Bounded memory
Process discrete batches and release temporary objects instead of preserving an unbounded in-memory comparison set.
Cloud ContactsSee how it works
Large contact collections
Keep thousands of contacts understandable and responsive.
Cloud Contacts separates large operations into bounded batches, keeps long-running work away from the primary interface, and reports progress that can survive navigation.
For consultants, executives, engineers, sales teams, and long-time users whose address books have grown across many years and providers.
iPhone & iPad
Mac
Large libraries change the engineering problem.
Comparing every contact with every other contact can produce excessive work and memory pressure. Cloud Contacts uses indexed candidate generation, graph-based grouping, and bounded processing so cleanup and synchronization can scale without preserving the entire object graph in memory.
Designed for sustained work
Background execution
Keep parsing, matching, backup, and synchronization away from the primary interface when operations are long-running.
Visible, recoverable progress
Leave an operation screen and return without presenting a frozen or disconnected progress indicator.
From scan to approved result
- 1
Generate focused candidates
Use normalized and indexed properties to narrow the records that need deeper comparison.
- 2
Process manageable batches
Keep memory exposure bounded and save progress at controlled points during long operations.
- 3
Apply and report
Commit approved changes according to provider capability while keeping progress and recovery state visible.
Measurements need context
Performance varies by device, operating system, contact complexity, network, provider, and operation. Exact timing or memory claims should be accompanied by reproducible methodology.
Documented architecture
A public engineering article explains the data structures, chunking model, and security boundaries in ordinary technical language.
No universal timing promise
The page avoids guaranteeing a fixed latency for every device, server, or contact dataset.
Provider-aware writes
Large-scale processing does not override the field and write limitations of the destination provider.
Questions about this workflow
How many contacts can Cloud Contacts manage?
The app is designed and tested for large collections, including tens of thousands of records. Practical performance depends on the device, dataset, provider, and operation.
Does duplicate detection compare every pair?
The architecture uses indexed candidate generation and graph grouping to avoid a naive all-pairs comparison for the full library.
Can I leave a long-running operation?
Background-capable workflows are designed to continue while their progress state remains available when you return.
Continue exploring
Start with the contacts you already have.
Connect a source, review what needs attention, and approve changes with a clear view of ownership.
iPhone & iPad
Mac