Data duplication is not a storage issue.
It is a signal management problem.

iPhones accumulate redundant data gradually — screenshots, recordings, similar media, duplicated contacts. The challenge is not removal, but correct identification.

We build iOS applications that surface duplication signals, rank confidence, and keep decisions user-led.

On-device privacy boundary

Processing stays local by default. No external data flow.

Conservative decision model

Prefer clarity and review over aggressive automation.

Focused scope 3 domains

Photos, videos, contacts — where duplication is costly.

A focused iOS data systems company.

We develop mobile applications that analyze local iOS data to identify duplication across photos, videos, and contacts. We aim to reduce ambiguity — not to maximize deletion.

The product is built around constraints: explainable signals, conservative grouping, and user-led review.

Scope Definition

We intentionally limit scope to domains where duplication can be evaluated with higher confidence. This enables simpler UX, safer outcomes, and predictable behavior.

The outcome is a workflow that supports careful decisions: what is duplicated, why it’s flagged, and what will change.

Designed for calm control.

Storage pressure usually comes from gradual accumulation, not misuse. Our applications group content by relevance and size, helping users act without guessing.

• Clear grouping by content type

• Side-by-side review for similar items

• No irreversible actions without context

• Minimal configuration, predictable outcomes

Signal-driven processing.

Photos & Videos

Duplicates, similar shots, screenshots, Live Photos, large videos.

Screen Recordings

Old or forgotten recordings that quietly consume storage.

Contacts

Duplicate entries, overview, and optional contact backup.

ON-DEVICE DEDUPLICATION PIPELINE
[Scan] → [Normalize] → [Extract Signals] → [Compare] → [Confidence Ranking] → [User Review]
   │          │              │                │                  │                    │
   │          │              │                ├─ similarity      │                    └─ user decides
   │          │              │                ├─ metadata        └─ avoid false positives
   │          │              │                └─ size/duration
   │          │              └─ time/hash/structure
   │          └─ type/format/resolution
   └─ local access only
The system evaluates observable signals. Decisions remain user-led.

Observed Metrics.

These are instrumentation-oriented reference ranges used during development and testing. Actual performance varies by device, library size, media mix, and iOS constraints.

Instrumentation Notes

Metrics are collected via lightweight, in-app instrumentation during internal test sessions. We track interaction latency, processing throughput, memory behavior, and battery impact.

Values are reported as reference ranges. We treat them as engineering inputs: to detect regressions, validate constraints, and guide optimization.

Median UI latency
interaction
≤ 16ms
Target: keep interaction within a single frame where possible.
Scan throughput
pipeline
~ 2–6k items/min
Typical library conditions (varies by device & media type).
Working memory footprint
runtime
~ 90–220MB
Bounded strategy; avoids unbounded caching in long scans.
Battery impact
sessions
low/ bounded
Chunked processing reduces spikes during heavy work.
Device Profile
Baseline
Modern iPhones with typical photo libraries and steady daily usage.
balanced everyday
Large Library
High media volume and long retention; throughput and batching matter.
chunked scans throughput
Constrained
Lower headroom; UI responsiveness and bounded memory are prioritized.
UI-first bounded memory
PERFORMANCE ENVELOPE (REFERENCE)
UI LOOP (frame)
  0ms ─┬───────────────┬───────────────┬─ 16ms
       │ input / paint │ small updates │
       └───────────────┴───────────────┘

BACKGROUND WORK (chunked)
  scan chunk → compare → rank → yield → next chunk → ...

BOUNDS
  • keep UI responsive
  • chunk heavy work
  • cap caches / buffers
Processing is structured to protect responsiveness and bounded resource usage.

Design principles.

Locality

Analysis stays on-device by default. No uploads, no remote storage.

Explainability

Flagging is backed by observable signals, not opaque automation.

Reversibility

Workflows are designed to minimize irreversible loss.

PRIVACY & PROCESSING BOUNDARY
┌───────────────────────────────┐
│             iPhone            │
│                               │
│  Photos / Videos / Contacts   │
│               │               │
│               v               │
│     Pro Cleaner Processing     │
│       (local algorithms)      │
│                               │
└───────────────┬───────────────┘
                x
        no upload / no storage / no sharing
The boundary is explicit: processing stays local.

Contact

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Company address

Address: BC-890512, 26th Floor, Amber Gem Tower, Ajman, UAE, 1111