RAG Development Services
A fluent answer is not enough when the question concerns your policy, product, contract, or operating procedure. RaftLabs develops retrieval-augmented generation systems that index approved knowledge, preserve access boundaries, retrieve supporting passages, cite them, and refuse when the available evidence is insufficient.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
Evidence and scope
4 to 8 weeks
First release
One use case, one or two sources, and a labelled question set.
$15K
Starting scope
Ingestion, retrieval, citations, evaluation, and handover.
Defined first
Release rule
Retrieval and answer quality measured on your questions.
The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Does the assistant answer confidently when the supporting document is missing, outdated, or outside the user's permissions?
Can you tell whether a bad answer came from retrieval, source content, or the model itself?
Plain answer
RAG development lets a language model answer from approved private knowledge and cite the evidence it found. RaftLabs develops ingestion, search, permissions, refusal paths, and tests. A focused system for one use case and 1 to 2 sources starts at $15,000 and takes 4 to 8 weeks.
The assistant found a relevant paragraph, but the index contained two versions. It chose the older one, wrote a confident answer, and displayed a citation that made the mistake look trustworthy.
RAG is not accurate because it has a vector database. It becomes dependable when source versions, permissions, retrieval tests, citations, and refusal behaviour are designed as one system.
First scope
A plain model call is simpler when the necessary context is small, stable, and already supplied in the request.
Employees or customers repeatedly ask questions answered by approved documents, tickets, or records.
Knowledge changes often enough that citations, versions, and deletion behaviour matter.
Different users have different permission boundaries over the same source collection.
The task is mainly tone, style, or output-format control rather than knowledge retrieval.
The answer depends on judgment that no approved source contains.
The source material is contradictory and nobody can decide which version governs.
Scope
| RAG | Fine-tuning | |
|---|---|---|
| Best fit | Private or changing knowledge with source evidence | Repeatable behaviour, style, format, or a narrow task |
| Knowledge update | Re-index the changed or deleted source | Prepare data, retrain, and evaluate another model version |
| Citation | Can link the answer to retrieved passages | Does not expose which training example supports a claim |
| Main failure | Wrong, missing, stale, or unauthorized retrieval | Insufficient examples or behaviour that fails to generalize |
| First test | Can search find the right evidence? | Does training improve the task beyond prompting? |
For broader model integration controls, see LLM integration services. AI knowledge management owns the employee-facing workflow, while AI semantic search owns search experiences that may not generate an answer.
How it works
A labelled question set keeps retrieval and generation honest.
Choose one workflow, approved sources, user groups, and a labelled set of questions with expected passages, unacceptable answers, and permission cases.
Test parsing, chunking, metadata, versions, permissions, hybrid search, and reranking on representative content. Fix missing evidence before adding generation.
Generate from the retrieved context, display source links, validate access, and decline or escalate when evidence is insufficient.
Measure retrieval misses, unsupported statements, stale sources, latency, cost, and user escalations before expanding the corpus or audience.
Scope and price
Begin with one use case, one or two sources, a labelled question set, retrieval, citations, access controls, and a handover path.
Multi-domain systems grow in fixed phases after the first corpus clears its agreed retrieval and answer thresholds.
Starting investment
Starts at $15,000
A focused first release usually takes 4 to 8 weeks. Source quality, custom connectors, and permission complexity move the estimate most.
Retrieval measured separately
The evaluation shows whether the right evidence was found before generation quality is judged. That makes failures diagnosable.
Fixed-price phase
Once the sources, questions, permissions, acceptance rules, and handover are agreed, the phase price is locked in writing.
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Read moreRetrieval-augmented generation retrieves relevant passages from an approved knowledge source before a language model answers. The response can cite those passages and reflect newer private information without retraining the model. The system still needs evaluation because retrieval can return irrelevant, incomplete, or unauthorized context.
Use RAG when answers must use private or frequently changing knowledge and show their supporting source. Fine-tuning fits repeatable behaviour, tone, format, or a narrow task when prompting has reached its limit. Some systems combine them, but retrieval should not be added when the required context already fits safely in the request.
We create a labelled set of representative questions, expected source passages, unacceptable answers, and permission cases. Retrieval is assessed separately from answer generation so the team can tell whether a failure came from the source, parser, index, search, reranker, prompt, or model.
Yes, when the source identity, document metadata, and application authorization model support it. Permission filters must be applied before retrieved text reaches the model, then tested with positive and negative cases. A RAG layer should never broaden access beyond the source system's approved policy.
A focused system for one use case and one or two sources starts around $15,000 and usually takes 4 to 8 weeks. Custom connectors, scanned content, several permission tiers, larger corpora, custom interfaces, and stricter evaluation requirements increase the scope.
Work with us
Show us the approved sources, user groups, and evidence a correct answer should cite. We will scope one measurable retrieval workflow.