Services · Retrieval infrastructure
RAG Development Services
Make your documents useful inside the software your team already uses. Code and Trust builds retrieval systems that find relevant source material, respect agreed data access rules, and give AI answers something a person can check.
Scope Your RAG Project →Builds start from $15,000, depending on scope.
What are RAG development services?
Retrieval-augmented generation connects a language model to a searchable collection of your content. A question triggers retrieval of relevant passages; the model receives those passages as context and generates an answer with source references. RAG development services build the ingestion, indexing, access controls, retrieval and evaluation around that exchange. They do not retrain the model on your documents.
The buyer problem is often a disconnected document library: policies in one place, product records in another, and answers that change with every revision. Custom RAG development makes that material available through an application or internal tool, with a defined path from source document to answer.
Our guide to building a RAG system explains the architecture. This service covers designing and delivering it around your own sources, users and acceptance criteria.
The retrieval work behind a useful answer
Source ingestion and index freshness
Start by identifying the authoritative sources and who maintains them. Documents are split into passages, with metadata that connects each passage to its origin. Agree how additions, revisions and deletions reach the index, how failed imports are noticed, and what happens while a source is out of date. A successful import is different from proof that the right passage can be found.
Permissions before generation
Decide which content each user or customer may retrieve before passing it to a model. Metadata such as customer, document version and access level helps filter the search. Include restricted documents in the test cases: an answer should not expose a passage simply because it happens to be a strong semantic match.
Retrieval, citations and source inspection
Choose document boundaries, embeddings and search settings against questions from the actual workflow. Where appropriate, compare keyword and vector retrieval or reranking rather than assuming one search method fits every collection. Carry source references through to the answer so a reviewer can inspect the supporting passage and its version.
Storage should fit the system you operate. Our database design practice covers the underlying data layer; PostgreSQL with pgvector is one option when keeping vectors alongside existing records makes sense.
Test retrieval separately from the final answer
Before launch, agree on a set of real questions, their supporting passages, and what a correct response should contain. Check whether retrieval found the evidence first, then whether generation used it correctly. This makes a missing document distinguishable from a poorly formed answer, so changes address the actual failure.
- Unanswered questions: include questions the collection cannot support. Define when the application says it cannot answer or routes the request for review.
- Stale or conflicting material: test revised policies and competing versions. Decide which source wins and how obsolete passages leave the index.
- Unauthorized questions: test the same question under different access permissions, including attempts to retrieve another customer’s records.
- Regression checks: rerun the agreed examples when prompts, models, retrieval settings or source content change.
Grounding can reduce unsupported answers; it cannot eliminate hallucinations. Citations also need checking against the claim they supposedly support. Evaluation thresholds and review rules belong in the written scope, alongside the circumstances in which the system should decline to answer.
Choose the right project boundary
A RAG project owns the path from source content to grounded response. It can feed an existing application, an internal search tool or a conversational interface. If the main need is a support conversation, qualification flow or human handoff experience, see AI chatbot development. If software must take actions with tools, our agent factory addresses that additional scope.
Our broader AI implementation service starts with the operational workflow and may include extraction, routing or other AI software. Retrieval-augmented generation development services are appropriate when finding and using the right evidence from your own content is the central engineering problem.
Sometimes a maintained help page, ordinary database lookup or search interface is enough. We assess that choice before adding model calls. Provider integration and evaluation approaches are covered in our OpenAI and Anthropic expertise pages.
Scope, cost drivers and handoff
The useful starting point is a sample of the content, representative questions, the users who need access, and the application that will consume the answers. Scope depends on source cleanliness and count, update frequency, permission complexity, integration depth and the breadth of the evaluation set. Model usage, embedding refreshes and storage also affect ongoing operating cost.
We agree the deliverables, price and acceptance criteria before the build. Prototype, staging, and deployment milestones are agreed in the written scope. The handoff covers the agreed source connections, indexing process, retrieval configuration, evaluation cases and application integration. Deploy the software, prepare the team handoff, and provide the agreed post-launch support. Source code and IP transfer on final payment. Post-launch support and maintenance are scoped per engagement.
Bring the questions your current search cannot answer, plus examples where it returns the wrong material. Those are more useful for scoping than a target chatbot feature list.
Talk Through Your Sources →