Retrieval Systems That Answer From Your Data
Production RAG pipelines: hybrid retrieval, reranking, and citation-grounded generation over your documents, measured on real questions before launch.
Most RAG dies on the real corpus: messy PDFs, conflicting versions, questions with no answer in the source. Ours run on infrastructure we already operate: DOCKR retrieves over source code in 11+ languages every day, with the same eval gates, access controls, and observability we deploy for clients. That is why our builds move fast: the hard parts already exist.
- Hybrid retrieval
- Rerank
- Permission filter
- Generate + cite
Why RAG Dies on the Real Corpus
Retrieval is the most frequent failure point in production RAG, and robustness evolves rather than being designed in. These are the six failure patterns that recur across systems we get called in to fix, ordered by where they strike in the pipeline.
Ingestion Drops the Content
A parse failure drops the table, a chunk boundary splits the fact in half, or a poisoned document slips in through a connector. The retriever can only find what survived ingestion.
Silent Retrieval Misses
The right document exists but retrieval never surfaces it: ranked below the cutoff, or structurally missed because it is an exact identifier and the search is vector-only. Wrong answer, no alarm.
Retrieved but Still Wrong
The correct passage was retrieved and ranked, then dropped in consolidation, buried mid-context, or contradicted by a stale duplicate. "We have RAG" was true; the answer was still wrong.
Permissions Ignored
The index treats every document as public. An intern asks a question and gets an answer sourced from a board deck they should not have read.
Stale Index, Stale Answers
The corpus moved on; the index did not. The system cites last year's policy with full confidence because nobody wired freshness sync.
No Eval Harness
Chunking changes, model swaps, and prompt tweaks ship untested against real questions. Quality drifts silently, because robustness is never designed in once; it has to be measured continuously.
Retrieval Systems We Build
Six kinds of RAG system, each measured against your real questions before launch. Most deployments are one of these, or two wired together.
Enterprise Knowledge Search
One query box over wikis, drives, tickets, and docs, with permissions intact and every answer cited to its source.
Document Intelligence
Extraction, classification, and Q&A over contracts, invoices, and forms at volume, with structured output your systems consume.
Support Knowledge Grounding
The retrieval layer under a support bot or helpdesk: policies, resolution history, and product docs served with citations.
Compliance & Policy Q&A
Regulatory and policy corpora with version awareness and audit trails, built for answers that must hold up under review.
Codebase Intelligence
Retrieval over repositories: architecture questions, module summaries, and code-aware search. The system DOCKR runs on.
RAG Inside Existing Products
Retrieval added to the product you already ship: search upgrade, in-app answers, or a knowledge feature for your users.
Retrieval Patterns, Chosen Per Use Case
RAG is not one technique. The feasibility study samples your corpus and real questions, then picks the pattern. Most production systems end up hybrid plus one specialized layer.
Vector RAG
Semantic similarity over embeddings. The right call for natural-language Q&A over prose: wikis, docs, ticket history.
Hybrid RAG
Vector plus keyword (BM25), fused and reranked. The production default when answers hinge on exact terms: part numbers, policy codes, names.
GraphRAG
Knowledge-graph traversal over entities and relationships. For connect-the-dots questions vector search cannot reach: which vendors appear in contracts expiring this quarter.
Agentic RAG
A retrieval agent that plans, queries multiple sources, checks coverage, and re-queries before answering. For multi-hop questions spread across systems.
Corrective RAG
Retrieved evidence is graded before generation; weak retrieval triggers a re-query or an abstention. For corpora where a confident wrong answer is costly.
Multimodal RAG
Tables, scanned forms, and diagrams parsed and indexed alongside text. For corpora that are not clean markdown.
Ingestion Is Half the System
Retrieval quality is decided before the first query, at ingestion. Every source is parsed, chunked, tagged, and kept fresh, or the best retriever in the world serves stale fragments.
What the ingestion layer does
- Source connectors: wikis, drives, tickets, databases, APIs, and repos
- Document parsing: OCR for scans, table extraction, layout-aware splitting
- Chunking tuned per content type: contracts are not tickets are not code
- Metadata and permission tags captured at ingest, not patched later
- Dedup and versioning: superseded documents stop answering questions
- Freshness sync: webhooks and scheduled re-index, no stale policy
Why Our RAG Ships Faster
Most builds assemble retrieval, evals, access control, and governance from scratch. Ours start from the infrastructure DOCKR runs on daily: retrieval over source code across 11+ languages, in production. That is where the speed comes from.
What every query clears
- Hybrid retrieval: dense vectors, keyword, and knowledge-graph traversal, fused for recallEVERY QUERY
- Reranking: cross-encoder precision on the candidatesEVERY QUERY
- Permission filter: users only retrieve what they can seeEVERY QUERY
- Citation-strict generation: answer from sources or abstainEVERY ANSWER
- Governance layer: policy enforcement, audit log, kill switchALWAYS ON
- Eval harness: golden set of real questions gates every releaseEVERY RELEASE
- Feedback loop: retrieval misses feed the next eval setCONTINUOUS
- Operations: index refresh runbooks, regression checks after pipeline changesALWAYS ON
- Observability: every query traced, cost and latency attributedCONTINUOUS
What You Own at Launch
A measured retrieval system and everything needed to run, extend, and audit it, in your accounts from day one.
The launch handover
- Production retrieval pipeline in your infrastructure
- Ingestion pipeline: parsing, per-type chunking, and permission tagging for every source system, with freshness sync
- Retrieval pattern documented and benchmarked: which techniques run on your corpus and why
- Eval harness and golden set from your real questions
- Permission model with document-level access control
- Feedback loop: retrieval misses feed the next eval set
- Query observability: traces, scores, and miss feeds
- Audit log of what was retrieved and shown, to whom
- Operations runbook: index refresh, regression checks, rollback
- Cost dashboard with per-query attribution
- Post-launch observation window on live queries
Scoped by a Feasibility Study, Fixed at Kickoff*
The study samples your corpus, tests retrieval on real questions, and prices the build. The build runs weekly-demo sprints to a launch gate. After launch, you choose what continues.
Feasibility Study
Audits your corpus and permissions, runs retrieval tests on real questions, and prices the build. Ends with a go or no-go verdict and a costed estimate.
The Build
Scope, timeline, and fee locked at kickoff. Weekly demos on live retrieval over your actual documents, until the eval gate and launch criteria pass.
Iterate or Hand Over
An observation window on live queries is included. Then a retainer for tuning and new sources, or a clean handover with the eval harness and runbook.
*Build pricing depends on scope and is confirmed in the feasibility study. The number is locked before work starts.
Tutorial RAG, Vector Shops, and Operator Engineers
RAG delivery splits between pipelines copied from a tutorial, vendors who sell the database, and teams that run retrieval in production. The difference shows up on your real corpus.
Whatever the demo PDF contains.
Whatever the database supports.
Your corpus, your permissions, your real questions.
The demo answer looked right.
Benchmark numbers on public datasets.
A golden set from your questions, gating every release.
One embedding model, top-k, hope.
Dense search with whatever knobs exist.
Hybrid retrieval plus reranking, tuned on your misses.
Wrong answer, confidently.
Silence, or the nearest document.
Citation-strict generation that abstains when the source is thin.
A notebook and a demo video.
Vendor benchmarks.
DOCKR runs this stack over source code daily, in production.
| Dimension | Tutorial RAG | Vector Shops | FLYTEBIT |
|---|---|---|---|
| What gets scoped | Whatever the demo PDF contains. | Whatever the database supports. | Your corpus, your permissions, your real questions. |
| How quality is proven | The demo answer looked right. | Benchmark numbers on public datasets. | A golden set from your questions, gating every release. |
| Retrieval design | One embedding model, top-k, hope. | Dense search with whatever knobs exist. | Hybrid retrieval plus reranking, tuned on your misses. |
| The hard cases | Wrong answer, confidently. | Silence, or the nearest document. | Citation-strict generation that abstains when the source is thin. |
| Proof | A notebook and a demo video. | Vendor benchmarks. | DOCKR runs this stack over source code daily, in production. |
Match the Tool to the Question
RAG development answers how the knowledge layer gets built. If that is not your question, one of these fits better.
Frequently Asked Questions
What does RAG development include?
The full retrieval stack: document ingestion and chunking, hybrid search (dense vectors plus keyword plus knowledge-graph traversal for entity and relationship questions), reranking, citation-grounded generation, access controls, freshness sync, and an eval harness built from your real questions. The deliverable is a measured retrieval system over your corpus, not a vector database with a prompt.
Why do most RAG systems fail after the demo?
Because the demo corpus is curated and real corpora are not. Production RAG fails on messy PDFs, conflicting document versions, questions with no answer in the corpus, and retrieval that silently misses. Our pipelines handle those cases: hybrid retrieval for recall, reranking for precision, and citation-strict generation that abstains when the source does not support the answer.
How do you measure retrieval quality?
With an eval harness built before the pipeline ships. We compile a golden set from your real questions, score retrieval recall and answer faithfulness on every release, and treat a regression as a blocked deploy. Chunking changes, model swaps, and prompt tweaks are measured against the same set, not vibes.
Can RAG work with our existing systems and permissions?
Yes. Ingestion connects to your document stores, wikis, ticketing systems, and databases. Document-level permissions carry through to retrieval, so users only get answers from content they are allowed to see. Audit logging covers what was retrieved and shown to whom.
How long does a RAG build take?
Typically 4 to 8 weeks depending on corpus size, source systems, and permission complexity. The retrieval, eval, and observability components already exist from DOCKR, our product that runs RAG over source code daily, so builds start from proven infrastructure. The exact window is set in the feasibility study.
How much does RAG development cost?
Cost depends on scope: corpus size, number of source systems, permission model, and latency requirements. The feasibility study, which starts from $2K, produces a costed estimate. The build price is then fixed at kickoff, so the number is agreed before work starts.
What happens after the RAG system launches?
Every build includes an observation window where we monitor live queries, tune retrieval, and expand the golden set from real misses. After that, you can keep us on a retainer for iteration, or we hand over to your team with the eval harness, dashboards, and runbook.
Retrieval That Holds Up on Real Questions
Schedule a 30-minute working session with our expert team. We will look at your corpus, your questions, and give you a straight answer on what production RAG takes.