01
Policy and operations
Answer internal questions from approved procedures, with citations and role-aware access.
ServicesRAG development
Retrieval-augmented generation
Enterprise RAG Development Services
ResoluteX builds retrieval-augmented generation systems for company knowledge, support, policy and document workflows. The useful part is not the chat box. It is ingestion, permissions, retrieval quality, citations, evaluation and keeping the index correct as source material changes.
Answers tied to approved evidence, with a measurable retrieval pipeline behind them.
Where this work fits
02
Customer support
Ground product answers in current documentation and preserve the source trail during human handoff.
03
Document intelligence
Search, compare and extract evidence across contracts, reports, manuals and regulated records.
04
Product knowledge
Give teams one retrieval layer across documentation, tickets, release notes and structured product data.
What you receive
- 01Source connectors and a repeatable ingestion pipeline
- 02Chunking, metadata, access filters and index strategy
- 03Retrieval and answer-quality evaluation datasets
- 04Citations, refusal behaviour and human escalation
- 05Monitoring for freshness, latency, quality and model cost
How delivery works
- 01
Audit the corpus
We inspect source formats, ownership, update frequency, permissions and contradictions before choosing an index.
- 02
Build retrieval first
Search quality is measured independently from generation so weak evidence cannot hide behind fluent answers.
- 03
Evaluate real questions
A golden set represents the questions, sources and edge cases that matter to the operation.
- 04
Operate the pipeline
Freshness jobs, tracing and quality checks make the system maintainable after the first successful demo.
A good fit when
- The organisation has a defined corpus and people who can judge correct answers
- Citations or source traceability matter to the user
- Permissions and document freshness can be modelled explicitly
Probably not the right fit when
- Source documents are not owned, current or trustworthy
- A normal database query or site search would answer the problem more reliably
- There is no plan for who will maintain the corpus after launch
Questions buyers ask
Before you scope the work
What does a RAG system cost?
A focused system for one corpus and one workflow can start in a fixed sprint from $2.9k. Multiple sources, complex permissions, document extraction, multilingual retrieval and production integrations increase the scope.
Which vector database do you use?
We choose after measuring the corpus and operating needs. PostgreSQL with pgvector is often enough; dedicated services such as Pinecone, Qdrant or Weaviate fit other scale and operations profiles.
How do you measure RAG quality?
Retrieval is tested against expected documents and generation against supported answers, citations, refusals and task-specific criteria. Those evaluations run again when prompts, models or indexes change.
Can RAG respect document permissions?
Yes, when identity and source permissions are available. Access filters must be applied during retrieval, not added as a prompt after restricted content has already been retrieved.
Related services
Describe the outcome in one sentence. We will return a fixed sprint plan within one business day.
Scope a sprint