Multimodal AI development
A multimodal application connects information that does not arrive in one format. It might answer a question about a drawing, compare a product photo with a written specification or turn a recorded inspection into structured findings.
We start by pinning down the exact inputs and outputs, then decide whether one multimodal model is enough or separate components give more control.
What does multimodal AI do?
A multimodal system uses more than one type of information for the same task, for example text with images, or audio with video and structured records. It either joins the results of separate components or uses a model that reasons across the inputs directly.
Detecting a damaged part in a photo is computer vision. Comparing the damage with a maintenance manual and writing a repair instruction that cites its source is multimodal.
What we build
We scope each task as a fixed set of inputs that produce one output, and test it on your own examples.
Document review
Reads mixed page layouts, answers defined questions and keeps the page or region behind each finding.
pages + text + tables → fields + findings + citations
Visual support
When a customer sends a photo with their message, the system reads both to classify the issue and asks for anything missing.
customer message + image → diagnosis + next step
Operations recordings
Finds defined events, links speech to visible actions and produces a time-stamped record.
video + audio + timestamps → events + summary
Product search
Finds items by visual similarity, then filters them by catalog attributes, stock and business rules.
image + text filters → ranked items
Field reporting
Combines evidence captured on site into a record with the required fields and links for review.
photos + voice note + location → structured report
Charts and drawings
Reads charts, drawings and diagrams with their captions and turns them into values or descriptions that can be checked.
diagram + caption + text → values + description
When it's a good fit
The inputs differ in format
The answer depends on putting two kinds of input together, such as a photo and a written specification, or what is said and what is visible on video.
Computer vision may be enough
If the result you need comes from the image or video alone (a label, a measurement, a defect), computer vision on its own will do the job.
One model or a pipeline of components?
Mostly it comes down to how closely you need to trace and control each step. Accuracy, cost, response time and deployment limits decide the rest.
Single multimodal model
One model receives the media and instructions and returns the answer or structured output. This works well for mixed documents, visual questions and any task where one model already meets your quality and privacy requirements.
Pipeline of specialized parts
Separate components handle OCR, detection, speech recognition, retrieval, language generation and rule-based validation. A pipeline makes sense when intermediate results must be checked, when parts have to run in different environments or when a specialized model does a key step better.
How a project runs
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Pin down inputs and outputs
We agree with you on the exact inputs, the output and what counts as acceptable, using examples of your own material.
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Compare the options
We try a single model and a pipeline of components on samples of your material and choose the one that meets your requirements.
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Prepare each input
The system resizes images, samples video, transcribes audio and parses documents. Each result keeps the timestamps and coordinates of the input it came from.
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Connect to your work
Results are checked against approved references, the records in your systems and the task rules. They reach your workflow or interface with source links, a confidence level and a review status. Nothing is stored beyond what your retention policy allows.
Testing across input types
Cross-modal alignment
When the system links a sentence to a region of an image, or a voice to a moment in a video, the link has to be right. We test those links directly.
The whole task
The final output is scored on the task it serves, including human review and checks in the systems that use it.
Privacy in media
Faces, voices and personal data in images, audio or documents are handled by agreed rules, and we test that none of them leak into outputs.
What you receive
- Input, output and acceptance specification
- Model and architecture comparison on your material
- Media processing and model integrations, with retrieval, tools and structured validation where in scope
- Cross-modal evaluation set and test report
- Code and documentation, handed over to your team
Frequently asked questions
Can a multimodal system handle long videos?
It can. Long videos are split into parts, and the system looks more closely only at the parts that matter.
Can a multimodal system show which part of an image or document an answer came from?
Usually, yes. It can point to the page, the area of an image or the moment in a recording it relied on.
What happens when an image or recording is poor quality?
The system can ask for a better file, lower its confidence or pass the case to a person. We decide with you before release what it should do.
Can sensitive images and recordings stay in our environment?
Yes. Everything can run in your environment, or the original files can stay inside while only approved results go to another service.
Technical references
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