# Multimodal RAG Application | Super Cat

> Retrieval over source material that is part text, part table and part image — where naive chunking quietly returns answers that are confident and wrong.

Source: https://www.super-cat.tech/portfolio/multimodal-rag  
Last modified: 2026-10-22

[← All case studies](https://www.super-cat.tech/portfolio)246 hours · sustained engagement

# Multimodal RAG Application

Retrieval over source material that is part text, part table and part image — where naive chunking quietly returns answers that are confident and wrong.

## The problem

A retrieval application over multimodal data, where the source material is part text, part table and part image rather than the clean prose most RAG tutorials assume.

## What we built

Each format parsed on its own terms before anything reaches the vector store, rather than flattening everything to text and hoping the embedding model sorts it out. 246 hours of sustained engineering across the ingestion, retrieval and evaluation layers.

Before: Tables split mid-row; Numbers hidden in images; Confident, wrong answers. After: 246 hours · sustained engagement.Before

- Tables split mid-row

- Numbers hidden in images

- Confident, wrong answers

After
246 hours · sustained engagement

Stack
RAG Multimodal Machine Learning Python

## Why it holds up

That mix of formats is where naive retrieval quietly fails: the chunker splits a table down the middle, the image carries the number nobody indexed, and the answer comes back confident and wrong. Nothing errors, nothing alerts — you simply get wrong answers delivered with total fluency, which is the worst failure mode a retrieval system has.

Related service: [Enterprise RAG in Hong Kong](https://www.super-cat.tech/hong-kong-rag)

## More case studies

Big 4 client · fully on-premise

### Enterprise RAG Chatbot for a Big 4 Accounting Firm

Multimodal retrieval over a Big 4 firm’s full compliance library, tuned for citation instead of summary — every answer arrives with its source paragraph attached.

- Juniors reading thousands of pages per question

- Seniors acting as search engines

Read the case → 200k calls/month · 67% booking rate vs 77% human

### 24/7 AI Agent Call Center: 200k Calls a Month Across 60 US HVAC Brands

Voice agents orchestrating 200,000 calls a month across 60 US HVAC brands for a 5,000-person enterprise, booking 67% of calls against 77% for human agents — within 10 points of a trained team, around the clock. Built as a product, an AI front desk that answers, books and routes, not a pilot.

- Night and weekend calls unanswered

- Staffing every shift hard to justify

Read the case → US$1M collected in the first 3 months

### AI Payment Collection and Reminders

The companion product to the AI front desk: it chases unpaid invoices with polite, timed reminders and a payment link, and stops the moment the customer pays or replies. It collected over US$1 million for the client in its first three months.

- Chasing payment is awkward and late

- Billable time lost to collections

Read the case →

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## Talk to the engineer who will build it.

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