tecnovy AI Suite

Multimodal AI: Combining Text, Images, Audio and Documents

Image understanding, document extraction and transcription are the three multimodal model capabilities you put to work here. You have photos described and classified, pull details out of scans, forms and diagrams, and turn voice recordings into text. In Python you connect these data types into a pipeline, a chain of processing steps. Your code checks every result against a schema before it moves on to the next step.

Attendance certificate4.8/5 on ProvenExpert

What this module delivers.

Checked, structured data from scans, photos and voice recordings is the output of the applications Python developers build in tecnovy's Multimodal AI training. Afterwards your team picks a model for each data type and decides where people review results.

How the training runs

Method: Python exercises with image, document and audio data
Basis: tecnovy curriculum on multimodal models, as of November 2026
Outcome: tecnovy certificate of participation, no examination

Dates & booking

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New dates are published regularly. Join the waitlist or plan an in-house course for your team.

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Fit

Who this module is designed for

Typical roles

You develop applications for document processing or customer service and are asked to bring in images, scans or speech.
You work in data science, computer vision or automation and want to put multimodal models to use.
You are asked to analyse scans, photos or voice recordings automatically and process the results further.

Prerequisites

No prerequisites

You can start right away. What you need: programming skills in Python, experience with LLM APIs.

Consider instead LLM Engineering Without experience of LLM APIs: there you build model APIs into Python code, with prompts, JSON schemas, tool calls and tests.

Curriculum

Multimodal AI in Detail

Five parts lead from how a model processes images, audio and text together, through images, documents and speech, to the pipeline that combines data types. The last part checks where the models make mistakes and when people take a second look.

01Foundations of multimodal models

Before you feed in data, you clarify what a model does with images, audio and text together.

How models process images, audio and text together
Which data types a model accepts
Choosing a model by cost and data protection

02Images and documents

You have images analysed and pull details from documents into a fixed structure.

Image description and classification
Visual questions: asking an image targeted questions
Extracting data from scans, forms and diagrams

03Audio and speech

You make spoken language usable for your application, in both directions.

Transcribing and summarising recordings
Voice control: handling spoken commands
Speech output for assistants

04Multimodal pipelines

Here you join the data types into one flow whose result your code can process further.

Combining data types, for example a document plus a spoken follow-up question
Structured outputs with fixed fields
Validating every output before it moves on

05Quality and limits

Finally you check where your application gets things wrong and who takes over then.

Error sources in image and audio processing
Tests for multimodal results
Human review for the cases that fail

Outcome

What you will be able to do afterwards

01
You choose a model for each data type by supported inputs, cost and data protection and justify the choice.
02
You have images described, classified and questioned directly, and process the answers in your code.
03
You extract details from scans, forms and diagrams as structured output, meaning JSON with fixed fields.
04
You transcribe voice recordings, summarise them and handle spoken commands in your application.
05
You turn an assistant's answers into speech output.
06
You combine several data types in one pipeline, for example a document with a spoken follow-up question.
07
You check every structured output against a schema and route the cases that fail to human review.
08
You test your application specifically for typical error sources in images and recordings.

tecnovy Teilnahmezertifikat

Confirms your attendance and the curriculum covered in the training. Issued by tecnovy, shareable on LinkedIn.

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Offer scope

What is included

Included

Project repository with sample dataIt holds sample images, scans, forms and audio recordings for every exercise, plus the code of the course pipeline.

Why tecnovy

What you get on top with us

No Slideshow, Hands-On!

Promised: no PowerPoint marathon. We work in groups, tie theory to practice, and you get real project examples from our experienced trainers plus the exchange with like-minded people.

Certificate Showroom

Get your certificate of participation and, if you have one, add your exam certificate from E-Learning. Fully automated, beautifully designed. Just for you, only at tecnovy.

Flexible Date Change

If you are not able to attend the course, you can rebook your training free of charge up to one week before the start of the training.

Attend Twice, Pay Once

You are welcome to attend the training online again within a year as a refresher.

Learn from Experts

We always guarantee you the use of didactically and methodically first-class qualified trainers who draw their knowledge from training experience as well as professional practical and project experience.

FAQs

Frequently asked questions

01Does multimodal AI replace classic text recognition (OCR)?

Often it complements OCR, the machine recognition of text in images, rather than replacing it; which route fits depends on volume, image quality and cost. Anthropic, for example, passes a PDF page to Claude as extracted text plus a page image (as of November 2026). In tecnovy's Multimodal AI training this trade-off is part of choosing a model.

02Do I need prior knowledge of computer vision?

No. For tecnovy's Multimodal AI training, programming skills in Python and experience with LLM APIs are enough. If you lack the API experience, start with the tecnovy course LLM Engineering.

03Does every model handle images, documents and audio?

No, providers cover the data types differently. Anthropic's Claude and Google's Gemini read PDFs including charts and tables, Gemini accepts audio directly, and OpenAI offers gpt-transcribe as a dedicated transcription model (as of November 2026). That is why tecnovy's Multimodal AI training has you choose the model per task.

04How large can documents and recordings be?

Each provider sets its own limits: Claude accepts up to 600 PDF pages per request (100 with a context window under 1 million tokens), Gemini PDFs up to 50 MB or 1,000 pages and up to 9.5 hours of audio per prompt, OpenAI files up to 25 MB for transcription (as of November 2026). In tecnovy's Multimodal AI training these limits feed into choosing a model.

05Do multimodal models read scans without errors?

No. Anthropic names mistakes with low-quality, rotated or very small images, and OpenAI names misreadings of rotated or upside-down text (as of November 2026). In tecnovy's Multimodal AI training you test such cases on purpose and hand results that fail your checks to people for review.

06What applies to photos and recordings that show people?

Faces in photos and voices in recordings can be personal data. In tecnovy's Multimodal AI training you check which data goes to which provider and whether it reuses inputs. What you may process is set by your company's policies.

07Do the exercises run on real model APIs?

Yes, the exercises in tecnovy's Multimodal AI training call models through their APIs and need API access with a key for that. Billing differs by provider: Anthropic charges a PDF page as text and image tokens, while Google offers a free tier for the Gemini API with limited model access (as of November 2026).

08Does Multimodal AI end with a certificate?

Yes, with the tecnovy certificate of participation, which you receive with at least 80% attendance. The tecnovy Multimodal AI training includes no examination and no certification from a model vendor.

09What builds on Multimodal AI?

At tecnovy, Building Enterprise AI Apps comes next: there you take an AI application such as your multimodal pipeline into an architecture for production use, with permissions, guardrails, tests and deployment.

What does your training at tecnovy look like?

Multimodal AIMultimodal AI: Combining Text, Images, Audio and Documents

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