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
- 1Records are embedded and compared.
- 2Matches are linked in a graph.
- 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
- Provisioning
- Processing
- Complete
01Records are embedded and compared.
queued- Ahura AIai.inferqueued
status: queued · 0 / 150 model calls
- Ahura GPUgpu.trainqueued
status: queued · 0 / 90 GPU-seconds
02Matches are linked in a graph.
queued- Ahura Graphgraph.edge.putqueued
status: queued · 0 / 380 edges recorded
- Ahura NoSQLnosql.queryqueued
status: queued · 0 / 600 records written
03Confirmed merges are written back.
queued- Ahura Searchsearch.knnqueued
status: queued · 0 / 520 documents indexed
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
Rules that break on messy data, or a data team manually merging records for weeks.
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
Industries this applies to
More: all use cases, for researchers, for developers, pricing.

