What accuracy can we expect from a RAG system?

Typically 85–95% answer faithfulness on well-structured corpora with good chunking and reranking. We measure this with a test set before launch. We will not promise 100%, no RAG system achieves it, and anyone who claims otherwise is either measuring wrong or hasn't tested edge cases.

How large does our document library need to be?

There is no minimum. RAG works well on corpora as small as 50 documents and as large as millions. Very small corpora may be better served by including documents directly in the context window. We'll tell you honestly on the discovery call which approach makes more sense.

Can the RAG system answer from multiple document types?

Yes. We ingest PDFs, Word documents, HTML, Markdown, spreadsheets, and database records. Each source type may need different preprocessing. We handle that in the pipeline design.

How do you prevent the system from making up answers?

We implement faithfulness scoring that checks whether the LLM's answer is grounded in the retrieved context. Answers that score below a threshold are either refused with a fallback message, flagged for human review, or accompanied by a low-confidence warning, depending on your requirements.

How do you keep the knowledge base up to date?

We build ingestion that re-indexes on a schedule or when documents change, with content hashing so unchanged files are skipped rather than re-embedded every run. For sources with their own change feeds, like a wiki or a document management system, we hook into those directly so updates flow through within minutes. Stale retrieval is one of the most common reasons a RAG system that worked at launch degrades over the following months, and the fix is designed in from the start.

What query latency should we expect?

For a typical pipeline with hybrid search and reranking, end-to-end latency usually lands between one and three seconds, most of which is the language model generating the answer rather than the retrieval itself. We set a latency budget during architecture and design to it: reranking depth, model choice, and streaming the response all trade against each other. If you have a hard real-time requirement, we tell you what it costs in retrieval depth before we commit.

Can the system run inside our own infrastructure for compliance?

Yes. We deploy the full pipeline inside your cloud tenancy when policy requires it, use embedding and inference endpoints that are covered by a Business Associate Agreement for healthcare data, and keep query and answer logs in storage you control. The retrieval layer and vector store run in your environment, so sensitive documents never leave it. We design around your compliance requirements during discovery rather than retrofitting them later.

RAG Development

RAG that actually retrieves the right answer.

Most RAG systems fail in production not because the LLM is bad but because the retrieval is. Wrong chunks, missing reranking, no hallucination guardrails, and the system confidently returns wrong answers. We fix the retrieval layer first.

We build RAG pipelines that reach 90%+ answer faithfulness on real test sets, not benchmark datasets. That means chunking strategy tuned to your corpus, hybrid search, reranking, and faithfulness scoring before you serve a single query to users.

How we work

Tell us about your knowledge base.

What's included.

Eight components make a production RAG system. We don't hand you a prototype that skips four of them.

Chunking strategy

How you split documents determines retrieval quality more than almost any other decision. We test multiple strategies (fixed-size, semantic, hierarchical) against your actual content before committing.

Embedding pipeline

We select, benchmark, and deploy the right embedding model for your domain. Medical, legal, and technical corpora often need different models than general-purpose text.

Vector store selection & setup

Pinecone, Weaviate, pgvector, or Chroma. We choose based on your scale, latency requirements, and infrastructure preferences. We handle provisioning, indexing, and metadata filtering setup.

Hybrid search (dense + sparse)

Pure vector search misses keyword-heavy queries. We combine dense vector search with BM25 sparse retrieval to get the best of both, then tune the weighting for your corpus.

Reranking

A cross-encoder reranker re-scores top retrieved chunks against the query before they hit the LLM. This significantly improves precision without a major latency cost.

Hallucination guardrails

We build faithfulness checks that measure whether the LLM's answer is grounded in the retrieved context. Answers that fail the check can be flagged, refused, or routed to a human.

Evaluation (retrieval recall + answer faithfulness)

We build a test set from your documents and measure retrieval recall, answer faithfulness, and answer relevance before launch. You see the numbers, not just our assurance.

Monitoring dashboard

Post-launch visibility into query volume, retrieval quality scores, latency, and flagged low-confidence answers. You can see where the system struggles and improve it over time.

Where RAG breaks in production.

Almost every failing RAG system we are asked to fix fails in one of four places, and none of them is the language model. The model is the last step. The work that decides whether answers are right happens before the model ever sees the question.

The retrieval returns the wrong chunks. If chunking splits a procedure in half, or the embedding model was never trained on your domain's vocabulary, the right passage never makes it into the context window. The model then answers from whatever it did retrieve, which looks confident and is wrong. We tune chunking against your actual documents and benchmark embedding models on your queries rather than trusting defaults.

The system has no idea when it failed. A RAG pipeline without faithfulness scoring cannot tell the difference between a well-supported answer and a fabricated one. We add a grounding check that measures whether the answer is actually backed by the retrieved text, so low-confidence cases get flagged, refused, or sent to a human instead of shipped as fact.

The index goes stale. Knowledge bases change. A pipeline that indexed your documents once and never again slowly drifts out of date, answering from superseded policies and retired procedures. We build ingestion that re-indexes on a schedule or on change, with content hashing so unchanged documents are not needlessly re-processed.

Nobody measured it before launch. The most common failure is shipping on a demo that worked once. We build a test set from your real documents and real questions, then report retrieval recall, answer faithfulness, and relevance against a threshold you approve. The same suite runs on every change, so a later tweak cannot quietly degrade quality without anyone noticing.

When RAG beats fine-tuning.

Fine-tuning and RAG are different tools for different problems. Here are three cases where RAG is clearly the right choice.

Your data changes frequently

Fine-tuning bakes knowledge into model weights at a point in time. RAG queries a live knowledge base. If your documents update weekly (or daily) RAG is the right choice.

You need citations

A fine-tuned model can't tell you which document it learned from. A RAG system returns the source chunks alongside the answer, so users can verify and trust the output.

You can't retrain a base model

Fine-tuning requires significant compute and data. RAG works with the model you already have. If your budget doesn't include retraining, or your corpus isn't large enough, RAG is the practical path.

Who this is for.

Companies with large internal document libraries (wikis, SOPs, contracts, manuals)
Legal and compliance teams that need answers grounded in specific policies
Support teams handling repetitive questions that are answered somewhere in their documentation
Product teams adding search or Q&A features to their SaaS application

Who this isn't for.

Projects that only need public knowledge
If your use case is answering questions about public information, a simple web search tool attached to an agent is cheaper and faster than a RAG pipeline. RAG shines on your private, proprietary documents.
Projects without a document corpus
RAG requires documents to retrieve from. If the plan is to build the knowledge base “later”, the system cannot work yet. We'll tell you to come back when you have a real corpus.
Anyone expecting 100% accuracy
No retrieval system achieves perfect accuracy. We routinely get clients to 90%+ answer faithfulness with proper evaluation and guardrails, but the last 10% requires human review for high-stakes decisions. If your use case cannot tolerate any errors, RAG is not sufficient on its own.

Frequently asked questions.

What accuracy can we expect from a RAG system?: Typically 85–95% answer faithfulness on well-structured corpora with good chunking and reranking. We measure this with a test set before launch. We will not promise 100%, no RAG system achieves it, and anyone who claims otherwise is either measuring wrong or hasn't tested edge cases.
How large does our document library need to be?: There is no minimum. RAG works well on corpora as small as 50 documents and as large as millions. Very small corpora may be better served by including documents directly in the context window. We'll tell you honestly on the discovery call which approach makes more sense.
Can the RAG system answer from multiple document types?: Yes. We ingest PDFs, Word documents, HTML, Markdown, spreadsheets, and database records. Each source type may need different preprocessing. We handle that in the pipeline design.
How do you prevent the system from making up answers?: We implement faithfulness scoring that checks whether the LLM's answer is grounded in the retrieved context. Answers that score below a threshold are either refused with a fallback message, flagged for human review, or accompanied by a low-confidence warning, depending on your requirements.
How do you keep the knowledge base up to date?: We build ingestion that re-indexes on a schedule or when documents change, with content hashing so unchanged files are skipped rather than re-embedded every run. For sources with their own change feeds, like a wiki or a document management system, we hook into those directly so updates flow through within minutes. Stale retrieval is one of the most common reasons a RAG system that worked at launch degrades over the following months, and the fix is designed in from the start.
What query latency should we expect?: For a typical pipeline with hybrid search and reranking, end-to-end latency usually lands between one and three seconds, most of which is the language model generating the answer rather than the retrieval itself. We set a latency budget during architecture and design to it: reranking depth, model choice, and streaming the response all trade against each other. If you have a hard real-time requirement, we tell you what it costs in retrieval depth before we commit.
Can the system run inside our own infrastructure for compliance?: Yes. We deploy the full pipeline inside your cloud tenancy when policy requires it, use embedding and inference endpoints that are covered by a Business Associate Agreement for healthcare data, and keep query and answer logs in storage you control. The retrieval layer and vector store run in your environment, so sensitive documents never leave it. We design around your compliance requirements during discovery rather than retrofitting them later.

Ready to build your RAG system?

Tell us about your document corpus and what questions users need to answer. We'll reply within one business day with a rough scope and price range.