Orion Corps // Automation // Custom AI & LLM Integration
TRAIN.DEPLOY.SCALE.
Train and deploy custom AI models securely on your enterprise data. We deliver expert enterprise LLM integration services with zero data leaks.
On premise security through private AI model deployment.
Public models expose your data. We provide enterprise LLM integration services to engineer private AI models that run on your infrastructure, querying your proprietary databases safely without ever sending sensitive records to third-party endpoints.
Data privacy with custom RAG architecture.
Enterprise knowledge retrieval with semantic document search solutions.
DATA INGESTION & AUDIT
WE AGGREGATE YOUR COMPANY'S PRIVATE DOCUMENTS, TICKETS, AND DATABASES SECURELY.
VECTOR EMBEDDING ENGINEERING
DOCUMENTS ARE CONVERTED INTO HIGH-DIMENSIONAL VECTORS TO CAPTURE DEEP SEMANTIC MEANING.
VECTOR STORE SET-UP
VECTORS ARE STORED IN A HIGH-PERFORMANCE CLOUD DATABASE CONFIGURED TO AUTOMATICALLY SCALE.
RAG PIPELINE OPTIMIZATION
WE ASSEMBLE STABLE RETRIEVAL LOGIC TO PULL RELEVANT CONTEXT AND MITIGATE HALLUCINATIONS.
DETERMINISTIC INFERENCE DEPLOYMENT
AI GENERATES RELIABLE ANSWERS PURELY FROM YOUR INTERNAL DATA WITH HIGH ACCURACY.
Common Questions
Straight answers. No sales language.
WILL THE AI ENGINE TRAIN ON OUR PRIVATE INTELLECTUAL PROPERTY OR COMPANY DATA?
NO. WE USE ENTERPRISE API ENDPOINTS WITH ZERO DATA RETENTION (ZDR) COMPLIANCE. YOUR PROMPTS, DOCUMENTS, AND DATABASE QUERIES ARE NEVER USED FOR MODEL TRAINING OR SHARED OUTSIDE YOUR ENVIRONMENT.
WHAT IS RAG ARCHITECTURE AND HOW DOES IT MITIGATE HALLUCINATIONS?
RETRIEVAL-AUGMENTED GENERATION (RAG) RESTRICTS THE LLM TO ANSWERING SOLELY BASED ON SPECIFIC PARAGRAPHS RETRIEVED MATHEMATICALLY FROM YOUR DATABASES. IF THE REQUESTED DATA IS NOT PRESENT, THE SYSTEM DEFERS RATHER THAN FABRICATING AN ANSWER.
WHICH FOUNDATION MODELS DO YOU SUPPORT FOR ENTERPRISE DEPLOYMENTS?
WE ARE COMPLETELY MODEL-AGNOSTIC. WE INTEGRATE WITH CLOUD APIs LIKE OPENAI GPT-4O, ANTHROPIC CLAUDE 3.5, OR GOOGLE GEMINI 1.5, OR WE HOST PRIVATELY DEPLOYED OPEN-SOURCE MODELS (E.G., LLA3 / MIXTRAL) ON YOUR OWN GPU SERVERS.
HOW LONG DOES IT TYPICALLY TAKE TO INGEST DATA AND DEPLOY A CUSTOM LLM SYSTEM?
STANDARD PRODUCTION-GRADE LLM PIPELINES INTEGRATION AND VECTOR EMBEDDINGS DEPLOYMENT GENERALLY TAKE 4 TO 6 WEEKS, INCLUDING THOROUGH LATENCY AND ACCURACY BENCHMARKING.
HOW CAN ENTERPRISE SEARCH AND SEMANTIC DOCUMENT SEARCH REDUCE EMPLOYEE SEARCH TIME?
BY INDEXING COMPLICATED MULTI-FORMAT ARCHIVES (PDFS, WIKIS, CRM LOGS) INTO A CENTRALIZED VECTOR DATABASE, EMPLOYEES CAN QUERY INFORMATION CONVERSATIONALLY AND RETRIEVE TARGETED PAGES AND ANSWERS IN SECONDS, REMOVING MANUAL SEARCH FRICTION.
WHAT ARE THE MAIN SIGNS A BUSINESS ENTERPRISE NEEDS PRIVATE VECTOR DATABASES AND LLM INTELLIGENCE?
IF INTERNAL DEPARTMENTS WASTE SIGNIFICANT TIME SEARCHING FOR REPETITIVE SERVICE MANUALS, CONTRACT DISCREPANCIES, OR TECHNICAL DOCUMENTS, OR IF RESOLUTION TIME FOR CUSTOMER CORRESPONDENCE EXCEEDS COMPLIANCE SLAS, CUSTOM AI IS A MISSION-CRITICAL REQUIREMENT.
Production AI engineering from a specialized LLM engineering agency.
Tell us about the proprietary data you want to query. We will outline the exact roadmap for enterprise LLM integration services that won't expose your IP.
// Capabilities Index