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Data & knowledge

Entity resolution

Work out which records are the same thing.

  • AI
  • GPU
  • Graph
  • NoSQL
  • Search

The scenario

An agent merges duplicate customers, companies or products across messy sources.

How it works

  1. 1Records are embedded and compared.
  2. 2Matches are linked in a graph.
  3. 3Confirmed merges are written back.

An illustrative scenario, not a customer story.

Interactive simulation

Simulate this case study

Watch the partition come up, each step run, and usage build as it goes. Simulated: volumes, timings and prices are illustrative.

Partition

—

Status

not created

Elapsed

00:00

Overall0%
  1. Provisioning
  2. Processing
  3. Complete

01Records are embedded and compared.

queued
  • Ahura AIqueued

    status: queued · 0 / 150 model calls

  • Ahura GPUqueued

    status: queued · 0 / 90 GPU-seconds

02Matches are linked in a graph.

queued
  • Ahura Graphqueued

    status: queued · 0 / 380 edges recorded

  • Ahura NoSQLqueued

    status: queued · 0 / 600 records written

03Confirmed merges are written back.

queued
  • Ahura Searchqueued

    status: queued · 0 / 520 documents indexed

Not createdus-eastRetention hold
Run time
00:00
0 calls
Size
2.3MB
0.00 ACU used

Services in this partition

  • AI

    0 calls · 0.00 ACU

  • GPU

    0 calls · 0.00 ACU

  • Graph

    0 calls · 0.00 ACU

  • NoSQL

    0 calls · 0.00 ACU

  • Search

    0 calls · 0.00 ACU

Spend by service

Event log

  • Waiting to start.

Why Ahura OS

Why this is better on Ahura OS

Deciding that two messy records are the same thing is where rules break and analysts burn weeks. Ahura OS compares records by similarity, links likely matches in a graph and sends only the uncertain pairs for review. Entire datasets are compared in parallel, and every merge is a recorded link, so a wrong merge can be traced and undone rather than discovered months later.

What each capability replaces

  • Ahura AIInstead of a model gateway and several vendor contracts, one priced call reaches several models and chooses a cheap one when it can.
  • Ahura GPUInstead of renting accelerators by the hour for inference or training, hardware starts when an agent calls it, hot models load in under a second and the bill stops when the work does.
  • Ahura GraphInstead of standing up and operating a graph database, relationships live beside your other state at the same price and isolation.
  • Ahura NoSQLInstead of designing tables and maintaining indexes, you write documents and query them. Indexes are looked after for you.
  • Ahura SearchInstead of running a search cluster, a managed, high-performance search engine indexes your content on write and queries it fast.

And on every use case

  • One endpoint, not a stack

    Memory, storage, compute, inference, identity and messaging share one connection, one identity and one bill. The integration work you would normally own disappears.

  • Isolation by construction

    Every agent’s world is a partition. Separation is how the platform is built, not a convention teams must remember to follow.

  • Cost follows use

    Pay per call and per millisecond. Idle agents freeze to archive pricing. There is no capacity to reserve and nothing running unseen.

  • Retention and deletion built in

    Lifetimes, expiry, freezing and destruction are platform features, so your data policy is enforced rather than aspirational.

  • Safe autonomy

    Spending limits, protected signing keys, approval steps and signed actions let agents work unattended without being unaccountable.

  • Ready for audit

    Designed against SOC 2 and ISO 27001 controls, with every call metered, recorded and attributable.

Compared

The old way of doing things

Today

Rules that break on messy data, or a data team manually merging records for weeks.

Cheaper with Ahura OSSimilarity does the heavy lifting; people only review the uncertain pairs.
Faster with Ahura OSWhole datasets are compared in parallel, not record by record.
Less risk with Ahura OSMerges are recorded and linked, so a wrong merge can be traced and undone.

Data retention is built in

Everything this scenario stores lives in one isolated partition with a lifetime you set. Expired data deletes itself, a paused project freezes to low-cost archive, and a finished one can be destroyed completely. Security and compliance