Business Requirements Document (BRD) --- Visual Asset Similarity Search Utility (High-Level / Research-Oriented)

1) Purpose

Design and evaluate viable approaches for a utility that helps UI/UX
designers quickly find visually similar existing assets (logos, icons,
illustrations, UI fragments, brand elements) from a large internal
repository---so teams can reuse work and maintain design consistency
across products.

This BRD is intentionally high-level to keep the research space open
(algorithms, vendors, architectures, and UI patterns are all in-scope
for exploration).

2) Background & Problem Statement

Designers often produce or iterate on a logo/icon/visual artifact during
prototyping and then need to answer: - "Do we already have something
like this somewhere?" - "Is there an approved asset with similar
geometry/composition?" - "What's the closest match that follows the same
brand visual language?"

Traditional keyword/tag search fails because similarity is often
about: - Color palette (but not only color) - Structural similarity
(shapes, edges, layout/composition) - Stylistic similarity (flat
vs. skeuomorphic, stroke weights, corner radii, gradients, etc.) -
Partial similarity (a symbol inside a logo, or an icon within a UI
screenshot)

A dedicated similarity tool should reduce duplicate creation, speed up
reuse, and improve cross-product consistency.

3) Goals and Desired Outcomes

Primary goals

Enable designers to search by image input (upload, paste, drag-drop,
or select a region) and retrieve ranked similar assets.
Support similarity beyond color: shape/layout/composition and
"visual language" similarity.
Provide fast iteration loops: query → results → refine → reuse.

Business outcomes (examples)

Reduced duplicate asset creation
Faster discovery of reusable components
Improved adherence to brand / design system conventions

4) Non-Goals (to keep scope flexible)

Not prescribing a single algorithm (research should compare
multiple).
Not requiring perfect "semantic understanding" (e.g., "this is a
fox") unless it helps.
Not mandating a specific repository platform (DAM, drive, git,
design system tool).
Not limiting results to a single asset type (icons/logos/UI
screenshots can all be considered).

5) Stakeholders & Users

Primary users

UI/UX designers, brand designers, design system contributors

Secondary users

Product teams, marketers, developers (occasionally searching for
approved assets)

Stakeholders

Design leadership, brand governance, platform/search team,
security/compliance, IT/infra

6) Key Use Cases (Illustrative)

Find similar logo marks
Input: a draft logo sketch/export → Output: similar symbols, marks,
and compositions.
Find icons with similar geometry
Input: new icon → Output: icons with similar stroke style/shape
proportions.
Find assets that match a visual style
Input: example illustration → Output: assets with similar style
(line weight, shading, palette).
Find partial matches
Input: cropped area of an image / symbol inside a bigger image →
Output: assets containing that motif.
Consistency check across products
Input: UI screenshot or component image → Output: similar UI visuals
from other products.

7) Functional Requirements (High-Level)

Querying

Search by image (upload/paste/drag-drop)
Optional: search by selecting a region (crop/box select)
Optional: search by "more like this" from a result set
Optional: filters (asset type, product, brand, date, owner,
license/approval status)

Results & interaction

Ranked results with similarity score (or relative "more/less
similar" indicators)
Quick preview + metadata (source product, tags, owner, usage rights,
last updated)
"Open original," "Copy link," "Download," "Request access"
(depending on permissions)
Save searches / collections for reuse workflows

Ingestion / indexing

Continuous or scheduled indexing of new/updated assets
Metadata ingestion (existing tags, product mapping, ownership,
approval status)

Administration / governance

Permission-aware search results (don't leak restricted assets)
Monitoring: usage, most-searched patterns, gaps (where designers
create new assets because none exist)

8) Non-Functional Requirements (NFRs)

Performance: interactive response time suitable for design workflows
(target defined during research)
Scalability: large asset repositories; growth over time
Security & privacy: enforce ACLs; audit access; avoid leaking
restricted brand materials
Reliability: graceful degradation if an embedding/index service is
down
Explainability (practical): provide lightweight "why this matched"
signals (e.g., "shape similarity high", "palette similarity
moderate")
Extensibility: ability to swap/upgrade embedding models and indexes
as better approaches emerge

9) Data & Content Considerations

Asset types: raster (PNG/JPG), vector (SVG/PDF), design files
(Figma/Sketch exports), UI screenshots
Metadata: product, component name, design system token usage,
approval status, authorship, licensing
Quality issues: duplicates, near-duplicates, multiple sizes,
transparent backgrounds, watermarks, outdated brand versions

10) Solution Options (Build vs. Buy vs. Hybrid)

This initiative is naturally suited to vector similarity search using
embeddings + nearest-neighbor search.

Option A --- Build (custom pipeline)

Generate embeddings for each asset (and optionally multi-embeddings
per asset: whole image + regions).
Store embeddings in a vector index and retrieve nearest neighbors.

Option B --- Buy (managed vector database / search platform)

Outsource parts of scaling/ops to a managed vector DB.
Still requires an embedding strategy and ingestion pipeline.

Option C --- Hybrid

Use existing enterprise search plus vector search for similarity,
combining keyword + metadata + vectors.

11) Algorithm / Technique Research Areas (Keep Wide)

A) Embedding-based similarity (recommended baseline)

Use modern image embeddings so visually similar items are close in
vector space; nearest-neighbor search retrieves candidates.

B) Approximate Nearest Neighbor (ANN) indexing strategies

Compare multiple ANN approaches and engines for scalability and
performance.

C) Multi-stage retrieval & reranking

Stage 1: fast vector retrieval (top N)
Stage 2: rerank with a stronger model or additional heuristics
(style similarity, shape emphasis, palette alignment)

D) Classical computer vision signals (useful as complements)

Perceptual hashing for near-duplicate detection
Keypoint/feature matching methods for geometric similarity
Edge/contour-based features to emphasize structure over color

E) Region-based / component-based similarity

Index whole-image embeddings + embeddings of regions (tiles/crops)
to enable motif-level similarity.

12) UX Research Directions (Non-prescriptive)

"Search by example" vs "search by region" workflows
Controls that help designers steer similarity:
- weight color vs structure vs style
- "more like this / less like this"
- filtering by product / design system version / approval status
Result presentation options:
- grid view with hover-compare
- side-by-side overlay comparison
- "closest approved alternative" callout

13) Success Metrics (Research-Friendly)

Time-to-find reusable asset (median)
Reuse rate / downloads / "open source file" actions
Duplicate creation rate (before vs after)
Search satisfaction (designer rating)
Precision@K / Recall@K on a curated evaluation set

14) Risks & Open Questions

Risks

"Similarity" is subjective and may vary by designer intent.
Models may overweight semantics and underweight geometry (or vice
versa).
Permission/ACL complexity may be a larger engineering challenge than
modeling.

Open research questions

What similarity definition best matches designer intent
(geometry-first, style-first, hybrid)?
Do we need separate models for logos/icons vs UI screenshots?
Should the system learn from internal feedback loops ("this match is
good/bad")?

15) Recommended Research Plan (Phased)

Phase 0 --- Discovery

Audit repositories, formats, metadata quality, access control model.
Collect a small "golden set" of similarity examples from designers.

Phase 1 --- Prototype baseline

Embedding + vector search proof-of-concept.
Compare at least 2 storage/index options.

Phase 2 --- Improve relevance

Add reranking and/or multi-signal scoring (structure/palette/style)
Add region search

Phase 3 --- Productization

Governance (approved assets surfaced)
Analytics + feedback loop
Operational hardening + scale testing