The governed data layer for AI agents.
An S3-compatible bucket your agents connect to over MCP, with a live fork of production they can safely work on, zero egress on every read, per-agent access control, and a signed record of everything they touch.
Real data.
Zero production risk.
A live, governed copy of production your agents can branch in seconds. Work in isolation, promote what's good, or roll back to any point in time. Train, test, and experiment freely; production stays untouched.
Zero egress by default.
Tune for speed when you need it.
Read-heavy agents stay on a zero-egress mix, so automated reads never run up a bill. Latency-sensitive ones optimize for speed across the providers closest to their workloads, set by their credentials.
Each credential sets its own point on the dial. No surprise read bills, no one-size-fits-all trade-off.
The first governed S3-compatible storage for AI: every agent request authenticated, every action scoped by policy, every decision traceable via MCP.
Every file your AI touched.
Signed and on record.
One line of code gives you a complete, immutable record of every file your AI touches. Signed, searchable, and ready when the audit question arrives.
Multi-Cloud Architecture
How a single file becomes private, provider-agnostic, and always available.
Mixed for resilience·Pinned to your regions·GDPR-safe everywhere
Your data is encrypted before it ever leaves your device.
Files are split and erasure-coded. Each shard alone is meaningless but fully recoverable.
Shards are stored across your chosen jurisdictions, ensuring compliance and resilience.
Files are decrypted only for verified users. Every reconstruction is secure and auditable.
Keep your S3 tools. Change one line.
IronShard is S3-compatible: your existing SDKs, scripts, and tools work by swapping a single endpoint.
Already have data? Import it over the S3 API.
import boto3 # The only change — swap the endpoint URL s3 = boto3.client( "s3", endpoint_url="https://s3.amazonaws.com", ) # Everything else stays the same s3.download_file("my-bucket", "datasets/dataset.parquet", "local.parquet")
Start over MCP or by hand.
Let your agent set it up
Connect, sign in once, and let your agent provision the optimal bucket for your workload.
- 1Connect & authenticateAdd the IronShard connector to your agent and sign in once:Or choose IronShard from your agent's MCP registry (e.g. Claude > Connectors).
- 2PromptOnce connected, ask your agent:
Connect to IronShard over MCP and provision an isolated sandbox bucket for AI experimentation, with zero-egress reads and a signed audit trail I can verify.
IronShard is discoverable over MCP, so an agent can find it, evaluate it, and provision storage end to end on its own. Read the docs here.
Set it up yourself
Create a bucket, point your S3 SDK at it, and start building. Your tools, your control.
Early access →