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AI Support Agents Need More Than RAG
A lot of AI support agents are built like this:
User asks → retrieve documents → send context to LLM → generate answer.
That works for a demo.
It gets much harder in production.
A reliable AI support agent needs:
→ Good retrieval and chunking
→ Permission-aware knowledge access
→ Conversation memory
→ Source-grounded responses
→ Fallbacks when information isn't available
→ Evaluation of retrieval + answer quality
→ Monitoring for hallucinations and failed queries
The LLM is only one part of the system.
I’ve been working with Python, Django, LangGraph, RAG, PostgreSQL and LLM APIs to build AI agents that are designed around the actual business workflow—not just a chatbot UI.
The interesting part isn't making an AI answer.
It's making sure it answers correctly, consistently, and within the right boundaries.
#AI #RAG #LangGraph #AIAgents #Python #Django #Automation #SaaS
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Interesting!
A government just hired us to build AI that screens foreign investments for national security risks.
Here's what happened.
Ukraine's government launched a program funded by a UK grant to modernize how it evaluates foreign investment risk. They needed an AI engine that could estimate financial risk across multiple variables, fast and at scale.
We're excited to be part of that transition.
For us this is a different kind of project. We've built 40+ AI and automation systems for founders and startups across the US, UK, and Scandinavia.
But a government choosing you to build something this sensitive says one thing clearly: the work you did before was trusted enough to bet national policy on.
Here's what I'm noticing.
Governments are no longer "exploring AI." They are buying. They are shipping. They are putting it into decision-making pipelines that actually matter.
That's the shift.
If you've been building real AI systems for real businesses, your next client might not be a startup.
It might be a country.
