Resilience Solutions
Artificial IntelligenceExplore

Multimodal AI

Models that process and relate multiple input types — text, image, audio, video — within a single system.

How a question reaches a governed answer

Knowledge, retrieval, model, reasoning, action — with the user’s permissions evaluated before retrieval, not applied to the answer afterwards.

What it is

Understanding Multimodal AI

Multimodal models accept and reason across more than one input type. Practically, this means a system can read a document's layout as well as its text, interpret a photograph alongside a description, or process speech and text together.

Why it matters

Many enterprise processes are multimodal in nature — an inspection with photographs, a claim with documents and images, a technical drawing with annotations. Handling them in one system removes the pipeline of separate single-purpose tools.

Problems it addresses

  • “Our document processing loses information carried by layout.”
  • “Inspection and claims processes involve photographs a text system cannot use.”
  • “We run separate tools for text, image and speech.”

Capabilities

  • Document understanding

    Layout-aware extraction preserving table and form structure.

  • Visual inspection support

    Image analysis with human review on low-confidence results.

  • Speech processing

    Transcription and analysis of recorded interactions.

Use cases

  • Claims processing

    Documents and photographs assessed together.

  • Quality inspection

    Visual defect identification with human confirmation.

  • Complex document extraction

    Forms and tables where layout carries meaning.

Before you commit

Prerequisites, governance and risks

The questions that determine whether this will work in your environment, rather than in a demonstration.

What has to be true first

  • Representative evaluation data across every modality
  • Human review path for low-confidence output
  • Clarity on data protection for images and audio

Governance

Images and audio frequently contain personal data that is harder to identify and redact than text. Data protection assessment should precede deployment.

Risks

  • Higher inference cost than text-only models
  • Evaluation complexity across modalities
  • Privacy exposure in image and audio data

Explore solutions using Multimodal AI

Open the guided finder with this technology already selected, and see which capabilities apply to your goal, sector and environment — with the reasoning shown.

Start from a goal