RAG chatbots that answer from your content, not thin air.
Generic LLMs hallucinate. We build production RAG pipelines with hybrid retrieval, MMR reranking, and confidence-gated answers, so your AI says "I don't know" instead of making things up.
- Confidence scoring
- BM25 + semantic hybrid
- Source-cited answers
- Anti-hallucination guardrails
// live pipeline walkthrough
Source docs
Sentence-aware split
text-embedding-3-large
Vector + BM25 index
Hybrid retrieval
MMR reranking
Cited + confidence-gated
// indexing phase
// why most rag implementations fail in production
Four problems we fix before you hit users.
Confident wrong answers
An off-the-shelf LLM fabricates product names, prices, and policies it was never given. One hallucinated answer in a customer-facing context destroys trust.
Confidence-gated answers. Below 0.45: "not in knowledge base." No guessing.
No source grounding
Answers that cannot be traced to a specific document chunk cannot be audited, updated, or trusted. You cannot know what the AI is drawing from.
Every answer is tagged with source document and chunk. Fully auditable.
Exact-term retrieval failure
Pure vector search misses exact product SKUs, serial numbers, model names. "Heritage Chronograph AU-2101" gets a generic watch answer.
Hybrid BM25 + semantic. Exact terms and paraphrases, both covered.
Stale or redundant context
Without reranking, the top chunks are near-duplicates of the same passage. The LLM gets redundant context and misses breadth.
MMR reranking selects 8–10 diverse, relevant chunks from 50 candidates.
// what it looks like in practice
The same question. Two very different answers.
Q: What is the return policy on the Heritage Chronograph?
The Heritage Chronograph has a 30-day return policy with free shipping both ways. Contact support@yourstore.com to initiate a return.
Q: What is the return policy on the Heritage Chronograph?
Based on your store policy: watches may be returned within 14 days in unworn condition with original packaging. Contact info@store.com.
source: store-policies.pdf · chunk 3
Q: Do you carry the Vacheron Patrimony in rose gold?
Not in knowledge base. I can only answer questions about products and policies in our knowledge base. Please contact us directly for availability queries.
// confidence < 0.45 → returns "not in knowledge base" instead of guessing
// production proof
Building a RAG pipeline that answers from real content, not hallucinations
Generic AI chatbots give confident wrong answers. We built a production RAG pipeline with semantic search, BM25 hybrid retrieval, and anti-hallucination guardrails that answers only from what it actually knows.
- >80%
- Retrieval hit rate (relevant source in top 5)
- >70%
- Questions answered from grounded context
- <5%
- Empty context when data exists
- >0.50
- Average RAG confidence score
// what we build
RAG engineering, end to end.
Document ingestion
PDF, DOCX, HTML, plain text, chunked with sentence-awareness to preserve context across boundaries.
Hybrid retrieval
BM25 for exact-term queries + dense vector search for paraphrases. Better recall than either alone.
MMR reranking
Top-50 candidates reranked to 8–10 maximizing relevance and diversity. No redundant context.
Confidence gating
Below 0.45: "not in knowledge base." No guessing, no hallucinating. Configurable threshold.
Source citation
Every answer tagged to the exact document chunk it came from. Fully auditable.
Multi-tenant RAG
Isolated knowledge bases per tenant. Each seller's AI only knows that seller's content.
HIPAA-safe AI
RAG on PHI with AWS HIPAA-eligible infra, BAA-covered APIs, no raw data leaving the boundary.
Streaming responses
Server-sent events for character-by-character output. Perceived latency drops significantly.
Custom guardrails
Topic restriction, PII detection, and domain-scope enforcement at the prompt layer.
// faq
RAG questions, answered without the marketing.
What is RAG and why does it reduce hallucinations?
RAG (Retrieval-Augmented Generation) grounds the LLM's answers in a specific set of documents you control. Instead of generating from training data (which may be outdated or wrong), it retrieves relevant chunks from your knowledge base first, then generates an answer based only on those chunks. Hallucinations drop because the model is constrained to what was retrieved.
What is a confidence score and why does it matter?
Our RAG pipeline scores each answer based on how closely the retrieved chunks match the query. Below 0.45, the system returns "not in knowledge base" instead of guessing. This prevents the system from confidently answering questions it cannot actually answer from your content, which is the core hallucination problem in production RAG.
What is hybrid retrieval (BM25 + semantic)?
Pure semantic search excels at paraphrased or conceptual queries but can miss exact product names, serial numbers, or SKUs. BM25 is a keyword-matching algorithm that handles exact terms well. Combining both gives higher recall across both query types. We use this on every production RAG we ship.
Do you use Pinecone or another vector database?
It depends on the scale and read pattern. For read-heavy, low-write workloads we often use MySQL with cosine similarity: simpler infrastructure, lower cost, faster queries for datasets under 10M vectors. For high-volume multi-tenant workloads, Pinecone or Weaviate. We recommend based on your actual data volume, not what's fashionable.
Can you integrate RAG with HIPAA-regulated data?
Yes. We have shipped HIPAA-compliant AI in production. PHI never leaves the HIPAA-compliant infrastructure boundary. No data is sent to an API without a BAA. For clinical AI, confidence thresholds are stricter and the system is restricted to answering from approved clinical content only.
What LLMs do you use?
GPT-4 and GPT-4o for most production work. Claude 3.5 Sonnet for long-context reasoning. For on-premise or air-gapped deployments: Llama 3 or Mistral via Ollama. We recommend based on your latency, cost, and data residency requirements.
// start your rag project
Tell us what your AI needs to know.
We scope RAG projects in a single 30-minute call. By the end you will know what retrieval strategy fits your data, what the confidence threshold should be, and what it costs to build it right.
- Hybrid retrieval architecture scoped to your data
- Anti-hallucination guardrails from day one
- Production experience: shipped and measured
- 30-minute call · No pitch · NDA on request
email: info@nexios.in
Need HIPAA-safe AI? See our HIPAA-compliant software development →