| What it does | AutoML that just works. Keep training local.
| Execution governance for consequential AI-agent operations
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| Stage | MVP
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| Founded | Not given
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| Based in | Not given
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| Pricing model | Free trial
| Freemium
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| Pricing | Free beta with no billing. Paid plans launching in the future; pricing not yet announced. Join the waitlist for paid plans without payment or purchase commitment.
| Free engine (MIT/Apache-2.0 open source). Team tier: €1,900/month including 25 writing seats and 50,000 governed actions per month; overage €0.02 per action above 50k monthly. Enterprise: from €90,000/year. Design partner program: €18,000 for 90 days (credited against first annual contract). Add-ons
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| Key features | - Python SDK for tuning suggestions
- Local model training with remote suggestions
- Supports classification and regression
- Included CPU demo workloads
- Efficient observation budget allocation
- Model ranking and selection
- Reference scheduler implementations
- HTTPS secure communication
| - Authority revalidation at dispatch time against live conditions
- Approval binding to canonical intent with single-use, time-bound enforcement
- Sequence and prerequisite policy enforcement at plan time and runtime
- Effect identity matching to prevent duplicate actions across runs
- HMAC-signed, hash-chained, and Ed25519-anchored evidence
- Cross-session capacity ledger for aggregate exposure control
- Verifiers in five languages for independent offline verification
- MCP server support for integration with Claude Code, Cursor, and other agents
- Air-gapped deployment with documented key ceremony on Enterprise
- Deterministic Rust enforcement engine with no models in the decision path
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| What makes it different | - Trains locally, not on the service—your models and data stay on your computer
- Efficient tuning using only half the observation budget of full training
- Outperforms reference schedulers in benchmarks on diverse datasets
- Free beta with no billing or account costs
| - Authority is revalidated at the moment of execution, not just at approval time
- Signed, verifiable evidence allows third-party audit without trusting the vendor
- Deterministic enforcement with no AI models influencing the gate decision
- Pricing on governed actions, not seats, aligned with consequence not headcount
- Refusals and verification are never metered or charged
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | Not checked yet ● no change since last check |
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| Trust Flow (Majestic) | 3 | 0 |
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| Citation Flow (Majestic) | 13 | 0 |
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| Referring domains (Majestic) | 2 | 0 |
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hyperopt.dev
Rigorix