---
title: "Multimodal RAG stacks — niche opportunity inside AI & Machine Learning"
url: https://signals.gitdealflow.com/niche-down/ai-ml/multimodal-rag-stacks
description: "Text + image + table retrieval — the indexing layer that doesn't yet have a winner."
source: VC Deal Flow Signal
---
# Multimodal RAG stacks

> Text + image + table retrieval — the indexing layer that doesn't yet have a winner.

**Sector**: [AI & Machine Learning](https://signals.gitdealflow.com/niche-down/ai-ml)  
**Build cost**: One-quarter build  
**Deal velocity**: Steady — one deal per month

## Why now

GPT-4o and Claude vision are good at single-document Q&A but terrible at large corpus retrieval. The indexing layer for multi-modal corpora is unbuilt.

## What the signal looks like

Repos with PDF parsing benchmarks in the README, contributor list of OCR/CV engineers, and growing test fixture directories of real documents (insurance forms, lab reports, contracts).

## Public examples

*Public projects + categories only — we never name founders tracked inside the paid product.*

- ColPali-based document retrieval libraries
- LlamaParse-style PDF chunkers
- Vision-RAG benchmarks with reproducible scoring

## What this displaces

Hand-rolled OCR → text → embed pipelines that lose layout context.

## Our build-vs-invest call

Build vertical: a stack that wins on legal contracts beats a stack that's mediocre on everything. The defensible asset is the ingest pipeline plus the eval set on real documents.

## Frequently asked

### Doesn't every LLM vendor ship vision now?

They ship inference. They don't ship retrieval at scale. That's the gap.

### What's the signal that one stack is winning?

Same eval set scoring 30%+ better with the same model — the difference is the retrieval, not the model.

### What's the moat?

The eval set, then the ingest pipeline, then the API stickiness.

## Canonical

https://signals.gitdealflow.com/niche-down/ai-ml/multimodal-rag-stacks