struggles when knowledge evolves. Every new project, spend pattern or methodology may require retraining, making it difficult to maintain.

Distillation

Distillation trains a smaller model (student) to imitate a larger expert model (teacher).

The Flow is :

Expert Model -> Generates Knowledge -> Student Model Learns

This helps reduce cost and latency.

However, a distilled model only knows what existed at training time. It cannot automatically access your latest MMM projects, new learnings or evolving methodologies.

The MMM Reality: Why Aryma Labs believes in RAG architecture

At Aryma Labs, our architecture is:

LLM -> RAG Layer -> Proprietary MMM Repository -> MMM Applications / Products

- The LLM provides language and reasoning.

- The RAG layer provides retrieval.

- The repository provides domain expertise.

Our repository contains:

• MMM consulting notes

• Historical MMM projects

• Spend and effect-share patterns

• Optimization studies

• Validation frameworks

• Experimentation and causality learnings

The model does not need to memorize this knowledge. It simply retrieves the right information at the right time.

The future moat in MMM AI will not be who trained the biggest model.

It will be who built the richest and trustworthy domain memory.

When people ask with surprise what model powers our products like Aryma Deck, Singularity Nebula etc.

Our answer is:

It is not the model. It is the data and domain knowledge 😎

Check out our website here - https://www.aryma.ai/

---

Thanks for reading.

For help with MMM, Causal Marketing Experiments and Experimentation, get in touch with us.

We also build some pretty cool AI products to aid Marketing Measurements. Check out our products page to know more - https://www.aryma.ai/