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Ai · RAG

RAG System Development Services in Austin Texas

We build retrieval-augmented generation systems grounded in your own documents and data — with citations, evaluation, and guardrails against hallucination.

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What we offer

Our ai services.

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01

Retrieval Pipeline

Chunking · Embeddings · Vector DB

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Retrieval Pipeline

Ingest, clean, chunk, and embed your documents into a vector store tuned for your content — the foundation that decides whether answers are any good.

02

Grounded Answers

Citations · Context · Guardrails

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Grounded Answers

Responses grounded in your sources with citations, plus guardrails that prefer 'I don't know' over a confident hallucination.

03

Evaluation

Relevance · Faithfulness

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Evaluation

We measure retrieval relevance and answer faithfulness with real test sets, so quality is a number you can track, not a vibe.

04

Security & Access

Permissions · PII · Audit

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Security & Access

Per-user document permissions, PII handling, and audit trails so the system only surfaces what each user is allowed to see.

FAQ

Common questions.

What is RAG and why do we need it?
Retrieval-augmented generation grounds an LLM's answers in your own documents and data, so responses are accurate, current, and citable instead of made up from the model's memory.
How do you stop it from hallucinating?
Strong retrieval, citations back to sources, prompts that prefer abstaining when context is missing, and faithfulness evaluations that catch unsupported claims before launch.
What about document permissions and sensitive data?
We enforce per-user access controls at retrieval time, handle PII appropriately, and keep audit trails, so users only ever see what they are authorized to.
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