RAG development services
Private Knowledge AI That Answers With Evidence.
CoreRiqo Labs builds secure retrieval-augmented generation systems that search approved company knowledge, respect permissions, return citations, and improve through evaluation.
ORIGINAL CORERIQO LABS DEMONSTRATIONThe operating problem
Company knowledge needs grounding, permissions, and evaluation.
Generic chatbots cannot reliably answer questions about private policies, contracts, manuals, support history, or structured company data. Search alone often misses intent, while ungrounded generation can produce plausible but unsupported answers.
How CoreRiqo Labs engineers the solution
We build ingestion and retrieval around the real content structure, combine semantic and keyword search, rerank evidence, enforce user permissions before context reaches the model, return source citations, and test the system against realistic questions.
Engineering scope
Capabilities built into the system.
Hybrid semantic and keyword search
Reranking and context assembly
Permission-aware retrieval
Cited grounded answers
RAG evaluation and monitoring
Reference architecture
Designed beyond the model layer.
A production AI system needs data, interfaces, controls, evaluation, deployment, and ownership—not only a model API.
- 01Approved content and data connectors
- 02Parsing, chunking, metadata, and indexing
- 03Query rewriting and hybrid retrieval
- 04Reranking and permission filtering
- 05Grounded generation with citations
- 06Feedback, evaluation, and retrieval traces
Where it creates value
Practical applications.
Internal knowledge copilots
Give employees one interface for policies, procedures, technical documentation, and institutional knowledge.
Customer support assistants
Ground support responses in approved product documentation, policies, and resolved cases.
Contract and policy research
Find relevant clauses, compare documents, and produce cited summaries for human review.
Multi-tenant knowledge products
Deliver isolated, permission-aware knowledge experiences inside SaaS platforms.
Typical technology stack
Python · OpenAI / Anthropic / open models · LlamaIndex / LangChain · pgvector / Pinecone · FastAPI · PostgreSQL · AWS
Service FAQ
Questions before a build begins.
How do you reduce hallucinations?
We improve task design, retrieval quality, context assembly, citations, structured outputs, refusal behavior, and evaluation rather than relying on a prompt alone.
Can access permissions be preserved?
Yes. Identity and authorization filters are applied before the model receives retrieved context.
Can RAG use databases as well as documents?
Yes. We can combine documents, structured databases, approved APIs, wikis, support systems, and other business sources.
Start with the workflow
Bring us the business problem.
In a focused 30-minute consultation, we will discuss the users, workflow, data, integrations, risk, and the smallest useful next step.
Book your consultation