Enterprise AI Architecture
AI Architecture
Beyond Wrapper Apps
We architect production-grade AI systems that own your data, not rent it.
While others sell chatbot integrations, we design the infrastructure layer — vector stores, retrieval pipelines, private model deployments, and autonomous agents — and work alongside your engineers to get it running.
System Architecture
What We Actually Build
Your Data Sources
Documents, databases, APIs, knowledge bases
Intelligence Layer
This is what we build. Custom retrieval, embedding pipelines, and orchestration logic.
Vector Store
pgvector / Pinecone
LLM Layer
vLLM / Claude API
Orchestration
LangChain / Custom
Production Results
Grounded answers, autonomous actions, workflow execution
Standard AI models do not know your business. They hallucinate because they lack context about your documents, processes, and domain knowledge.
Context-Aware Retrieval Systems
Advanced RAG
We design the vector stores and retrieval pipelines that ground AI responses in your actual data, and specify the embeddings, chunking strategy, and retrieval logic for your use case.
The Deliverable
A Private Oracle for your internal knowledge
Tech Stack
Sending sensitive financial data, customer PII, or proprietary IP to OpenAI or Anthropic is a compliance and security risk your legal team will not approve.
Local & Private Model Deployment
The Security Play
We select and size open-source models (Llama, Mistral, Qwen) for your own VPC or on-premise infrastructure, and work with your engineers through quantization and rollout. Full control over your inference layer.
The Deliverable
AI infrastructure that never leaves your network
Tech Stack
Chatbots just talk. They answer questions but cannot take action. Your operations need AI that executes multi-step workflows autonomously.
Agentic Workflows
Beyond Chatbots
We design agents that read emails, query databases, update CRMs, draft documents, and coordinate across systems — with the human-in-the-loop guardrails that keep them reliable.
The Deliverable
Autonomous Loops for Operations
Tech Stack
Anti-Commodity Positioning
What We Don't Build
Generic support chatbots
Use Intercom, Zendesk AI, or Freshdesk. These are commodity products.
“Prompt engineering” workshops
YouTube is free. We build systems, not slide decks.
Thin wrappers around OpenAI APIs
Any developer can call an API. We build the infrastructure around it.
We build assets—proprietary code, custom pipelines, and production infrastructure that add value to your company's IP. Not temporary fixes that disappear when the vendor changes their pricing.
Is This Right For You?
Good fit if:
- ✓You have proprietary data that makes generic AI useless
- ✓Security/compliance prevents using third-party AI services
- ✓You need AI that takes action, not just answers questions
- ✓Engineering team exists but lacks AI/ML expertise
Not a fit if:
- —You just want a chatbot on your website
- —No internal engineering capacity to maintain systems
- —Looking for a quick demo without production intent
- —Need off-the-shelf solutions, not custom architecture
Common Questions
Architecture, Answered
What is RAG, and when do you actually need it?
Retrieval-augmented generation grounds a model's answers in your own documents by retrieving relevant passages at query time instead of relying on training data. You need it when answers must reflect private, current, or frequently changing information — internal policies, contracts, product documentation — and when you need to show where an answer came from.
Should we run open-source models on our own infrastructure?
Consider it when sensitive data cannot leave your network, or when inference volume is high enough that per-token API pricing dominates your costs. Smaller quantized models running in your own VPC give you fixed infrastructure costs and full data control, at the price of running the deployment yourself.
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent takes actions: it calls tools, queries databases, updates records, and coordinates multi-step work across systems. Agents need guardrails a chatbot does not — permission boundaries, human approval steps, and retry logic — because their mistakes change state rather than just producing a bad sentence.
Why does AI cost more in production than in the pilot?
Pilots run on small volumes and forgiving prompts. Production adds retries, longer context windows, evaluation runs, and traffic that scales with users. Cost per successful task, not cost per call, is the number that matters — and it only becomes visible once real usage patterns and failure rates show up.
Ready to build real infrastructure?
Skip the discovery call theater. Send us your architecture problem and we will tell you if we can help.
Describe Your ProblemOr email directly: info@inuxo.com