Information split across formats
Teams must manually combine images, documents, audio and data to understand a case.
04 / Hybrid Intelligence
Multimodal AI Solutions
Real situations rarely come in a single format. An insurance claim includes photos, forms and a phone call. A maintenance issue involves sensor readings, images and technician notes. Multimodal AI brings these together so systems can understand situations the way people do, by combining evidence of different kinds.
At a glance
- Service
- Multimodal AI Solutions
- Pillar
- Hybrid Intelligence
- Typical starting point
- Use case and data modality assessment
- Typical first phase
- 8 to 14 weeks for a first multimodal solution
- Key technologies
- Multimodal AI, Computer Vision, Large Language Models
Durations depend on scope, data readiness and integration needs.
What is Multimodal AI?
Short answer
Multimodal AI refers to AI systems that process and relate multiple types of data, such as text, images, audio, video, documents and sensor signals, within a single model or a coordinated pipeline, enabling tasks like answering questions about images, analysing video with transcripts or combining sensor data with visual inspection.
Recent vision language models and audio-capable models can interpret images, charts, screenshots and speech alongside text, which has opened many practical applications. Multimodal systems can also be built by combining specialised models, such as a vision model and a sensor anomaly detector, whose outputs are fused and interpreted together.
We design multimodal solutions around the evidence a decision needs. Sometimes a single large multimodal model is the best option. Often a pipeline of specialised components is more accurate, efficient and controllable. Evaluation must test each modality and their combination, since failures can arise from any input or from how they are interpreted together.
Business problems
What problems does Multimodal AI solve?
These are the situations where organisations most often find this service worthwhile.
Visual documents that text AI cannot read
Charts, diagrams, forms and screenshots contain information text-only models miss.
Video and audio archives without insight
Recordings of calls, inspections or meetings are stored but not analysed.
Incomplete picture from single sensors
One data source alone gives an unreliable view of equipment or environments.
Use cases
Where can Multimodal AI be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Insurance claims | Combining damage photos, claim forms and call notes for adjuster review. |
| Field inspections | Linking inspection photos, voice notes and checklists into structured reports. |
| Manufacturing | Combining camera inspection with process sensor data to identify defect causes. |
| Customer support | Understanding screenshots or product photos shared by customers alongside their messages. |
| Education | Analysing handwritten work, diagrams and spoken responses with teacher oversight. |
| Media and archives | Searching video by spoken content, visual scenes and on-screen text. |
Who should consider it
- Organisations whose cases involve several data types
- Teams with large image, audio or video archives
- Industrial operations combining vision and sensor data
- Product teams building assistants that must see and hear
How AARHIT approaches it
How we deliver Multimodal AI
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Use case and evidence mapping
We identify which data types contribute to the decision and how experts combine them today.
Output: evidence map and requirements.
Stage 2 of 6
Data alignment
Data from different sources is linked by time, case or location and prepared for processing.
Output: aligned multimodal dataset.
Stage 3 of 6
Architecture choice
We compare single multimodal models with specialised pipelines for accuracy, cost, latency and control.
Output: selected architecture with evaluation evidence.
Stage 4 of 6
Build
Models and pipelines are built, including fusion logic, prompting and structured outputs.
Output: working multimodal system.
Stage 5 of 6
Evaluation
Performance is measured per modality and combined, including cases where inputs conflict or are missing.
Output: evaluation report.
Stage 6 of 6
Deployment and review
The system is deployed with human review for uncertain results and monitoring across modalities.
Output: deployed solution with monitoring.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Multimodal models | Vision language models such as GPT-4o, Claude, Gemini, Qwen-VL and LLaVA variants |
| Vision | Detection and segmentation models, OCR and layout models |
| Audio | Speech recognition such as Whisper, speaker diarisation, audio event detection |
| Sensors | Time-series models and anomaly detection |
| Retrieval | Multimodal embeddings for image, text and video search |
| Pipelines | Orchestration, media processing and structured output validation |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- A fuller understanding of cases from combined evidence
- Less manual effort assembling information from different formats
- Searchable image, audio and video archives
- Better detection by combining complementary signals
How long does implementation generally take?
Typical timeline
A first multimodal solution typically takes 8 to 14 weeks, depending on data alignment effort, the number of modalities and whether specialised models must be trained.
What determines complexity?
- Number of modalities involved
- Alignment of data across sources
- Volume of media to process
- Real-time versus batch processing
- Privacy of images, audio and video
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Alignment is often the hardest part
Linking the right photo, recording and record to the same case or moment requires careful data engineering.
Media processing costs add up
Video and audio processing can be expensive at scale. Sampling and pre-filtering help.
Test with missing or conflicting inputs
Real cases often lack a modality or contain contradictions. The system must handle both.
| Risk to consider | How we manage it |
|---|---|
| Misinterpretation of visual content | Specialised models for critical visual tasks, validation and human review. |
| Privacy of media data | Consent, anonymisation, access controls and retention policies. |
| High processing costs | Selective processing, efficient models and cost monitoring. |
| Evaluation gaps | Test sets covering each modality, combinations and edge cases. |
FAQ
Questions about Multimodal AI
When is a multimodal approach worth the extra complexity?
When decisions genuinely depend on combining evidence from different formats, such as photos with documents or sensor data with images, and when single-format approaches have been shown to miss information that matters.
What are vision language models?
Vision language models are AI models that accept both images and text as input and can describe images, answer questions about them, read charts and documents, and reason about visual content in language.
Is one multimodal model better than several specialised models?
Not always. A single model is simpler to build for broad understanding tasks, while specialised pipelines are often more accurate, efficient and controllable for demanding tasks. We evaluate both for your use case.
Can multimodal AI analyse phone calls?
Yes. Speech recognition converts calls to text, and further analysis can identify topics, sentiment and compliance, sometimes combined with related documents or screen activity.
How do you evaluate a multimodal system?
By testing each input type separately and in combination, including cases where inputs are missing, poor quality or contradictory, and measuring the final outcome against expert judgement.
Next step
Considering Multimodal AI for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.