Answers Your Security Team Will Sign off on: Grounded, Cited, Access-Controlled.
Most enterprise knowledge tools answer from a model's memory, not your documents. CreateOS forward-deployed engineers build retrieval that grounds every answer in your own documents and systems of record, with citations, freshness, and access controls, delivered on the unified AI execution layer.
- ISO 27001 and SOC 2 Type II certified
- Grounded and cited by default
- Access scoped per role
- Zero data retention by default
The Gap is Grounding, Not the Model
Knowledge tools that answer from a model's memory cannot be cited, scoped, or audited. CreateOS builds the retrieval pipeline that closes that gap: every answer grounded in your documents, cited to the source page, and scoped to what each user is allowed to read.
of enterprise AI pilots never reach production.
MIT NANDA, 2025
of enterprises already run AI projects, then hit the wall when answers cannot be cited or access cannot be scoped.
McKinsey, 2025
average breach cost in financial services, the exposure that ungoverned, uncited AI answers add to regulated industries.
IBM, 2025
What We Deliver
Each layer of the retrieval pipeline built for production, not a demo that stalls at the security review.
Document ingestion and chunking
We ingest your documents in any format, PDFs, scans, emails, spreadsheets, mixed-format files, and chunk them with layout awareness so context is preserved across page boundaries and document types.
Embeddings and vector retrieval
We build and maintain the vector index against your corpus. Embedding models are benchmarked against your own documents, not generic leaderboards. Retrieval is scoped and auditable from the first call.
Reranking and relevance
Candidate passages are reranked for relevance before they reach the model. Cross-encoder reranking and query-expansion logic reduce noise so the model answers from the right sources, not the nearest ones.
Citations and grounding
Every answer carries citations back to the source document and page. Citation validation runs before a response reaches the user, so the system cannot assert something the retrieved passages do not support.
Freshness and re-indexing
Documents do not go stale silently. Re-indexing pipelines keep the vector store current as your corpus changes, and staleness signals surface in the answer layer so users know when a source has been updated.
Access control and least privilege
Retrieval is scoped to what each user is allowed to read. Role-based access filters run at query time, not index-build time, so a user never receives a passage from a document outside their permission boundary.
How an Engagement Works: The Production Path
A staged path from concept to governed production. Value lands early and governance holds at every step.
- 01
Discover
We identify the highest-impact retrieval use case, scope your corpus and access model, and produce a build spec and production roadmap. Fixed pricing agreed in writing.
- 02
Prove
We stand up a scoped RAG pilot on the execution layer, grounded in your own documents and governed from the first call, to prove citation accuracy and relevance against real queries.
- 03
Productionize
Forward-deployed engineers harden it: re-indexing pipelines, citation validation, access controls, output validation, and a full audit trail.
- 04
Scale
It goes live across your corpus, then spreads. Index lifecycle management, monitoring, and retrieval quality improvement on the layer you keep.
Proof: RAG over a 400+ page contract
CreateOS built a litigation intelligence system that reads a 400+ page contract and surfaces the dozen clauses that bear on a live litigation question, each with surrounding context and a citation back to the source page. The system runs over the firm's own documents, on infrastructure the firm controls, with zero data retention by default. The output is a source-cited Timeline Brief ready before the first strategy meeting.
Less manual Timeline Brief preparation time, subject to matter complexity and document quality.
Relevant clauses surfaced from a 400+ page contract, each with surrounding context preserved.
Deployment modes: CreateOS cloud, firm environment, or fully on-premise, with zero data retention by default.
Why CreateOS for RAG
Grounded with citations, not free-text guesses
Every answer cites the source document and page. Citation validation runs before the response reaches a user. The system cannot assert something the retrieved passages do not support.
Access scoped per role, no cross-boundary leakage
Retrieval filters run at query time against your permission model. A user receives only passages from documents they are allowed to read, regardless of what the index contains.
Freshness so answers do not go stale
Re-indexing pipelines keep the vector store current as your corpus changes. Staleness signals surface in the answer layer so users know when a source has been updated, not after a stale answer has been relied on.
You own the index and the IP
The vector index, embedding logic, retrieval pipeline, and all IP are yours outright. We document everything and train your team to manage what has been built.
Common Questions
How do you prevent hallucination in a RAG system?
Hallucination in RAG comes from two sources: the model asserting something the retrieved passages do not contain, and retrieval returning the wrong passages in the first place. We address both. Citation validation checks every assertion against its cited passage before the response reaches the user. Reranking and relevance tuning reduce the chance of noise passages reaching the model. Where the corpus is silent, the system is configured to say so rather than guess.
How do citations work in practice?
Every answer carries citations linked to the source document and page. The citation is generated from the retrieved passage, not inferred from the model's training data. A user can inspect the exact passage the answer was drawn from. Citation validation runs before the response is returned, so an answer that cannot be grounded in a retrieved passage is flagged rather than surfaced.
How do you handle access control and permissions?
Access filters are applied at query time, not at index-build time. When a user submits a query, the retrieval layer checks their permission profile and restricts candidate passages to documents they are allowed to read. A user never receives a passage from outside their permission boundary, even if the document exists in the same index. Role-based and document-level access models are both supported.
How do you keep the index fresh as our documents change?
Re-indexing pipelines monitor your document corpus and update the vector store when documents are added, changed, or removed. You can configure re-indexing on a schedule or on document-change events. Staleness signals are surfaced in the answer layer so users can see how current the sources behind an answer are.
What does a RAG engagement with CreateOS cost?
Engagements run on fixed-scope pricing, not hourly retainers. A discovery sprint and first pilot scope is agreed in writing before any build begins. Cost depends on corpus size, document complexity, access model requirements, and deployment mode. Ongoing index management and monitoring run on milestone-based contracts.
How long does it take to get a RAG system to production?
A scoped pilot over a defined corpus can go live in two to four weeks. Full production with access controls, re-indexing pipelines, and citation validation typically lands in eight to twelve weeks, depending on corpus complexity and the number of retrieval layers required.
Where does our data live and who owns the index?
In your environment. CreateOS runs in your VPC or on-premise, with region-locked compute and zero data retention by default. The vector index, embedding logic, retrieval pipeline, and all associated IP are yours outright after the engagement. No document or query is retained by CreateOS after the session ends.
Where do you want to start?
Bring one corpus where answers need to be grounded and cited. We will take it to governed production on the execution layer.