Solutions / AI Automation

AI answers grounded in your actual documents, not a guess.

Retrieval-augmented generation is the difference between an AI that knows your policy and one that invents a plausible version of it.

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A general-purpose AI model does not know your product catalog, your support documentation, or your internal policies — and left alone, it will confidently guess rather than admit it does not know. RAG systems fix that by retrieving the actual relevant document or record before the model answers, so responses are grounded in what you actually wrote, not what sounds right.

Signs This Is Worth a Conversation

Support or internal answers need to be grounded in specific documentation, not general knowledge

A product catalog or knowledge base is too large for anyone to search manually with confidence

You have tried a general AI tool for internal questions and gotten confidently wrong answers

What We Do

A retrieval pipeline built on your real content

Documentation, product data, or policies indexed and kept current, not a one-time snapshot.

Answers grounded with citations

Responses reference the actual source, so accuracy can be checked, not just trusted.

Kept in sync with your source content

When the underlying documents change, the system reflects it, not a stale copy from launch day.

Proof, Not Promises

The same catalog and data-relationship work behind our large-scale ecommerce projects — 30,000+ pieces of equipment, 200,000+ parts — is exactly the kind of structured content a RAG system needs to be built on well.

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How It Works

01

Structure your content for retrieval

Not every document is ready to be searched accurately as-is.

02

Build the retrieval and generation pipeline

Tuned so the right document gets found, and the model sticks to it.

03

Test against real, specific questions

Not generic ones — the edge cases are where ungrounded answers usually show up.

Why Prioxo for This

We have already solved the harder version of this problem — making a huge, deeply relational catalog fast and query-able. Making it AI-searchable and accurately grounded is the same discipline applied one layer up.

Tired of AI tools confidently guessing at your documentation?

Let us ground the answers in what you actually wrote.

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