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MULTIMODAL AI

AI that reads, sees, and listens.

Most business data isn't clean text — it's scanned invoices, product photos, support calls, and factory-floor video. Dezvo builds multimodal AI systems that turn images, documents, audio, and video into structured, searchable, actionable data — using frontier vision-language models where they win and specialised models where they're cheaper.

See Our Work
Multimodal capabilities
  • Vision-language models (Claude, GPT-4V, Gemini)
  • OCR & document layout parsing
  • Speech-to-text & TTS (Whisper & more)
  • Video analysis pipelines
  • Structured JSON out of any medium
WHAT IS MULTIMODAL AI?

Beyond text-in, text-out.

Multimodal AI processes more than one type of input — images, documents, audio, and video alongside text. Modern vision-language models can read a scanned invoice, describe a product photo, or interpret a chart directly; speech models transcribe and synthesise voice; video pipelines combine both over time.

The practical value is conversion: unstructured media becomes structured data. A folder of supplier invoices becomes rows in your ERP; a call recording becomes a summary, action items, and a CRM entry; a product photo library becomes searchable, tagged catalog data. That conversion is what we build.

CAPABILITIES

Four media, one structured output.

Vision AI & image understanding

Product tagging, visual quality checks, chart and diagram interpretation, and image-based search — frontier VLMs or trained specialist models as the task demands.

OCR & document AI

Layout-aware extraction from scans, PDFs, and photos — tables, stamps, handwriting, multi-language. Every field validated before it touches your systems.

Speech AI

Call transcription with speaker separation, voice assistants, meeting summaries, and TTS — tuned for Indian-accent English, Hindi, and Gujarati where needed.

Video intelligence

Scene detection, object tracking, safety-compliance monitoring, and searchable video archives — frames plus transcripts fused into one index.

FAQ

Common questions, answered.

If your question isn't here, message us — usually same-day reply.

On clean digital PDFs, field-level accuracy above 98% is normal; on photographed or degraded scans, 90-97% depending on quality. The system design matters more than the raw number: confidence scoring per field, validation rules, and human review queues for low-confidence extractions mean errors get caught, not silently posted.

Usually not to start. Frontier vision-language models handle most document and image tasks out of the box, and we benchmark them on your data in week one. Custom-trained models earn their keep at high volume (cheaper per call), for niche visual domains, or for on-premise requirements — the benchmark tells us when.

Yes. Open-source vision and speech models (Whisper, open VLMs) can run on your servers or edge hardware when data can't leave your network — common for healthcare, defence-adjacent, and factory settings. Cloud APIs remain the fastest path when policy allows.

Pilots start around $3,000-$8,000 — for example, an invoice-extraction pipeline for one document type, benchmarked on your real files in 2-4 weeks. Production systems with multiple document types, review workflows, and ERP integration typically run $10,000-$35,000.
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