RAG Solutions
Retrieval-augmented generation grounds an LLM's answers in your actual documents, tickets, or wikis instead of its training data — so users get accurate, sourced answers from your knowledge base rather than plausible-sounding guesses.
0%
Avg. Answer Accuracy
10k+
Documents Indexed (Typical)
5-9 wks
Typical Engagement
Always Included
Source Citations
What's Included
RAG pipeline with retrieval and generation stages
Vector index of your knowledge base
Source citation on every generated answer
How We Work
01
Knowledge Audit
We assess your documents, wikis, and data sources for structure, freshness, and quality.
02
Retrieval Pipeline Build
We build the chunking, embedding, and retrieval pipeline tuned for your content type.
03
Grounded Generation
Responses are generated strictly from retrieved context with source citations attached.
04
Evaluation & Launch
We test against real questions to confirm accuracy before rolling out to users.
What You'll Receive
- RAG pipeline with retrieval and generation stages
- Vector index of your knowledge base
- Source citation on every generated answer
- Accuracy evaluation report
- Content update pipeline for keeping the index fresh
How Engagements Typically Work
Every project starts with scoping — here's the shape most engagements for this service take.
Proof of Concept
A 2-3 week build against a sample of your documents to validate retrieval accuracy.
Production Build
Full pipeline covering your complete knowledge base — typically 5-9 weeks.
Ongoing Index Maintenance
Monthly retainer to keep the index current as documents are added or updated.
Technologies We Use
The tools and platforms our RAG Solutions team works in day to day.
RAG Stack
Infra
Frequently Asked Questions
Generation is constrained to retrieved context and configured to say 'not found' rather than guess when no relevant source exists.
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