Multimodal & Vision AI
Vision-language pipelines for diagrams, photos, scans, and video — with the same grounding, eval, and infra rigor as our text systems.
Understanding across text, image, and video
Modern models see as well as they read. We build multimodal systems that interpret images, diagrams, charts, medical imaging, UI screenshots, and video — combining vision-language models with structured extraction, retrieval, and validation.
Outcomes
Our approach.
Sample the visual corpus
Representative images or frames across every variant. Visual edge cases (angle, lighting, occlusion) drive the approach.
Pick the model shape
Vision-language model for open-ended understanding, CLIP-style embeddings for retrieval, specialized vision APIs for OCR or detection.
Structure the output
Schema-constrained extraction, validation, and confidence scoring. Ambiguous items route to review.
Ground and cite
Outputs reference the image region or frame they came from. Debuggability matters as much as accuracy.
What you get.
Production-shaped, from day one.
// Structured extraction from an image with citation
const result = await vision.extract({
image: page.buffer,
schema: ChartSchema,
model: "claude-sonnet-4-6",
cite: "bbox",
})
if (result.confidence < 0.85) {
queueForReview({
image: page.buffer,
draft: result.value,
regions: result.citations,
})
}A proven shape for this solution.
We adapt it to your cloud, data, and compliance requirements. Nothing here is boilerplate — every layer is justified by the numbers.
Where this shows up.
- Medical imaging metadata and triage summaries
- Invoice, receipt, and chart extraction
- UI screenshot analysis for QA and support
- Video session summarization for coaching and training
What we use.
We’re not religious about tools. We pick what fits your constraints and team.
Shipped examples.
Healthcare patient data mapping & health information chat
Mapped and normalized patient data to power a grounded chat experience where patients can ask questions about their own health information — safely.
What teams usually ask.
Can vision models replace OCR and Textract?
+
For complex layouts and diagrams, often yes. For high-volume structured forms, layout-aware OCR (Textract, Azure Document Intelligence) is still cheaper and more deterministic. We mix both.
Do you do medical imaging?
+
Non-diagnostic workflows — metadata extraction, triage summaries, flagging — yes. Diagnostic decisions require regulated devices; we stay in the supporting layer.
How do you search across images and text together?
+
Multimodal embeddings (Voyage Multimodal, CLIP) put text and images in the same vector space, so a text query can retrieve an image and vice versa.
Related solutions.
Document Intelligence
Pipelines that turn unstructured documents into structured data your systems can use.
Retrieval-Augmented Generation
End-to-end RAG pipelines from ingestion to retrieval to answer generation, built for accuracy and cost control.
Conversational AI & Chat Lookup
Production-grade chat systems that answer from your sources with citations, guardrails, and session memory.
Ready to accelerate your tech growth?
Schedule your free consultation today and let's discuss how we can help your business scale efficiently.
