Research & analysis
Autonomous contract and document auditing
Audit hundreds of contracts, filings or codebases in parallel.
- VM
- GPU
- Store
- Workflows
The scenario
A swarm of agents works through hundreds of structured PDFs, financial filings or codebases at once, and every document gets the same careful read.
How it works
- 1Ahura VM extracts and sanitises the raw text from each file.
- 2Ahura GPU generates high-dimensional embeddings and runs deep reasoning models across the whole set in parallel.
- 3Findings are stored and anything unusual goes to a human for sign-off.
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
01Ahura VM extracts and sanitises the raw text from each file.
queued- Ahura VMvm.runqueued
status: queued · 0 / 24 sandbox runs
- Ahura GPUgpu.trainqueued
status: queued · 0 / 90 GPU-seconds
02Ahura GPU generates high-dimensional embeddings and runs deep reasoning models across the whole set in parallel.
queued- Ahura Storestore.getqueued
status: queued · 0 / 48 objects stored
03Findings are stored and anything unusual goes to a human for sign-off.
queued- Ahura Workflowsworkflow.historyqueued
status: queued · 0 / 36 steps completed
Services in this partition
VM
0 calls · 0.00 ACU
GPU
0 calls · 0.00 ACU
Store
0 calls · 0.00 ACU
Workflows
0 calls · 0.00 ACU
Spend by service
Event log
- Waiting to start.
Why Ahura OS
Why this is better on Ahura OS
Auditing at volume breaks on two things: preparing messy files and running heavy models over all of them. On Ahura OS the swarm splits the work. Ahura VM extracts and sanitises the text in isolated sandboxes, while Ahura GPU generates embeddings and runs deep reasoning models across the whole set in parallel. Results land in storage, anything unusual stops at an approval step, and the GPUs are only billed for the seconds they ran.
What each capability replaces
- Ahura VMInstead of fleets of machines to patch, every run gets its own disposable sandbox, billed only while it executes.
- 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 StoreInstead of managing buckets, permissions and lifecycle rules, objects follow the partition: they freeze, thaw and expire with it.
- Ahura WorkflowsInstead of writing retry, timeout and approval plumbing, you declare the steps and the platform guarantees the behaviour and the history.
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.