Enterprise information is overwhelmingly multimodal. Financial statements are PDFs with embedded tables. Customer calls are audio. Inventory data lives in photos. Product defects show up in inspection images. Text-only AI pipelines systematically miss this information.
Four production-ready modalities
Document Vision (PDF + images)
Vision LLMs can read contracts, invoices, medical records, and scanned forms — extracting structured data from layouts that defeat traditional OCR. Table extraction accuracy is now commercially viable.
Chart & Graph Understanding
GPT-4o and Gemini 2.0 can analyze charts, graphs, and dashboards — extracting values, trends, and relationships from visual data that would otherwise require manual interpretation or chart-to-data pipelines.
Audio Transcription & Analysis
Whisper and proprietary alternatives achieve near-human transcription accuracy. Combined with LLMs, audio pipelines can transcribe, summarize, extract action items, and classify sentiment from calls and meetings.
Visual Inspection
Vision models detect defects, measure dimensions, classify objects, and verify assembly correctness from camera feeds or uploaded images — replacing or augmenting expensive manual inspection workflows.
Practical limitations (2026)
- Token cost: Image tokens are expensive — a high-res image in GPT-4o costs several thousand tokens. Use image compression and ROI cropping before sending to vision models.
- Consistency: Vision models are less deterministic than text models. Build evaluation suites specifically for your image types before deploying to production.
- Latency: Multimodal calls are 30–50% slower than text-only. Design async pipelines for non-real-time use cases.
Multimodal pipelines on MoltBot
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