RAG development services from senior AI pod teams

Ryz builds retrieval-augmented generation (RAG) systems with dedicated AI pod teams of senior engineers who work in your cloud and repos, alongside your team. The pod owns the whole pipeline, from document ingestion and hybrid search to permission filtering, citations and an evaluation harness, and ships it to production. Every engineer comes from the top 1% of the tens of thousands we interview, and they work on US business hours.

What we build

Most RAG systems fail at retrieval, not generation: the right passage never reaches the model, or the wrong one does and the model answers it confidently. Our pods treat ingestion, search and ranking as the core work and the prompt as the last step. Typical deliverables:

• Ingestion pipelines. Parsers for PDFs, HTML, Office files, tables and scans, with deduplication, change detection and incremental re-indexing so answers track the latest version of a document.
• Structure-aware chunking. Chunks split by heading, section or clause, with parent-child retrieval and contextual headers so a passage still makes sense out of its document.
• Hybrid search. BM25 keyword search plus dense vectors, merged with reciprocal rank fusion, so exact identifiers like policy numbers and SKUs match as well as paraphrased questions.
• Reranking. A cross-encoder or hosted reranker over the top candidates, kept only if it earns its latency on your eval set.
• Permission-aware retrieval. Access filters applied at query time from SharePoint, Google Drive, Confluence or database ACLs, so a user never receives a passage they could not open themselves.
• Answer generation with citations. Prompts and output schemas that force answers to cite the passages they used, and a fallback to "I don't know" or a human when retrieval comes back weak.
• Evaluation harness. A labeled question set from real users, with retrieval metrics (recall@k, MRR) and answer metrics (faithfulness, relevance) run in CI on every change.
• Stalled-pilot rescue. Measuring each stage of an existing RAG prototype to find whether ingestion, chunking, search or generation is the weak link before rewriting anything.

How an engagement works

Every engagement follows the same four steps: Talk, Match, Join, Grow.

1. Talk. We go through your sources, users, access model and what a correct answer looks like. We ask for 50 to 100 real questions people already send to support or analysts.
2. Match. We propose a pod scoped to your stack, usually a tech lead, an ML engineer with retrieval experience and backend engineers, with names and a price.
3. Join. The pod works in your repos, CI and standups, with weekly demos to your team.
4. Grow. You add people or disciplines, or the pod hands the system over to your engineers with runbooks and the eval suite.

Week 1 is access and baselines: connecting to the source systems, building a first eval set from real questions and measuring how a naive pipeline scores. Month 1 typically brings a working pipeline on a subset of sources, with hybrid search, citations and per-stage metrics, demoed to a pilot group. Month 3 is about production: the full source set, permission sync, monitoring, cost controls and a release process where every retrieval change has to pass the eval suite before it ships. Actual pace depends on scope, data access and your onboarding.

The stack our teams work in

How we keep RAG answers accurate

These are the failure modes we see most often in RAG systems that worked in a demo and broke with real users, and what our pods do about each:

• The eval set is too easy. Teams test with questions written by the people who built the index. We source questions from real tickets, chats and search logs, and include multi-hop questions, questions with no answer in the corpus and questions that use internal jargon.
• Exact identifiers never match. Dense embeddings blur codes like "XR-2207B" or a clause number. Hybrid search with a keyword leg, plus metadata filters, fixes most of these.
• Tables and layouts are destroyed in parsing. A rate table flattened into text gives wrong numbers. We parse tables into structured form and test numeric answers specifically.
• Stale or duplicate documents win. Five versions of the same policy compete in the index. Ingestion tracks versions, drops superseded copies and adds recency to ranking where it matters.
• Permission leaks. Filtering after generation is too late, because the model has already seen the text. We filter at retrieval time and write tests that try to retrieve restricted documents as an unauthorized user.
• Prompt injection from documents. Retrieved text is untrusted input. We separate instructions from retrieved content, strip or flag instruction-like text and limit what the model can do with tools.
• Silent regressions. A new embedding model or chunk size can lift one metric and sink another. Every change runs the full eval suite, and results are compared against the last release before merge.
• Cost and latency creep. Large contexts and rerankers add up. We track tokens and latency per query and cut context that does not change answers.

Sometimes the right answer is not RAG at all. For a few stable documents, long context is simpler. For style or format problems, fine-tuning may fit better. Our RAG vs fine-tuning comparison covers that decision.

As one example of document-heavy production work, a Ryz pod built AI marketing compliance review for a global capital management firm, covering 8,000+ documents and cutting review time from days to hours. More examples are on our case studies page.

Team shapes and cost

Typical Ryz cost is $7,000 to $15,000 per engineer per month. Senior engineers run $10,000 to $15,000 per month, and leads $15,000+ per month, quoted per team. Three shapes we often propose for RAG:

• Pilot pod: tech lead + 2 senior engineers. One lead at $15,000+ plus two seniors at $20,000 to $30,000 comes to roughly $35,000 to $45,000+ per month. Good for proving retrieval quality on one use case.
• Production pod: about 7 senior engineers, including a tech lead, an ML engineer, backend and data engineers. Seven people at $10,000 to $15,000 is about $70,000 to $105,000 per month, plus the lead premium. Good for multiple sources, permissions and a production SLA.
• Retrieval specialist: 1 to 2 senior engineers on your team. $10,000 to $30,000 per month. Good when you already own the app and need search and eval depth.

Project cost is team size × duration × monthly rate. A three-person pilot pod for four months at about $40,000 per month is roughly $160,000. Quotes are scoped per team, and you get a plan, a price and the names of the people before you start.

Dedicated team or staff augmentation?

Pick an AI pod team when you want one group to own the RAG system end to end, from ingestion to evals, and ship it. That fits when nobody on your side has built retrieval at production scale, or when your engineers are committed to other work.

Pick staff augmentation when your team already owns the application and you need retrieval depth. Senior RAG engineers or vector database engineers work on your team, take tickets from your backlog and follow your code review.

When Ryz isn't the right fit

If you want a hosted enterprise search product you configure rather than a system built in your stack, buy that product. If you want hourly freelance work or a trial before talking to anyone, a self-serve marketplace fits better. If you need engineers on European or Asian hours, use a global network.

Related

• AI development services
• LLM fine-tuning services
• MCP server development
• MLOps services
• Pinecone vs pgvector
• Guide: partners to take an AI pilot to production

FAQ

How much does RAG development cost?

Typical Ryz cost is $7,000 to $15,000 per engineer per month, and RAG work is mostly senior, at $10,000 to $15,000. A pilot pod of a lead and two seniors runs roughly $35,000 to $45,000+ per month. Total cost is team size × duration × monthly rate, and you get a scoped plan, price and names before you start.

How fast can a RAG project start?

After the scoping call we propose a team. Most of the timeline depends on scope and your onboarding, especially how quickly the pod gets access to source systems and real user questions.

Can you fix a RAG prototype that gives wrong answers?

Yes. We build an eval set from real questions first, then measure ingestion, retrieval and generation separately to find the weak stage before changing code. Many fixes are in parsing and search, not the prompt.

Which vector database should we use?

If you already run Postgres, pgvector often handles millions of chunks well. Pinecone, OpenSearch or Azure AI Search fit when you need managed scale, built-in hybrid search or your cloud's native service. We benchmark on your data before choosing.

Does our data leave our cloud?

The pod builds in your cloud accounts and repos. Model calls can go through AWS Bedrock or Azure OpenAI in your own tenancy, so you decide where data is processed.

Questions we didn't answer? Email info@ryzlabs.com.