Permission-aware assistants
Grounded question answering that respects source-level and user-level access.
KNOWLEDGE WITH PROVENANCE
Make company knowledge retrievable, permitted, and useful.
CodeCradle builds retrieval and knowledge systems that connect approved sources to grounded answers, search, and workflows. Freshness, citations, access control, and evaluation are designed in.
01Approved sources
02Retrieval + permissions
03Cited answer
Freshness and quality are tested against representative questions
05 / THE WORK
What this service is for.
Retrieval-augmented generation can give a model relevant company context at the time of a request. The useful work extends far beyond creating embeddings. Sources must be parsed and synchronized, access rules preserved, retrieval tuned, context assembled, answers cited, and quality tested against the questions people actually ask. RAG improves grounding; it does not guarantee truth.
A SOUND FIT
Start with the operating condition.
- 01
Knowledge is spread across documents, systems, and teams.
- 02
Users need answers that link back to approved source material.
- 03
Different users must see different subsets of the same corpus.
- 04
Search quality and freshness need to be measurable and operated.
WHAT WE CAN BUILD
Concrete capability, not a vague transformation.
Semantic and hybrid search
Retrieval that combines meaning, keywords, metadata, filters, reranking, and source context.
Knowledge workflow infrastructure
Ingestion, parsing, synchronization, provenance, evaluation sets, and review tools.
SYSTEM VIEW
The capability is only one part of the system.
Integration quality depends on the boundaries around it: identity, data, contracts, evaluation, failure behavior, and operational ownership.
01Approved sources
02Retrieval + permissions
03Cited answer
Freshness and quality are tested against representative questions
ENGINEERING POSITION
What matters in production.
Retrieval quality comes first
A fluent answer cannot repair missing or irrelevant context. We inspect ingestion, chunking, metadata, query behavior, ranking, and context assembly.
Permissions travel with the content
Access must be enforced during retrieval. Instructions telling a model not to reveal information are not an authorization system.
Show the source
Where the experience supports it, answers expose citations or provenance so users can verify important claims.
ENGAGEMENT
A reviewable path into the work.
- 01
Inventory sources, ownership, freshness, permissions, and reader tasks.
- 02
Build an evaluation set from representative questions and expected evidence.
- 03
Implement ingestion and retrieval before polishing answer generation.
- 04
Operate synchronization, quality, cost, and source changes after release.
SERVICE QUESTIONS
Before you start.
01Does RAG eliminate hallucinations?+
No. Good retrieval and grounded prompting can reduce unsupported answers, while citations and abstention make limits more visible. Important behavior still needs evaluation and review.
02Can the system respect our existing permissions?+
Yes, when the source systems expose usable identity and access information. We design retrieval filters and authorization around those existing rules.
03Do we need a vector database?+
Not always. The right retrieval stack depends on corpus size, update patterns, query types, metadata, latency, and existing infrastructure. Keyword or hybrid search may be part of the answer.
04How do you test retrieval quality?+
With representative questions, expected relevant sources, answer criteria, failure cases, and review. Retrieval and answer generation are measured separately where possible.
NEXT STEP
Bring us the system, workflow, or product that needs to change.
We’ll help define the smallest sound way forward, then build it with the surrounding software in view.