Hire a RAG Developer

Hire a RAG developer who has built retrieval systems that hold up in production.

Most RAG systems fail at retrieval, not generation. If your pipeline is returning the wrong documents, chunking content incorrectly, or producing confident answers with no grounding, the problem is upstream of the model. We fix the full pipeline.

Fixed scope, fixed price, scoped to your document volume, ingestion complexity, and evaluation requirements.

How we work

Tell us about your documents and what you need to retrieve.

What you get.

Four things every RAG engagement includes. None of them are optional.

Senior engineer, not a junior with an API key

RAG done poorly produces confident wrong answers. You get someone who has tuned chunking strategies, benchmarked retrieval quality, and debugged why a system hallucinates on edge-case documents.

Fixed project scope with deliverables defined upfront

Before work starts you know exactly what you're getting: a list of deliverables, a timeline, and a fixed price. No open-ended billing.

Code you own outright

IP transfers to you on final payment. No license fees to WayFind Labs, no vendor lock-in. Your team can maintain and extend the pipeline without us.

Post-launch support window included

Every engagement includes a post-launch window for bug fixes, retrieval tuning, and questions. You're not handed a codebase and abandoned.

What we build.

Every component of a production retrieval pipeline, from raw documents to grounded answers.

Document ingestion

Parsing PDFs, Word documents, HTML, and structured data into clean text. Handling metadata extraction, deduplication, and incremental updates.

Chunking strategy

Fixed-size, semantic, and hierarchical chunking evaluated against your documents. We pick the strategy that improves retrieval precision on your specific content.

Vector database setup

Pinecone, pgvector, Weaviate, or Chroma, chosen based on your latency, scale, and cost requirements, not what's trending.

Retrieval tuning

Hybrid search (keyword + semantic), re-ranking, and query expansion to improve what comes back before it reaches the model.

LLM answer generation

Prompt design for grounded answers with citations. Confidence thresholds and fallback handling when retrieval quality is low.

Evaluation framework

Automated tests for retrieval recall, answer accuracy, and hallucination rate. Benchmarked against a held-out test set of your real questions.

Who this is for.

Companies with proprietary documents that need Q&A
Legal contracts, technical manuals, internal policies, research reports. If your team spends hours looking for answers that already exist in your documents, RAG is the right tool.
Support teams that need accurate knowledge base retrieval
Generic chatbots hallucinate. A RAG system grounded in your actual support documentation answers accurately or says it doesn't know, and can cite the source.
Anyone who tried a generic chatbot and got hallucinations
The problem is usually retrieval quality, not the model. If your chatbot is confidently wrong, the fix is almost always upstream: better chunking, better retrieval, or better source documents.

Frequently asked questions.

What makes RAG hard to build well?: The model is the easy part. The hard parts are document pre-processing (messy PDFs, inconsistent formatting), chunking strategy (chunks too large lose precision, too small lose context), and retrieval tuning (getting the right documents back reliably). Most RAG systems that fail do so because of poor retrieval, not a bad model.
How do I evaluate a RAG developer before hiring?: Ask them how they measure retrieval quality. A good RAG developer should be able to describe their evaluation setup: a test set of question-answer pairs, metrics like recall@k and answer accuracy, and a process for iterating on chunking and retrieval based on those metrics. If they can only describe the architecture but not the evaluation, they haven't shipped RAG to production.
How accurate can RAG get on my documents?: It depends heavily on document quality and question type. For well-structured documents with clear factual content, retrieval accuracy above 85% is realistic. For highly ambiguous questions or poorly structured source documents, expectations need to be set accordingly. We benchmark against your actual documents during scoping so you have a realistic number before work starts.
How much work is ongoing maintenance?: Once built, a RAG pipeline mostly requires maintenance when your source documents change significantly. We document the re-ingestion process so your team can run it without us. Retrieval quality tends to drift if new document formats are added without updating the chunking strategy. We flag this during handoff so you know what to watch.

Ready to scope your RAG pipeline?

Describe your documents and what you need users to be able to ask. We'll reply within one business day with a rough scope and price range, no commitment required.

Hire a RAG developer who has built retrieval systems that hold up in production.

Fixed scope, fixed price, scoped to your document volume, ingestion complexity, and evaluation requirements.

What you get.

Four things every RAG engagement includes. None of them are optional.

Senior engineer, not a junior with an API key

RAG done poorly produces confident wrong answers. You get someone who has tuned chunking strategies, benchmarked retrieval quality, and debugged why a system hallucinates on edge-case documents.

Fixed project scope with deliverables defined upfront

Before work starts you know exactly what you're getting: a list of deliverables, a timeline, and a fixed price. No open-ended billing.

Code you own outright

IP transfers to you on final payment. No license fees to WayFind Labs, no vendor lock-in. Your team can maintain and extend the pipeline without us.

Post-launch support window included

Every engagement includes a post-launch window for bug fixes, retrieval tuning, and questions. You're not handed a codebase and abandoned.

What we build.

Every component of a production retrieval pipeline, from raw documents to grounded answers.

Document ingestion

Parsing PDFs, Word documents, HTML, and structured data into clean text. Handling metadata extraction, deduplication, and incremental updates.

Chunking strategy

Fixed-size, semantic, and hierarchical chunking evaluated against your documents. We pick the strategy that improves retrieval precision on your specific content.

Vector database setup

Pinecone, pgvector, Weaviate, or Chroma, chosen based on your latency, scale, and cost requirements, not what's trending.

Retrieval tuning

Hybrid search (keyword + semantic), re-ranking, and query expansion to improve what comes back before it reaches the model.

LLM answer generation

Prompt design for grounded answers with citations. Confidence thresholds and fallback handling when retrieval quality is low.

Evaluation framework

Automated tests for retrieval recall, answer accuracy, and hallucination rate. Benchmarked against a held-out test set of your real questions.

Who this is for.

Companies with proprietary documents that need Q&A

Legal contracts, technical manuals, internal policies, research reports. If your team spends hours looking for answers that already exist in your documents, RAG is the right tool.

Support teams that need accurate knowledge base retrieval

Generic chatbots hallucinate. A RAG system grounded in your actual support documentation answers accurately or says it doesn't know, and can cite the source.

Anyone who tried a generic chatbot and got hallucinations

The problem is usually retrieval quality, not the model. If your chatbot is confidently wrong, the fix is almost always upstream: better chunking, better retrieval, or better source documents.

Frequently asked questions.

What makes RAG hard to build well?

The model is the easy part. The hard parts are document pre-processing (messy PDFs, inconsistent formatting), chunking strategy (chunks too large lose precision, too small lose context), and retrieval tuning (getting the right documents back reliably). Most RAG systems that fail do so because of poor retrieval, not a bad model.

How do I evaluate a RAG developer before hiring?

Ask them how they measure retrieval quality. A good RAG developer should be able to describe their evaluation setup: a test set of question-answer pairs, metrics like recall@k and answer accuracy, and a process for iterating on chunking and retrieval based on those metrics. If they can only describe the architecture but not the evaluation, they haven't shipped RAG to production.

How accurate can RAG get on my documents?

It depends heavily on document quality and question type. For well-structured documents with clear factual content, retrieval accuracy above 85% is realistic. For highly ambiguous questions or poorly structured source documents, expectations need to be set accordingly. We benchmark against your actual documents during scoping so you have a realistic number before work starts.

How much work is ongoing maintenance?

Once built, a RAG pipeline mostly requires maintenance when your source documents change significantly. We document the re-ingestion process so your team can run it without us. Retrieval quality tends to drift if new document formats are added without updating the chunking strategy. We flag this during handoff so you know what to watch.

Ready to scope your RAG pipeline?

Describe your documents and what you need users to be able to ask. We'll reply within one business day with a rough scope and price range, no commitment required.