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Pull from repositories
Bring models straight from Hugging Face and other model repositories. The orchestrator loads, quantizes, and routes them across your GPUs automatically.
Hugging FaceOllamaModel catalog
Enclavia AI Ecosystem
Any model. Your data.
Agents that heal themselves.
Pull models from repositories or upload your own weights, link your data privately, and deploy task-specific agents. When a model hallucinates, the ecosystem stops it, heals it, and returns it to baseline — without ever stopping your pipeline.
Agent fleet — live
enclavia · ecosystemResearch Agent
llama-3.2-1b · repository pull
Document Agent
custom-7b.gguf · uploaded
Compliance Agent
gpt-4o · closed-weight API
Operating within task baseline
Pipeline statusRUNNING — 0 interruptions
Any
Model, size or type
.gguf
+ .safetensors upload
4
Drift classes monitored
0
Pipeline interruptions
Model-agnostic by design
Bring the model. We make it enterprise-ready.
Our agents are model-agnostic. Open weights, fine-tunes, or closed-weight APIs — the ecosystem treats them all as first-class citizens, regardless of size or type.
Upload your own weights
Upload proprietary or fine-tuned models directly — .gguf and .safetensors formats — and keep the weights entirely inside your environment.
.gguf.safetensorsPrivate weights
Connect closed-weight APIs
Frontier API-only models plug into the same ecosystem with the same guardrails and audit trail — hallucination detection and self-healing run at reduced depth.
OpenAIAnthropicAny API endpoint
From model to agent
Your model, your data, your agent — in four steps.
Upload your model, link your data privately and securely within the platform, then create a customized agent for the exact task you need done.
Bring your model
Pull it from a repository, upload the weights, or point us at an API — any size, any type.
Link your data privately
Connect documents and knowledge sources inside your enclave. Nothing leaves your environment, and nothing trains anyone else’s model.
Create a task-specific agent
Combine the model, your data, and your guardrails into a customized agent purpose-built for one job.
Put it to work
Deploy into your pipeline with continuous monitoring — every response verified before release.
Self-healing infrastructureHallucinations stopped.
Hallucinations stopped.
Pipelines never are.
The ecosystem continuously monitors every agent's behavior and accuracy. The moment a hallucination or drift is detected, the affected model is automatically paused and corrected, while the rest of your pipeline keeps running seamlessly. It works across all models and types, regardless of size.
Uninterrupted Operations
Your pipeline continues running flawlessly even if one specific agent is paused for correction.
Strict Guardrail Enforcement
Output tone, formatting, and source references are strictly verified against your custom rules.
Zero-Downtime Remediation
Affected models are autonomously healed and brought back to baseline without engineering intervention.
Proactive Quality Control
Queries and responses are continuously evaluated to ensure 100% faithfulness to your ground truth.
Baseline — operating within approved parameters
Agent Accuracy Score0.19
Continuous Health Monitoring
Output Accuracy18%
Task Alignment11%
13:57:55SYSTEMFleet console active — continuous monitoring enabled
Demo control — safely tests agent behavior against strict guardrails
Coverage, honestly stated
Full depth on open weights. Guarded everywhere else.
Self-healing depends on how deep we can see into a model. We tell you exactly what you get with each.
Open & self-hosted models
Full internal-signal access
- Hallucination detection from internal model signals — before bad output reaches a user
- Autonomous stop-and-heal with steering vectors, no full retraining required
- The original pipeline keeps running while the affected model heals
- Works across all sizes and types — from sub-1B workers to large orchestrators
Closed-weight, API-only models
Fully supported, guarded mode
- Same agents, same guardrails, same audit trail as open-weight models
- Output-level verification on every response before release
- Hallucination detection and self-healing run at reduced depth — internal signals aren’t exposed by the provider
- Mix freely: closed-weight orchestrators with open-weight workers, or the reverse
See the ecosystem run against your own models and data — open weights, uploads, or API-only.
Book a technical walkthrough