AI Answer Library
Short answer
The workloads that land today all share one shape: turning information inside an image into structured text. Invoice and form extraction, layout reconstruction of scanned documents, reading gauges and equipment nameplates, shelf and store-display checks, and interpreting drawings or flowcharts. What they have in common is a definite correct answer, the ability to spot-check by hand, and a bounded cost when wrong. Tasks requiring precise measurement, safety adjudication or real-time control still belong to specialised vision models or classical machine vision — multimodal LLMs are strong at semantics, not at precision or latency.
The table is framed around internal enterprise systems. The maturity column is a relative judgement — the same application is far easier where formats are standardised (a fixed-layout tax invoice) than where they are not (delivery notes each supplier designs themselves). When picking a pilot, prefer scenarios that satisfy all three of: a definite correct answer, the ability to spot-check, and a bounded cost when wrong.
| Application | Input | Output | Maturity and caveats |
|---|---|---|---|
| Invoice and form field extraction | Photos or scans of invoices, delivery notes and expense receipts | Structured fields: amount, date, supplier, tax number | Fairly mature, but monetary fields need human review |
| Layout reconstruction for scanned documents | Two-column scans containing tables and margin notes | Structured text with reading order and hierarchy preserved | Fairly mature, though complex tables still need dedicated extraction |
| Reading gauges and equipment nameplates | Inspection photos of dials, nameplates and status lights | Readings, model numbers and status, written into the inspection log | Usable, but glare, grime and camera angle matter — enforce a photo protocol |
| Store display and on-site compliance checks | Photos of shelves, displays and work sites | A compliance verdict plus a description of what is wrong | Fine for first-pass screening; verdicts should be human-reviewed, not fed straight into performance scoring |
| Understanding drawings and flowcharts | Process diagrams, wiring diagrams, schematics | A structured description for retrieval or assisted generation | Assistive only — engineering use requires sign-off by an engineer |
| Meeting and training video summarisation | Recorded video plus the shared-screen track | Minutes, decision lists and action items | Fairly mature; the hard parts are speaker separation and terminology accuracy |
They solve problems at different levels. Classical machine vision and purpose-built detectors excel at determinism and precision: under fixed lighting, a fixed fixture and a fixed acceptance rule, they decide whether a part is scratched or out of tolerance stably, extremely fast, and with reproducible quantitative output. Multimodal LLMs excel at semantics and generalisation: they read a document layout they have never seen, follow the logic of a flowchart, and answer "what in this site photo violates the rules". The right division is therefore: precise numbers, millisecond latency and perfect reproducibility go to the specialised solution; content comprehension, tolerance for varying layouts and natural-language explanation go to the multimodal model. They also chain well — the detector finds it, the multimodal model explains and classifies it.
First, cost. Images are split into many visual tokens, and one high-resolution photo can cost as much as thousands of words of text, so do the arithmetic before processing tens of thousands of documents. The usual optimisation is to downscale to what the task needs and crop the region of interest before sending. Second, input quality. Multimodal models are more sensitive to capture conditions than people expect — glare, occlusion, skew and overexposure all move results materially. Rather than endlessly tuning prompts, define a photo protocol and validate at capture time; this is usually the highest-return step available. Third, evaluation and fallback. Assemble a set of real images with reference answers and track results stratified by scenario, and design an explicit degradation path: low confidence or a missing field escalates to a human instead of letting the model guess a value into your business system.
Where this applies
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