A production-ready enterprise RAG system typically takes between six and sixteen weeks to build, depending heavily on document complexity, the number of source systems that need connectors, and whether access control and on-premise deployment are required, though a narrow proof of concept over a single, clean document set can be demonstrated in as little as two to three weeks. The proof-of-concept stage, which validates retrieval quality and answer accuracy on a representative document sample, is usually the fastest part; the work that extends the timeline is building reliable connectors to source systems like SharePoint, Confluence, or SAP, implementing permission-aware access control that mirrors existing system permissions, handling difficult document formats such as scanned PDFs and complex tables, and building an evaluation pipeline that proves the system is accurate enough to trust with real users. Projects that must run fully on-premise add time for GPU infrastructure provisioning and Kubernetes deployment before any RAG-specific work can begin. Teams that skip the evaluation and access control stages can appear to finish faster but typically discover reliability and security gaps once real users start relying on the system daily. Nanobase AI scopes realistic timelines against document complexity and integration count at the start of every RAG engagement rather than quoting a generic estimate.
A timeline is only useful with gates attached to each phase
Quoting a single number of weeks for a RAG build hides the more useful information: what has to be true before moving from one phase to the next, and which phase is actually consuming the time in a given project. A phase-by-phase plan with explicit gating criteria lets a team see exactly where a project is running on schedule or slipping, rather than discovering at week fourteen of a sixteen-week estimate that access control was never actually finished. A RAG timeline is more useful broken into phases with gating criteria than expressed as a single number of weeks.
A representative phased plan
| Phase | Typical duration | Gate to move forward |
|---|---|---|
| Proof of concept | 2-3 weeks | Retrieval and answer accuracy validated on a representative document sample against a small labeled set |
| Source system connectors | 2-4 weeks | Reliable, repeatable ingestion from all required systems, including permission metadata |
| Access control implementation | 1-3 weeks | Retrieval correctly mirrors source system permissions, verified with test accounts at different access levels |
| Hybrid search, reranking and tuning | 1-2 weeks | Golden test set metrics meet an agreed accuracy threshold |
| Evaluation and monitoring setup | 1-2 weeks | Dashboards and the golden test set are running automatically, not manually |
| Production hardening and rollout | 1-2 weeks | Load testing passed at target concurrency, citation and access control verified end to end |
Key takeaway: the proof of concept is usually the fastest phase; connectors, access control, and evaluation are what extend a project from a few weeks to several months.
What actually stretches a timeline beyond the baseline
- Legacy source systems with no clean API, requiring custom scraping, screen automation, or a manual export process instead of a straightforward connector.
- Difficult document formats, particularly scanned PDFs, complex nested tables, or documents in multiple languages that need specialized parsing beyond standard text extraction.
- Complex or inconsistent access control models in the source systems, where permissions do not map cleanly to simple role-based rules and require case-by-case logic.
- Late-discovered compliance requirements, such as a data residency rule surfacing after infrastructure decisions were already made, forcing a redesign of the deployment target.
- Skipping a formal evaluation phase, which does not actually save time; it usually surfaces as accuracy problems discovered by real users after launch, requiring rework under worse conditions than a planned evaluation phase would have.
- On-premise infrastructure provisioning, since GPU hardware procurement and Kubernetes cluster setup can add weeks before any RAG-specific work begins if this infrastructure does not already exist.
Key takeaway: integration complexity and access control, not the core RAG logic itself, are what most often push a project past its baseline estimate.
Why skipping phases creates false speed
Teams under pressure to show progress sometimes skip access control or evaluation phases to reach a demo faster, and this does produce a working-looking system sooner. The cost surfaces later and is usually more expensive than the time saved: an access control gap discovered after real users are relying on the system daily requires urgent remediation under pressure, and an accuracy problem discovered through user complaints damages trust in a way that a pre-launch evaluation phase would have caught quietly. This is the same reasoning behind treating a golden test set as a required deliverable rather than optional polish.
Key takeaway: skipping access control or evaluation phases to hit an earlier demo date usually costs more time later than it saves upfront.
Frequently asked questions
Can the proof-of-concept and connector phases run in parallel?
Partially. Proof-of-concept work on retrieval quality can start against a sample document set while connector development proceeds separately, though the two need to converge before the access control phase, since permission-aware retrieval requires real connector output, not sample data.
Does an on-premise requirement always add significant time?
Only if the infrastructure does not already exist. Organizations with an existing GPU cluster and Kubernetes environment can proceed at roughly the same pace as a cloud-based project; those provisioning infrastructure from scratch should budget additional weeks before RAG-specific work can start.
How much time should evaluation take relative to the rest of the project?
Evaluation setup itself is usually one to two weeks, but it should run continuously alongside every other phase rather than being scheduled as a single isolated block, since retrieval and access control changes both need to be checked against the same evaluation set as they are built.
Is a faster timeline possible by using a managed vector database?
Yes, using a managed vector database and hosted model API can meaningfully shorten the proof-of-concept and infrastructure phases, though it does not reduce the time needed for connector development, access control, or evaluation, which are independent of the deployment model chosen.
How Nanobase AI helps
Nanobase AI scopes realistic timelines against document complexity, connector count, and access control requirements at the start of every RAG engagement, breaking the project into the gated phases above rather than quoting a single generic estimate. See our cost breakdown for enterprise RAG for how these phases map to budget, or our solutions for the full engagement scope.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.