SaaS RAG QA

RAG Evaluation
for SaaS Products

You built an AI search or docs assistant on top of your knowledge base. But is it retrieving the right chunks and generating accurate answers — or confidently hallucinating? We measure every stage of your RAG pipeline.

Free 100-Query RAG Audit What We Measure

RAG Pipeline — Where We Evaluate

1
Document IngestionChunk completeness, metadata accuracy
2
EmbeddingEmbedding model alignment with domain
3
RetrievalRecall@1/3/5/10, re-ranker quality
4
Context AssemblyContext precision, noise ratio
5
GenerationAnswer faithfulness, no hallucination
6
Final AnswerAnswer relevance, completeness
100+
Queries per free audit
6
RAG pipeline stages evaluated
RAGAS
Compatible scoring output
48hr
Report delivery

Free 100-Query RAG Reliability Audit

We run 100 test queries against your RAG system and measure Retrieval Recall, Context Precision, Answer Faithfulness, and Latency per pipeline stage. Full RAGAS-compatible report in 48 hours.

1
Share API endpoint or logs
2
100 domain-specific test queries
3
RAGAS-compatible metric report
4
Optimization recommendations
Claim Free Audit
Metrics
What We Measure
RAGAS-compatible metrics across the full RAG pipeline, not just end-to-end accuracy.
Retrieval Recall@K
Did the right chunk rank in top K results?
Context Precision
What % of retrieved chunks are actually relevant?
Answer Faithfulness
Does the answer only assert what the context says?
Answer Relevance
Does the answer actually address the question?
Chunk Quality Score
Semantic completeness and boundary correctness
Latency P95
95th percentile end-to-end query time
🔍

Retrieval Recall Audit

The most common RAG failure: the right chunk exists in your database, but the retrieval step doesn't surface it.

  • Recall@1, @3, @5, @10 per query type
  • Multi-hop question retrieval testing
  • Sparse vs. dense retrieval comparison
  • Re-ranker effectiveness measurement
  • Stale document detection
📐

Chunk Quality Audit

Wrong chunk boundaries cause retrieval failures no amount of model tuning can fix.

  • Semantic completeness per chunk
  • Cross-chunk dependency detection
  • Optimal chunk size recommendation
  • Metadata completeness check
  • Table and structured data handling
🤝

Faithfulness & Groundedness

Does your RAG system answer from retrieved context — or does the LLM fill in gaps with hallucinations?

  • Per-claim grounding verification
  • Hallucination rate per query category
  • LLM over-generation detection
  • Context utilization rate
  • Conflicting document handling
⚡

Latency & Cost Profiling

Production RAG needs to be fast and cost-efficient. We profile every stage.

  • Latency breakdown: embed / retrieve / generate
  • P50 / P95 / P99 response times
  • Token cost per query
  • Caching opportunity identification
  • Batch vs. streaming optimization
Compatible Stacks
Works with Your Existing RAG Stack
We evaluate the behavior of your system regardless of the underlying stack.

Vector Databases

Pinecone, Weaviate, Qdrant, ChromaDB, pgvector, Azure AI Search, Milvus, Redis Vector

Embedding Models

OpenAI text-embedding-3, Cohere embed-v3, sentence-transformers, BGE, E5, custom fine-tuned embeddings

Orchestration Frameworks

LangChain, LlamaIndex, Haystack, custom Python, AWS Bedrock Knowledge Bases, Azure AI Search RAG

SaaS Use Cases

Docs assistant, in-app AI search, onboarding chatbot, feature documentation QA, customer support knowledge base, internal helpdesk

Pricing
SaaS RAG Evaluation Packages
Retrieval Audit
₹1.5L – ₹4L
Focused retrieval and chunking audit
  • 100-query test suite
  • Recall@1/3/5/10 measurement
  • Chunk quality analysis
  • Top 5 retrieval failure patterns
  • Optimization recommendations
Get Started
Most Popular
Full Pipeline Evaluation
₹4L – ₹15L
End-to-end RAG evaluation + optimization
  • Everything in Retrieval Audit
  • 200–500 ground-truth Q&A dataset
  • Faithfulness + groundedness scoring
  • Latency and cost profiling
  • Re-ranker effectiveness analysis
  • Stale document detection
  • RAGAS-compatible score output
Start Free Audit
Continuous Monitoring
₹2L – ₹8L/mo
Ongoing RAG quality monitoring in production
  • Production query sampling
  • Monthly metric report
  • Regression alerts on score drops
  • New test cases from user failures
  • Quarterly benchmark re-run
  • CI/CD evaluation API available
Contact Us
FAQ
Common Questions
How is RAG evaluation different from standard LLM evaluation?
Standard LLM evaluation measures model quality. RAG evaluation measures the entire pipeline — did the retrieval step find the right documents, did context assembly include enough signal, and did the LLM generate an answer grounded in that context. Most RAG failures happen at the retrieval stage, not the generation stage. Without RAG-specific evaluation, you're testing the wrong thing.
Do you support multi-hop reasoning evaluation?
Yes. Multi-hop queries — questions that require connecting information from 2+ documents — are one of the hardest RAG failure modes. We explicitly include multi-hop scenarios in the benchmark dataset and measure Recall@K specifically for these cases, which standard single-query evaluation misses entirely.

Your RAG System Is Answering User Questions.
But Are the Answers Right?

Free 100-query audit. RAGAS-compatible report. Optimization recommendations included.

Free RAG Pipeline Audit

Free RAG Reliability Audit