Multimodal AI

Multimodal AI Development Services

Build AI systems that see, read, listen, and reason across modalities simultaneously. AINinza develops multimodal applications that process text, images, audio, and video together — unlocking insights that single-mode AI cannot reach.

Text + Image Understanding
Build systems that analyse documents containing both text and visuals — charts, diagrams, screenshots — and answer questions that require understanding both modalities together.
Video Analysis
Extract insights from video content: scene detection, action recognition, object tracking, and automated summarisation for surveillance, media, and training content.
Audio Transcription & Analysis
Transcribe and analyse audio content with speaker diarisation, sentiment detection, and topic extraction — from meeting recordings to call centre audio at scale.
Cross-Modal Search
Search across your entire content library using any modality. Find images using text descriptions, locate video segments from spoken keywords, or match documents to visual references.
Document Understanding
Process complex documents that combine text, tables, charts, and images. Extract structured data from annual reports, research papers, and technical manuals that stump traditional OCR.
Content Generation
Generate text from images, captions from video, image descriptions for accessibility, and multimodal reports that combine data visualisations with narrative insights.
How It Works

From Multi-Format Data to Unified Intelligence

AINinza builds multimodal AI systems through a structured process that handles the complexity of multiple data types while delivering unified, actionable outputs.

1

Use Case & Data Audit

Identify which modalities matter and assess your data landscape across text, image, audio, and video

2

Model Selection & Architecture

Choose the optimal multimodal model (GPT-4o, Claude, Gemini, or open-source) and design the system architecture

3

Pipeline Development

Build ingestion, preprocessing, and inference pipelines for each modality with unified output

4

Fine-Tuning & Evaluation

Fine-tune on your domain data and benchmark against accuracy, latency, and cost targets

5

Deployment & Scaling

Deploy with GPU auto-scaling, monitoring, and cost optimisation on your preferred cloud

Business Outcomes

What Teams Gain

Result

40–60% more accurate insights when AI can reason across text, images, and audio together

Result

70% reduction in manual content review time with automated multimodal analysis pipelines

Result

New capabilities unlocked — cross-modal search, video summarisation, and visual Q&A that were impossible with single-mode AI

Technology Behind Multimodal AI

AINinza builds on the latest multimodal foundation models and pairs them with production-grade infrastructure for reliable, scalable deployments.

Foundation Models

  • GPT-4o — native multimodal model processing text, images, and audio in a single call
  • Claude (Anthropic) — vision-enabled LLM with strong document and image understanding
  • Gemini (Google) — natively multimodal with long-context video and audio processing

Open-Source & Specialised Models

  • LLaVA & InternVL — open-source vision-language models for on-premise or air-gapped deployments
  • Whisper — multilingual speech-to-text for the audio modality
  • CLIP & SigLIP — cross-modal embedding models for image-text search and retrieval

Infrastructure

  • GPU clusters (A100, H100) — auto-scaling inference on AWS, Azure, or GCP
  • vLLM & TensorRT — optimised model serving for low-latency multimodal inference
  • Vector databases — cross-modal search with Pinecone, Weaviate, or Qdrant

4

Modalities Supported

Model-Agnostic

Swap Models Freely

On-Prem Ready

Air-Gapped Deployments

Frequently Asked Questions

Ready to Go Beyond Single-Mode AI?

Share your data types and business challenges, and we'll show you how multimodal AI can unlock insights that text-only or image-only models miss.

Book A Discovery Call