Parcourir les catégories
Explorer
Fiverr Pro
Français
$
USD
I will build your corporate rag system and institutional ai knowledge base
Ajilo Myde
À propos de ce service
Your enterprise AI is hallucinating. The problem isn't your data it's your RAG architecture.
Standard Vector Database implementations fail at enterprise scale for three reasons: poor chunking strategy, weak retrieval logic, and no source verification. The result is an AI that sounds confident and is factually wrong. In a corporate environment, that's a liability.
As an AI Engineer, I build Institutional Knowledge Bases with Custom LLM Grounding RAG systems that cite sources, respect access controls, and never expose restricted data.
What I build:
- Institutional RAG multi-source ingestion, precision chunking, verified retrieval
- Vector Database pgvector, Pinecone, or Weaviate matched to your sovereignty needs
- LangChain / LangGraph multi-step reasoning, tool-calling, agent orchestration
- MCP integration live connection to your databases, CRMs, and document systems
- Data Sovereignty AI self-hosted, your data never used to train public models
Your corporate AI cites sources. Your legal team approves the architecture. Employees trust the output.
Message me with your data sources and compliance requirements I'll audit your RAG readiness in 24 hours.
Découvrez Ajilo Myde
Ajilo Myde
Enterprise AI and Systems Architect
- DeNigeria
- Membre depuismai 2026
- Temps de réponse moy.1 heure
Langues
Anglais, Allemand, Français, Espagnol
I implement AI & automation for established companies, not startup MVPs. Internal enterprise system.
Four categories for operations, IT, & executive teams:
→ Corporate RAG: institutional knowledge base, Data Sovereignty AI, LangChain, MCP
→ Legacy Modernization: Microsoft Access, desktop apps, ERP migration to Supabase cloud
→ Internal Automation: n8n Enterprise, approval workflows, ERP sync, compliance trails
→ API Design & Integration: RESTful API architecture, enterprise integration, webhooks
Proprietary data. Compliance requirements. Production-grade. Message me with your current system.
Mon portfolio
Autres services de Développement de logiciels I Offre
FAQ
What makes an institutional RAG system different from a standard one?
Standard RAG retrieves the closest text chunks. Institutional RAG verifies source authority, respects document-level access permissions, and returns cited responses with traceable provenance. The difference matters when your AI is advising decisions, not just answering questions.
How do you ensure our proprietary data isn't used to train public AI models?
I build self-hosted deployments — your data never passes through OpenAI's or Anthropic's training pipelines. API calls for inference only, not training. For maximum sovereignty, I deploy open-source models on your own infrastructure, eliminating third-party data exposure entirely.
What is MCP and how does it connect to our internal systems?
MCP (Model Context Protocol) allows your AI to query live internal systems — databases, CRMs, document repositories — in real time rather than relying on a static index. The AI reads current data rather than last week's snapshot. I use MCP to connect your corporate brain to production systems.
Which vector database should we use — pgvector, Pinecone, or Weaviate?
pgvector on Supabase for teams requiring full data sovereignty and self-hosted control. Pinecone for high-scale retrieval without managing infrastructure. Weaviate for multi-modal enterprise builds. I select based on your compliance requirements, query volume, and deployment preference.
How do you handle access controls for multi-department knowledge bases?
I implement document-level access policies using Supabase Row Level Security — each department's documents are only retrievable by authorized users. The AI cannot surface restricted content to unauthorized queries, regardless of how the question is phrased.
