Dropp Cortex builds AI agents and retrieval-augmented systems that are grounded in your own data, not just a model's general training.
Plan Your AI TransformationAn AI agent that hallucinates an answer is worse than no agent at all, which is why retrieval-augmented generation (RAG) matters: it grounds a model's answers in your actual documents, systems, and data instead of letting it guess. We build agents and RAG systems for specific, well-scoped tasks — internal knowledge search, customer support triage, structured data lookup — rather than open-ended “do anything” agents that are hard to trust in production. Every system we build includes a clear answer to the question of where the agent's information actually comes from.
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Define exactly what the agent should — and shouldn't — be trusted to do.
Index your documents, systems, and data so answers are retrieved, not invented.
Implement the agent or RAG pipeline against that grounded knowledge base.
Test accuracy against real questions before it reaches a real user.
Track accuracy and failure cases in production, and keep the knowledge base current.
By grounding every answer in retrieval from your actual documents and data, and being explicit about what happens when nothing relevant is found — it says so, instead of guessing.
Yes — indexing your existing knowledge base is usually the starting point, not a separate project.
Yes, for well-scoped tasks — but we design the boundaries of what an agent can do deliberately, rather than giving it open-ended access.
A focused, single-purpose agent or RAG system is typically a matter of weeks, not months — scope is what keeps it fast.