How long does an AI implementation take?

Most single workflow implementations take 2-6 weeks from kickoff to production. Full AI transformation programmes run 6-12 weeks.

Do you work with specific AI models?

We are model-agnostic and work with all major providers including Anthropic Claude, OpenAI GPT, Google Gemini, Meta Llama, Mistral, and more.

Can you deploy AI on our own servers?

Yes. Our Local & Private AI service deploys models on your own infrastructure or private cloud.

Glossary

Multi-modal AI

Multi-modal AI refers to artificial intelligence systems that can process, understand, and generate multiple types of data — such as text, images, audio, and video — within a single model.

Multi-modal AI describes AI systems capable of working with more than one type of data input or output. While traditional AI models specialise in a single modality — text, images, or audio — multi-modal models can process combinations of these, enabling richer and more natural interactions. Modern multi-modal models can accept an image and answer questions about it, generate images from text descriptions, transcribe and analyse audio alongside visual content, and understand documents that combine text, tables, and images. Models like GPT-4V, Claude with vision, and Gemini exemplify this capability. The technical approach typically involves training separate encoders for each modality (text, vision, audio) and then aligning their representations in a shared space. This allows the model to reason across modalities — for example, understanding that a photograph of a sunset and the phrase 'beautiful evening sky' represent related concepts.

Real-world business data is inherently multi-modal. Documents contain text and images. Customer interactions include voice and text. Products have photographs, descriptions, and specifications. Multi-modal AI can process all of these together, enabling more comprehensive analysis and automation. Practical applications include document processing (understanding invoices, forms, and reports that combine text, tables, and images), quality inspection (analysing visual defects while referencing specification documents), customer support (handling queries that include screenshots or photos), and accessibility (generating descriptions for images or transcribing audio content). Multi-modal AI reduces the need for separate specialised systems for each data type, simplifying architecture and improving the quality of AI-driven decisions by providing a more complete picture of the information being analysed.

Related Terms

Explore further

vision language model large language model text to speech speech to text computer vision

FAQ

Frequently asked questions

Leading models support text, images, and audio as inputs, with text and images as outputs. Video understanding is emerging. The specific capabilities vary by model — check provider documentation for the latest supported modalities.

Processing images and audio typically costs more than text alone, as these inputs require more computation. However, the cost is often offset by eliminating the need for separate models and the improved quality of analysis that considers all available data.

Yes. Modern multi-modal models can interpret documents with tables, charts, diagrams, and mixed layouts. They can extract information from scanned documents, photographs of whiteboards, and complex multi-page reports, though accuracy varies with document complexity.