You will get a RAG Readiness Audit with Indexing Risk Assessment
Project details
Choose this if: you haven’t indexed yet (or your RAG is unstable) and you want a risk assessment + go/no-go before building or rebuilding an index.
I assess whether your data is truly ready for RAG or semantic search. This audit evaluates structure, chunking, metadata, and embedding risks, and identifies failure modes before you invest in building or indexing.
This service does not build indexes; it determines whether indexing should be performed and which approach is appropriate.
Deliverables:
• Go / Conditional / No-Go verdict
• Risk table (structure, chunking, metadata, embedding failure modes)
• Remediation plan (prioritized fixes + recommended indexing approach)
This audit can be performed before any indexing is built or on an existing RAG or vector pipeline. The goal is to identify structural, chunking, metadata, or embedding risks before you waste time or money indexing the wrong thing.
Next steps: After audit approval, proceed to Production-Ready FAISS Index (no compliance) or Large-Scale Semantic Indexing (audit-grade validation).
I assess whether your data is truly ready for RAG or semantic search. This audit evaluates structure, chunking, metadata, and embedding risks, and identifies failure modes before you invest in building or indexing.
This service does not build indexes; it determines whether indexing should be performed and which approach is appropriate.
Deliverables:
• Go / Conditional / No-Go verdict
• Risk table (structure, chunking, metadata, embedding failure modes)
• Remediation plan (prioritized fixes + recommended indexing approach)
This audit can be performed before any indexing is built or on an existing RAG or vector pipeline. The goal is to identify structural, chunking, metadata, or embedding risks before you waste time or money indexing the wrong thing.
Next steps: After audit approval, proceed to Production-Ready FAISS Index (no compliance) or Large-Scale Semantic Indexing (audit-grade validation).
Machine Learning Tools
BERT, NLTK, NumPy, pandas, Python, PyTorch, TextBlobWhat's included $75
These options are included with the project scope.
$75
- Delivery Time 2 days
- Number of Revisions 1
- Model Documentation
Optional add-ons
You can add these on the next page.
Fast 1 Day Delivery
+$30
Additional Revision
+$25
Action Plan
+$50
Post-Fix Recheck
+$75
Full Dataset Pass
+$150Frequently asked questions
About John
Semantic Indexing Engineer | RAG Data Pipelines | FAISS + e5-large-v2
Poughkeepsie, United States - 7:25 am local time
I transform raw text into structured vector datasets using semantic chunking, dense embeddings, FAISS indexing, and metadata alignment — with validation so retrieval stays reliable over time. Clients use my indexes to power document Q&A, compliance search, knowledge base retrieval, and research discovery — so teams stop searching and start finding answers.
✅ What I Deliver
- RAG readiness audits + deployment prep for production launch
- Production-ready semantic indexing (FAISS + embeddings)
- Large-scale indexing with validation thresholds
- Framework-ready outputs (LangChain, LlamaIndex, Haystack compatible)
📊 Proof
- Indexed and validated 100+ datasets across legal, regulatory, scientific, and general knowledge domains
- Applied methodology across multiple research organizations
- Delivered auditable handoff packages (corpora, FAISS indexes, metadata, summaries)
🔍 How Reliability Is Verified
- Index loads successfully
- Vector count matches chunk count
- Vector–chunk alignment + dimensional integrity checks
🧰 Core Stack
- FAISS • e5-large-v2 • Python • semantic chunking • embeddings • retrieval validation
- Compatible with: LangChain • LlamaIndex • Haystack • pgvector • Pinecone
If your team needs results that don't break in production, I'll deliver the indexing stack you wish came prebuilt.
Steps for completing your project
After purchasing the project, send requirements so John can start the project.
Delivery time starts when John receives requirements from you.
John works on your project following the steps below.
Revisions may occur after the delivery date.
Dataset intake & scope confirmation
Review data access, format, size, and intended use case.
RAG readiness analysis
Evaluate structure, chunking strategy, metadata completeness, and embedding suitability.