Which intelligent systems service should we start with?

Usually the one closest to a workflow that already costs you time. For questions over documents that is RAG, for repeated multi-step work it is an agent or automation, and for adding AI to an existing product it is an API integration. We help you choose after looking at the workflow.

Can the AI work with our private data safely?

Yes, when access is designed properly. The system only sees the data a task needs, runs through your own accounts where possible, and sensitive fields can be masked before anything reaches a model provider.

How do you stop the AI from giving wrong answers?

Retrieval grounds answers in your own sources, responses can be required to cite them, and a set of real questions is run before every release. When confidence is low the system says so or hands over to a person.

Which models and providers do you use?

Claude, OpenAI and open-source models, chosen per task. We keep the integration provider-neutral where we can, so you can switch models later without rebuilding the product.

What does it cost to run once it is live?

It depends on volume and model choice, so we estimate it during scoping using your expected usage. Caching, smaller models for simple steps and usage limits keep running costs under control.

Intelligent Systems Services

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Intelligent Systems

Tech Team 4U

AI that works inside real operations

Agents, retrieval, model integrations and deployment. Every card below links to its own service, and most projects combine two or three of them.

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Agent architecture with clear control

Private data, internal APIs, and approval gates are mapped before the model is allowed to act inside your workflow.

Core AI Stack & Frameworks:

Claude 3.5 Sonnet OpenAI GPT-4o LangGraph Pinecone PostgreSQL pgvector LlamaIndex

AI Product & Agents

Domain-specific AI products, controlled tool-calling loops, and multi-agent coordination.

Claude & OpenAI Integration

LLM API integration with clear JSON schemas, fallbacks, and safe model routing.

Claude API Integration OpenAI Integration AI Integration

Enterprise RAG & Vector

Hybrid search, chunking rules, and vector indexing for fast client document answers.

RAG Architecture Vector Search LangChain / LangGraph

ML Deployment & Chatbots

Containerized inference scaling on AWS/GCP, custom co-pilots, and prompt guardrails.

ML Model Deployment Custom AI Chatbot AI Automation

How we build AI systems

Limits first

We decide what the model can read, change and send before it touches anything live.

Measured quality

Answers are scored against real examples, so a prompt change that makes things worse is caught before users see it.

Known costs

Token spend is tracked per feature, with caching and model routing so the monthly bill is predictable.

People in the loop

Risky actions wait for a person to approve them, and every decision the system makes is logged.