ZYRABIT

Your Artificial Intelligence.
Your Infrastructure.
Predictable Costs.

Reduce public cloud dependency for workloads where privacy and control are critical. AI Agents that run locally on your infrastructure, without mandatory external calls.

Small Language Models:
A Pragmatic Approach.

For many business problems, 1B–8B parameter models offer better efficiency, control, and cost predictability.

SLM (Small Language Models)

Zyrabit Core
  • Parameters: 1B - 8B
  • Hardware: Consumer GPU / Mac M1+
  • Privacy: 100% On-Premise
  • Cost: No per-token fees

LLM (Large Language Models)

Proprietary Cloud
  • Parameters: 70B - 1T+
  • Hardware: Massive Data Centers
  • Privacy: Third Parties (US/China)
  • Cost: Pay per Token

Open Source, Modular & Auditable Architecture

Designed for on-premise and edge deployments.

API Layer (FastAPI)
@app.post("/v1/chat") async def chat(request: ChatRequest): # Interceptamos para sanitización sanitized = gatekeeper.clean(request) return await brain.execute(sanitized)
RAG Pipeline
# Vector Retrieval context = vector_db.search(query) prompt = f"""Context: {context} User: {query}""" # Local Inference response = slm.generate(prompt)
Secure Agent
class SecureAgent: def sanitize(self, text): # Regex + NER for PII return pii_layer.redact(text)

Assisted AI: Human Control.

Our agents don't make final decisions: they assist, recommend, and document. Control remains in human hands.

The system prompt defines operational policies: limits, tone, priorities, and agent security criteria.

# Master Prompt Tip:

"Act as a Senior Systems Engineer. Prioritize security over speed. If you don't have context in the RAG, say you don't know."

Open Core vs Enterprise

Our core technology is free. Enterprise tools give you total control.

Zyrabit Community

Community Support

  • Open Source Models (Llama, Mistral)
  • Basic RAG
  • MIT License
View on GitHub

Zyrabit Enterprise

24/7 Support & SLA

Schedule Demo