Enterprise RAG Implementation
Answers from your own knowledge, with the source attached, shown only to the people allowed to see it.
- Typical timeline
- 6 to 14 weeks
- Typical timeline
- Listed below
- 7 deliverables
- Listed below
- No obligation
- Quoted after scoping
- No obligation
Built for
- Organisations with knowledge spread across documents, wikis, tickets and shared drives
- Support, operations, legal or sales teams who spend time searching for answers
- Teams whose first document assistant gave confident wrong answers
You recognise this
- People ask the same questions, and the answer exists somewhere but is hard to find
- A chatbot pilot answered well on the sample set and badly on real questions
- Some documents must only be visible to some people
The problem
Retrieval-augmented generation, or RAG, lets a model answer from your own content instead of from memory. It fails quietly: out-of-date documents, missing permissions, poor search and answers that sound right but cite nothing. The model is the easy part. The work is in the sources, the search, the permissions and measuring whether the answers are correct.
What you receive
- 01
Source audit
A review of the content you want to use: where it lives, how current it is, who may see it and what needs cleaning first.
- 02
Ingestion pipeline
Connectors that bring content in from your real sources and keep it up to date as documents change.
- 03
Permission-aware search
Search that returns only what the person asking is allowed to see, using your existing access rules.
- 04
Answer service
Answers that cite their sources, and say when the answer is not in your content rather than guessing.
- 05
Evaluation set and results
Real questions with checked answers, used to measure accuracy before launch and after every change.
- 06
Interface or integration
A search and answer interface, or integration into a tool your team already uses, such as your intranet or help desk.
- 07
Handover
Documentation, a runbook for adding sources and a working session with your team.
Phases and expected timeline
- 01
Source audit and design
1 to 2 weeks
We review the sources, the permissions and the questions people actually ask, and design the pipeline.
- 02
Pipeline and search build
3 to 6 weeks
Connectors, indexing, permission-aware search and the answer service, built against your real content.
- 03
Evaluation and tuning
1 to 3 weeks
We measure answers against the evaluation set with your subject experts and tune until results meet the bar agreed at the start.
- 04
Deployment and handover
1 to 3 weeks
Release to your users, monitoring in place, and handover to your team.
Typical timeline: 6 to 14 weeks
Typical range for a single, well-defined scope. The clock starts once access and the people we need are in place. Your quote confirms the timeline after a scoping call.
Before we start
- A list of the sources in scope, and an owner for each
- Access to those sources, and to how permissions are managed today
- People who know the content and can check answers during evaluation
- Real questions people ask, collected from support, email or chat
- Your rules on where data may be stored and processed
What you can do afterwards
- People get answers with the source document attached, and can check them
- Restricted content stays restricted
- Accuracy is measured on real questions, so you know where it is good enough and where it is not
- Your team can add new sources without a rebuild
What this work is designed to achieve. Results depend on your systems and your data.
Questions about this package
Not by us. We design the system so your content is used only to answer questions, and we review the terms of any model provider we propose with you, so you can see how they handle your data.
Yes. If your data cannot leave your environment, we can deploy the whole system there, including an open model on your own infrastructure.
That is normal, and it is what the source audit is for. We tell you what to clean first and what can be used as it is.
Because the work depends on your systems, your data and how far the scope reaches. After a scoping call we send a written quote that states the scope, the deliverables, the timeline and the cost. Larger enterprise projects are quoted separately, based on scope, integrations, deployment requirements and ongoing support.
Yes. Say so in your enquiry and we will sign one before you share details of your systems.
Yes. For example, an architecture review can lead straight into a build, and we can quote the combined work as one engagement with one plan.
Handover includes the documentation, runbook and working session your team needs to run what we delivered. If you want us to stay involved, ongoing support is quoted separately.
Get a quote for this package
Tell us about your systems and what you need. We reply with questions or times for a scoping call, then send a written quote that sets out the scope, the deliverables, the timeline and the cost.
Larger enterprise projects are quoted separately, based on scope, integrations, deployment requirements and ongoing support.
Prefer email? info@bastionshieldtechnologies.com
Often considered alongside
Custom AI Agent Development
A production AI agent built around your workflow, with evaluation, permissions and the admin screens your team needs to run it.
AI Infrastructure Deployment
Model serving that behaves like infrastructure: routing and failover across models, self-hosting where you need it, and costs you can forecast.