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Multimodal Search

FieldVision knowledge search for inspection evidence.

A field knowledge workflow for teams that need to search across photos, documents, notes, and inspection evidence.

[ Client review ]

FieldVision Search made the workflow easier to explain: the inputs, AI review, human handoff, and business action are all visible in one place.

Product team
Multimodal field search system finding inspection photos, notes, and document evidence.
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CS / 37FieldVision SearchField photos · OCR snippets
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Client

FieldVision Search

Field photos · OCR snippets

Engagement

Product narrative

Positioning · workflow story · product proof

Role

AI builder

Multimodal Search workflow

Year

2026

Project positioning

Buyer casemultimodal search outcomes
Photo
Visual results

Field images are searchable

OCR
Text snippets

Labels and forms indexed

Source
Evidence cards

Answers stay grounded

Filter
Search control

Site and date narrowing

Field evidence is split across photos, notes, forms, and reports.

Teams need to search inspection evidence by text and image context, then verify results through source cards.

The workflow needed a visual and operational story that buyers can scan quickly: what comes in, what the AI does, what a human reviews, and where the result lands.

Photos, notes, and documents require different indexing paths.

Site, date, asset, and inspector context improve search quality.

Forms, signs, and handwritten notes need confidence handling.

Users need to inspect the source before acting.

We designed search as a multimodal evidence workspace.

The interface shows search bar, photo results, OCR snippets, and cited source cards.

The project is framed around the business workflow itself: the source inputs, AI review, approval points, and final handoff are all visible in one story.

  • Search bar with field-specific filters.
  • Photo results and inspection notes.
  • OCR text snippets from field documents.
  • Source cards for answer verification.

Photo results

Visual evidence is treated as first-class search output.

OCR snippets

Extracted text appears beside the image result.

Source cards

Each answer can be verified.

Field filters

Users can scope results by operational context.

Week 1

Workflow audit

Mapped source inputs, users, review points, and the final business action.

Week 2

AI task design

Defined classification, extraction, drafting, prediction, or detection responsibilities.

Week 3

Human review path

Added approval, exception, and escalation points where judgment matters.

Week 4

Product narrative

Turned the workflow into a clear buyer story for sales conversations, reviews, and handoff.

Evidence retrievalPhotos and notes become searchable.
88
Answer trustSource cards support each result.
86
Field speedInspectors find prior evidence faster.
80
Context qualityOCR and photo signals combine.
84
[ 01 ] Sources
Field sources
  • Photos
  • Inspection notes
  • Forms
  • Reports
[ 02 ] Prepare
Index prep
  • OCR
  • Embeddings
  • Metadata
  • Geo context
[ 03 ] Decide
Search answer
  • Photo match
  • Snippet
  • Source card
  • Confidence
[ 04 ] Deliver
Field handoff
  • Evidence packet
  • Saved search
  • Reviewer note
  • Export

Multimodal field search works when photos, OCR text, and source cards are returned together.

Clearer product surface: FieldVision Search now communicates the workflow through the actual review states, handoffs, and outcomes buyers care about.

Faster buyer clarity: the problem, workflow, proof points, and next action are easy to understand without a technical walkthrough.

"

FieldVision Search made the workflow easier to explain: the inputs, AI review, human handoff, and business action are all visible in one place.

P
Product team
Sources
  • Photos
  • Inspection notes
  • Forms
  • Reports
Processing
  • OCR
  • Embeddings
  • Metadata
  • Geo context
Answer layer
  • Photo match
  • Snippet
  • Source card
  • Confidence
Delivery
  • Evidence packet
  • Saved search
  • Reviewer note
  • Export
Governance
  • Human review
  • Audit trail
  • Quality checks
  • Fallback rules
Book a call

Got a problem AI might solve? Let's find out.

30 minutes. Free. No NDA needed. You leave with a clear yes-or-no on whether to build — and a one-pager you can forward to your team the same day.

[ Response ]

Within 24 hours

[ Timezone ]

GMT+5 · flexible

[ Discovery ]

Free · no NDA needed

[ Engagement ]

$1,000 / week sprint