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Most companies dump their entire dataset into an LLM—we take a smarter approach. Our agentic RAG solutions selectively retrieve and share only the necessary context with external models, reducing exposure while maintaining accuracy. And if you ever want full privacy, we can help you deploy an on-prem LLM that keeps everything in-house.
Right now, AI is obsessed with bigger models, more data, and limitless scope. But more isn’t always better. Our solutions focus on retrieving only what matters—so your team gets more relevant answers without unnecessary noise.
You don’t have time for long, expensive AI projects that take years to show results. Our solutions are designed for quick integration and rapid impact, so you can start leveraging AI in weeks, not years—without overhauling your entire system.
We connect to your data—you don’t share it with us. Our AI solutions integrate with your existing databases and knowledge sources securely, ensuring that your proprietary information never leaves your environment unless you decide to share specific insights. You stay in control—always.
Answers to questions you might have about Unconventional Wisdom.
Unconventional Wisdom is an AI Engineering Studio that builds secure, agentic AI solutions tailored to your business needs.
We specialize in creating conversational AI applications that integrate with your private data without exposing unnecessary information to external LLMs. Our solutions leverage Retrieval-Augmented Generation (RAG), agentic workflows, and orchestration layers to ensure AI delivers relevant, structured, and actionable insights—all while keeping you in control.
We work with companies that need intelligent AI solutions (mainly for their internal teams) without waiting years for results. Whether you need a custom AI assistant, a database-driven chatbot, or an enterprise AI interface, we design and deploy solutions that work on your terms.
An abstraction and orchestration layer is the intelligent middleware that sits between your data, AI models, and user interactions. It ensures that AI doesn’t blindly access raw data but instead retrieves, structures, and processes it efficiently before generating responses.
• Abstraction – Controls how much and what type of data is shared with an LLM, reducing unnecessary exposure and improving security.
• Orchestration – Manages the flow of AI reasoning, allowing multi-step workflows, tool integrations, and structured decision-making before a final answer is generated.
In practice, this means instead of simply sending user queries to an LLM, we retrieve relevant information, structure reasoning steps, and ensure AI responses are accurate, contextual, and aligned with business needs.
We don’t follow a one-size-fits-all tech stack—instead, we select the best tools and frameworks based on each client’s specific needs. However, our core technology choices include:
• Python – Our primary language for AI development.
• FastAPI – For building high-performance, scalable backends.
• LangGraph & LangSmith – For agentic workflows, structured reasoning, and AI monitoring.
• Ollama – For running and managing local LLMs when privacy is a priority.
• Hetzner – Our preferred infrastructure provider for reliable and cost-effective cloud hosting.
This adaptable approach ensures that we always deliver the most efficient and tailored AI solutions
No, we are not limited to a single LLM provider. Since most of our workflows and agentic systems are built using LangChain, we support all LLMs that LangChain integrates with, giving our clients flexibility in model selection.
If you require a specific model that is not natively supported by LangChain, we can explore alternative solutions, including custom integrations using Ollama for local or specialized deployments. Our goal is to ensure you have the right AI model for your needs, whether hosted externally or deployed privately
Our process is structured into three key phases to ensure a seamless and effective implementation:
1. Discovery Phase – We collaborate with our clients to understand their specific challenges and assess whether our AI solutions are the right fit. If so, we evaluate the data sources that will serve as the application’s knowledge base, ensuring they are sufficient to meet expectations.
2. Development & Implementation Phase – In this stage, we design, build, and refine the conversational AI application. Early testing and client feedback are integral to fine-tuning the solution before full deployment.
3. Deployment Phase – We handle infrastructure provisioning and application deployment, aligning with the client’s operational model to ensure a smooth transition into production.