Introduction

RAG and AI Agents have emerged as the standard architecture for grounding Large Language Models (LLMs) in trusted enterprise data, and its integration into agentic AI systems is now commonplace.

However, the majority of AI applications deployed today share a critical flaw: they are essentially "text-only" engines trying to navigate a multimodal world.

Consider your organization’s most valuable intellectual property. Is it strictly found in plain text paragraphs? Likely not. The "source of truth" is often locked in the cell of a P&L spreadsheet, the trend line of a quarterly revenue chart, the visual flow of a technical diagram, or the spoken nuance of a customer support call.

When a standard text-only system encounters these formats, it typically fails. It either ignores them entirely, chunks them into gibberish, or hallucinates an answer because it lacks the visual or structural context to interpret the data correctly.

In this blog post, we will explore the critical importance of Multimodal data processing. We will examine why traditional text extraction fails for complex enterprise documents and how Vectara provides a turnkey, production-ready solution to ingest, index, and retrieve data across modalities - powering the next generation of RAG and AI Agent systems.

The Multimodal Reality of Enterprise Data

To understand why Multimodal RAG is necessary, we must first look at the limitations of text-based systems. Text-based RAG treats all information as a linear string of characters, which works fine for simple documents, but fails when tables or images are embedded in the document.

The "Table" Problem

In financial reports (for example SEC 10-K filings), manufacturing manifests (for example a bills of materials), or insurance policies, tables are not merely formatting - they include critical information that is required to answer many user queries, and without granular access to that information, the response would not be accurate.

The standard, "naive" approach to processing these documents typically involves serializing the entire file into a single, unstructured string of text. In this workflow, the intricate two-dimensional structure of a table - its spatial layout of rows and columns - is flattened into a one-dimensional linear stream of text. Consequently, the chunking algorithm blindly segments the text based on fixed character or token counts, oblivious to the fact that it is slicing through a structured table, ignoring the inherent meaning of rows and columns.

As an example, consider a company's Quarterly Earnings Report comparing revenue across different fiscal quarters.

Table 1: quarterly earnings report example

In a standard RAG pipeline, normal chunking might chop this document into arbitrary token-length chunks. The result?

• Chunk 1 contains the headers and perhaps the first row's data: “Metric Q3 2023 (Actual) Q3 2024 (Projected) YoY Growth Cloud Revenue $12.5M $14.2M +13.6%”
• Chunk 2 contains the second and third rows: “Hardware Sales $8.1M $7.5M -7.4%...”

And so on.

When a user asks, "What were the projected Hardware Sales for Q3 2024?", the system retrieves Chunk 2. However, the LLM only sees the raw text sequence. It has lost the header row, and no longer knows which dollar figure represents 2023 (Actual) and which represents 2024 (Projected). The semantic link is severed, leading the model to 'cross-contaminate' data. Without the header context, the LLM might confidently report the actual revenue as the projected figure, creating a high-stakes hallucination that looks factually correct but is contextually wrong

The "Image" Problem

While tables challenge retrieval due to their grid-like structure, diagrams and charts present an even deeper "modality gap." In technical documentation, blueprints, user manuals and scientific papers, visual elements act as dense information containers where the meaning is derived from spatial relationships, not just the text they contain. A flowchart in an engineering manual or a network topology diagram relies entirely on the directional flow of arrows, the nesting of boxes, and the color-coding of lines to convey logic.

The standard text extraction pipeline treats these rich visual assets as black boxes and traditional Optical Character Recognition (OCR) is 'spatially blind' - it lifts text out of an image but discards the visual syntax holding that text together.

As an example, consider a Troubleshooting Flowchart for an industrial cooling system, shown in figure 1:

Figure 1: example diagram of a flowchart in an industrial cooling system.

When a text-only parser processes this flowchart image, it often linearizes the text based on vertical position (top-to-bottom). The resulting chunk reads: "A. Is temperature above 100°C? Yes B. Engage Emergency Valve. No. C. Continue Monitoring." The crucial conditional logic - the "If/then" branching represented by the arrows - is lost.

One common approach in open source RAG implementations is to leverage a Vision-Language Models (VLM) to describe the visual relationships in natural language before indexing. You send the image to the VLM and ask it to provide a coherent description, which may look like this: 'A decision flowchart where the system triggers an emergency valve only if the temperature exceeds 100°C'. Then, you index this description instead of the image into your RAG pipeline.

While this is certainly an improvement, and it fits nicely into a text-only setup, it creates a “description bottleneck”: the AI’s intelligence is now capped by the quality of the initial summary. This 'lossy' compression means the agent is only as smart as the person who wrote the initial prompt for the VLM. If the summary ignores a specific serial number or a tiny junction in a circuit board, that information is effectively erased from the corporate memory

The "Temporal" Problem: Audio and Video

If tables and images represent spatial gaps in data processing, audio and video represent a temporal and tonal gap. Modern enterprises are drowning in multimedia data - Zoom meeting recordings, customer support calls, instructional video walkthroughs, and earnings call webcasts. In a standard text-based workflow, these assets are typically handled via transcription (speech-to-text), which flattens a rich, time-based medium into a static script.

While transcription captures what was said, it frequently fails to capture how it was said, who said it, and what was happening while they said it. This results in a few significant gaps:

1. The Loss of Speaker Diarization and Attribution: If a transcript says "I disagree with that risk assessment. We should proceed with plan A", you want to know if this was the Head of Legal (implying a cleared compliance check) or the Head of Sales (implying aggressive expansion).
2. The Loss of Visual Context in Video: if a recorded training session for assembly line workers. The instructor says: "Never touch this lever while the light is flashing red, but you can rotate that dial", the transcript loses the information about which lever was pointed at during the video.
3. The Loss of Tone and Sentiment: A customer support transcript might read: "That’s just great." Spoken with rising pitch and fast tempo, this represents genuine satisfaction. However, with a flat pitch and a sigh - it usually means frustration and/or sarcasm.

The limitations of text-only RAG are not merely edge cases; they represent a fundamental disconnect between how humans create information and how machines currently consume it.

To build AI systems that are truly reliable and autonomous, we must move beyond simple text extraction and embrace a multimodal architecture - one capable of seeing, hearing, and understanding data in its native format, preserving the structural and semantic integrity that text alone cannot capture.

Bridging the Modality Gap with Vectara

The power of Vectara’s multimodal architecture lies in its ability to bridge the gap between complex raw data and the reasoning capabilities of Large Language Models (LLMs) and Vision-Language Models (VLMs).

Multimodal Ingestion

This starts at the ingestion layer, where the platform handles data through two complementary pathways.

• Vectara provides Intelligent Table Extraction: the File upload API natively identifies tables in files, resulting in an accurate structured representation of tables that keeps data bindings intact, including titles, headers, and specific row labels.
• For more specialized data, such as files with images or time-based media like audio and video, the open-source vectara-ingest framework acts as a high-performance pre-processing gateway, handling the heavy lifting of complex images, OCR and transcription, converting complex multimodal data into a "Vectara-ready" state while maintaining temporal and spatial markers.

Once data is properly ingested, Vectara stores these tables and images natively in the platform and can then present this multimodal data in its raw form to the generative LLM.

For example, when an AI agent queries for "What was the Q3 revenue?" - when the retrieved results include a table or image, Vectara doesn't just send a text snippet; it provides the LLM (or VLM) with the raw table or image. This allows the model to 'see' the information in its original context, ensuring that spatial reasoning and structural logic remain intact during the final generation step, resulting in highly accurate responses.

Multimodal Data with Vectara Agents

Real-world AI workflows require agents to handle data that changes mid-conversation. In a standard RAG setup, an agent is limited to what was indexed yesterday. With Vectara Artifacts, your agent gains a dynamic workspace: it can "see," "analyze," and "persist" a variety of data types across multiple turns of a conversation.

How it works in practice:

• On-the-fly Uploads: A user can upload a screenshot of an error message or a fresh sales CSV during a live session. The agent immediately indexes this "Artifact" and uses it to provide context-aware answers.
• Cross-Modality Synthesis: An agent can cross-reference a technical diagram in a PNG artifact with the specifications found in a 1,000-page manual already in your corpus.
• Contextual Persistence: By decoupling file storage from the immediate prompt window, the agent can "remember" and refer back to these files throughout a complex, multi-step task without hitting token limits.

This allows developers to build agents that don't just find information - they can synthesize it across the entire visual and textual landscape of your enterprise.

By treating these files as transient, session-based knowledge, Artifacts allow your agents to act more like human analysts - pulling a file off the 'desk', examining it, and then applying those findings to the broader organizational knowledge base.

For more details, check out this jupyter notebook for a step-by-step example of using artifacts with Vectara, where the agent dynamically uploads and analyzes a sales report with multimodal data.

Conclusion

Building a production-grade multimodal RAG or agentic pipelines is far from simple, and requires significant investment, such as integrating vision models, writing complex processing code for handling images, tables and other multimodal data. All of this is undifferentiated heavy lifting.

Vectara offers a compelling alternative: an Agentic Platform where multimodal ingestion is a standard feature, not a custom engineering project. Whether you deploy as SaaS, within your own VPC, or on-premises, Vectara provides the agentic capabilities to turn "text-blind" applications into visually and contextually aware systems.

If you are building an enterprise AI application that deals with PDF reports, financial data, or technical manuals, or other multi-modal data, contact us for a demo or sign up for your Vectara account today to experience these features for yourself.