Course Objective & Scope
AI is no longer limited to reading and writing text: multimodal systems that interpret invoices and field photos, analyse call-centre audio and summarise video are moving to the centre of business operations. This programme explains how text, vision, audio and video models work and which business problems they solve in clear, practical terms.
Participants test OpenAI, Claude, Gemini and open-weight models (Qwen-VL, Llama, Whisper) through live examples, select the right modalities and models for a case from their own organisation, and prepare a quality, latency and cost measurement plan.
Who Should Attend?
- Business unit managers and product owners evaluating multimodal AI initiatives
- Digital transformation, innovation and business analysis teams
- Technical professionals seeking a structured overview of the multimodal landscape
- No technical prerequisites
Key Takeaways
- Explain the difference between stitched pipelines and native multimodal models
- Select the right model family for visual understanding, document reading, audio and video analysis
- Estimate how image tiles, audio seconds and video frames translate into token costs
- Assess hallucination, OCR errors, deepfake and copyright/KVKK privacy risks
- Draft a project brief and success metrics for an internal multimodal use case
Curriculum
The Multimodal AI Landscape
- Modular pipelines (OCR/STT + LLM) versus native omni models
- Current map of frontier and open-weight model families
Vision and Document Understanding Models
- Reading photos, diagrams, tables and handwritten forms
- Enterprise boundaries of image generation and editing models
Audio and Speech Models
- Speech-to-Text (STT), Text-to-Speech (TTS) and native Speech-to-Speech
- Call analytics, meeting summaries and voice assistant use cases
Video Understanding and Generation
- Indexing long videos, event detection and timestamped querying
- Video AI in training, marketing and field inspection
Cost, Latency and Safe Adoption
- How image resolution, audio duration and frame rate drive cost
- Privacy, biometric data (KVKK) and a model selection checklist
Other Courses in This Category
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End-to-end design of enterprise applications combining text, image, audio and document inputs: media preprocessing, structured outputs, hybrid model routing and cost/latency optimisation.
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High-accuracy extraction from complex PDFs, invoices, contracts, tables and forms using VLMs and modern document parsers: layout understanding, schema validation, bounding-box grounding and human-in-the-loop review.
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Multimodal RAG architecture over enterprise documents containing tables, charts, diagrams and images: visual-text indexing, ColPali and image embeddings, hybrid retrieval and visually grounded answer generation.
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Conversation design for LLM-based chat and voice assistants: persona and tone guidelines, multi-turn dialogue flows, disambiguation, repair strategies and seamless handoff to human agents.
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Designing AI customer service agents and human-agent assist systems powered by knowledge bases (RAG), CRM/order tools and policy guardrails, from contact centre architecture to production rollout.
- Live Course
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Speech-to-Text (STT), LLM and Text-to-Speech (TTS) pipelines for enterprise voice AI: low-latency audio processing with Whisper, Deepgram, ElevenLabs and Cartesia, acoustic challenges and telephony integration.
- Live Course
- 5 modules
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