Frequently
Asked Questions

Find quick answers to common questions about Asinoids and Asilab Studio. Need more help? Contact our team anytime.

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Picture one ChatGPT as a single “neuron.” An Asinoid links thousands of such GPT-like units into a coordinated, cross-talking network governed by a higher-level cognitive layer—essentially a brain built from many interacting subnets, and that’s only the beginning.

We currently collaborate mainly with industry-leading enterprises, but our roadmap includes progressively expanding access so that Asinoids become available to individual users as well.

You can request private access via our website or by contacting us via email

We model asinoids on the human brain for the same reason the Wright brothers copied birds: biomimicry lets us build on a design refined by millions of years of evolution instead of starting from scratch.

Asinoids self-adapt but remain within deterministic, verifiable code constraints set by their developers. They can be deployed on isolated, air-gapped hardware, eliminating external attack surfaces for high-security use cases.

AI Agents are created by humans. Asinoid on the other hand creates and improves new objectives which could be executed as AI agents themselves. Asinoid is like a human who creates micro agents and integrates them into its own brain, constantly, in real time, non-stop.

Early pilots show the highest ROI in knowledge-dense, time-critical domains such as pharma R&D, financial trading, advanced manufacturing, and large-scale customer operations, where rapid reasoning across heterogeneous data is essential.

You supply curated corpora or streaming data; the Asinoid’s meta-learner allocates specialized subnet “lobes,” then performs continual in-context adaptation. No model re-training is required—configuration is managed via declarative skill plugins and policy files.

REST, gRPC, and Python/TypeScript client libraries expose messaging, memory, and objective-graph endpoints. A WASM extension layer lets you deploy mission-critical skills directly inside the Asinoid runtime with single-digit-millisecond latency.

All embeddings and transient working memory can be encrypted with tenant-specific keys (FIPS-140-3 compliant). Optional differential-privacy layers and on-prem key management preserve confidentiality even during federated learning.

Yes. The governance layer supports data-lineage tracking, consent revocation, and audit-ready logging, enabling turnkey compliance with GDPR, HIPAA, ISO 27001, and SOC 2 Type II.

A built-in observability stack streams objective trees, resource usage, and policy decisions to Prometheus-compatible endpoints, while a formal-methods verifier continuously proves safety invariants against live execution traces.

A single enterprise-grade Asinoid runs efficiently on an 8-GPU DGX-class server (≈7 kW) but scales linearly across multi-node clusters or custom ASIC boards. Edge variants fit into a 24-core CPU box with 2 consumer GPUs for on-site deployment.

Yes. The Studio offers staged environments—dev, test, and prod—each with isolated memory pools and policy scopes. Objectives graduate only after passing automatic red-team and regression test suites.

Minor security and performance patches ship monthly. Major architectural releases follow a quarterly cadence, with in-place hot-swap so uptime remains > 99.95 %.