Fix Your RAG System's
Retrieval & Grounding Issues
Done-For-You RAG Evaluation Package
Your RAG system retrieves the wrong documents. Hallucinates answers. Gets 60% accuracy when you need 95%+. We'll diagnose every issue and give you a clear optimization roadmap—delivered in 2 weeks.
Is Your RAG System Struggling?
❌ Low Retrieval Accuracy
Fetching irrelevant documents. Missing the right ones.
❌ Hallucinated Answers
LLM makes up info despite having the right context.
❌ Poor Grounding
Answers don't cite sources or deviate from context.
❌ Inconsistent Quality
Works for some queries, fails unpredictably on others.
You know there's a problem. But you don't know why or how to fix it.
That's where we come in.
What's Included in the Package
Complete RAG System Audit
We analyze every component of your RAG pipeline:
- Retrieval Accuracy: Are you fetching the right documents?
- Context Precision: Are retrieved docs actually relevant?
- Context Recall: Are you missing relevant documents?
- Answer Grounding: Are answers supported by context?
- Faithfulness Score: Hallucination detection
- Embedding Quality: Are your embeddings optimal?
- Chunking Strategy: Is your document splitting effective?
Deliverable: 25-page RAG Audit Report with RAGAS metrics
RAGAS Evaluation Pipeline (Automated)
We build a fully automated evaluation pipeline for your RAG system:
- RAGAS framework integrated with your codebase
- 100+ RAG-specific test cases (retrieval, grounding, multi-hop)
- Automated scoring for Context Precision, Recall, Faithfulness
- CI/CD integration (runs on every commit)
- Pass/fail thresholds configured
- HTML reports generated automatically
Deliverable: Working RAGAS pipeline in your GitHub repo
Optimization Recommendations (Prioritized)
Not just diagnosis—we tell you exactly how to fix it:
- Embedding optimization: Switch to domain-specific models (e.g., Cohere, Voyage)
- Chunking improvements: Optimal chunk size, overlap, metadata
- Retrieval tuning: Adjust top_k, similarity thresholds, filters
- Reranking strategy: Add cross-encoder for precision
- Prompt engineering: Stronger grounding instructions
- Hybrid search: Combine semantic + keyword search
Deliverable: Prioritized optimization playbook with expected impact
2 Weeks of Implementation Support
We don't just hand you a report and disappear:
- Slack/Teams channel for questions
- 2 implementation review calls
- Code review of your optimizations
- Before/after metrics comparison
Deliverable: 2 weeks of hands-on support
The 2-Week Process
Audit & Build
- Day 1-2: System review + data collection
- Day 3-5: Run RAGAS evaluation + analyze metrics
- Day 6-7: Build test suite + CI/CD integration
Optimize & Deliver
- Day 8-10: Write optimization recommendations
- Day 11-12: Deliverables handoff + training session
- Day 13-14: Implementation support
By Day 14, you have a clear roadmap to 90%+ accuracy.
Results From Past Clients
Faithfulness improvement
Reduction in "I don't know" responses
Grounding failures caught
"The RAGAS pipeline caught grounding failures we never would have found manually. Our faithfulness score went from 72% to 94% after implementing their recommendations."
Jessica Park
Head of AI, Enterprise SaaS
"Switching to domain-specific embeddings (their #1 recommendation) improved our retrieval accuracy by 40%. Worth every dollar."
David Liu
CTO, Healthcare AI Startup
Investment
Fixed Price • No Surprises
Complete RAG audit with RAGAS metrics
100+ automated test suite
Optimization recommendations
2 weeks implementation support
Compare to:
$50K+
Hiring ML engineer for 3 months
$100K
Lost revenue from poor RAG performance
2-week delivery guaranteed • Next start date: This Monday
Ready to Fix Your RAG System?
Book a 20-minute scoping call. We'll review your RAG architecture and confirm if this package is the right fit.
Book Scoping CallNo commitment required • Quick turnaround