AI Solutions
RAG services: governed retrieval-augmented generation, designed and built for you
For teams that want an AI assistant to answer from their own policies, records and documents, with sources shown and access controlled. Kriv AI scopes, builds and hands over the system, and sets up monitoring and feedback so you can keep it accurate after launch.
RAG as a service means a specialist team designs and builds a retrieval-augmented generation system for you and sets up its monitoring and governance. RAG, short for retrieval-augmented generation, lets a language model look up your own documents when a question is asked and answer from them, instead of relying only on what it was trained on. Kriv AI delivers RAG services as an engagement: we work out whether RAG is the right pattern, prepare your content, build the retrieval and the application, test the answers against real questions, and run a pilot before production. Scope and price are set after a short discovery call.
definition
What does a RAG service deliver?
A RAG system has two parts. A retrieval part finds the passages in your content that relate to a question. A language model then writes an answer using those passages. Because the facts come from your documents, updating a document changes the next answer with no retraining.
A RAG service is the work of making that dependable for a real team: choosing and preparing the content, setting up the index, controlling who can see what, showing sources, and testing that the answers are right.
when rag
When is RAG the right choice?
Our view: add retrieval when the model needs facts it does not have, such as your policies or current records. RAG is often faster to deploy and easier to update than fine-tuning a model.
Fine-tuning is better when you need a consistent format or style, shorter prompts at high volume, or a smaller model for one narrow task. Many teams use both. Neither makes answers truthful by itself, so test accuracy either way. Our fine-tuning page explains how to choose.
scope
What do our RAG development services include?
Choosing the pattern: prompting, RAG, fine-tuning or a mix, based on the use case and its risk.
Preparing your content: curating the documents, assessing their quality, and removing identifiers where the task does not need them.
Retrieval build: setting up the retrieval index and embeddings, and building the application or workflow that uses them.
Sources and guardrails: answers that point to the documents they came from, with guardrails suited to the use case.
Testing: checking answer quality against your own questions and with your subject experts before launch.
Pilot and handover: a pilot with monitoring and feedback, then production with ongoing governance.
governance
How do you keep a RAG system under control?
A RAG system can show people content they should not see, cite an out-of-date document, or leave no record of what it answered. These are the controls we plan for. They come from our own published delivery guides.
Permissions first. Set who can see which collections of content up front, and limit sensitive indexes to approved roles.
Mask before embedding. Remove sensitive values before content is turned into embeddings. Masking after the fact is too late.
Show the source. Return answers with a citation to the current version of the document, and keep query logs.
Test before and after changes. Require a baseline of retrieval and answer quality before a release, and re-run the tests whenever the index is updated.
Plan for rollback. Keep snapshots so an index can be restored if an update goes wrong.
platforms
Which platforms do you build on?
We design for your chosen platform, including AWS, Azure, Databricks and Snowflake, and your cloud or on-premises setup. Where the data lives and how the model is reached are decided with you during design. For RAG, data typically stays in your vector stores. We never use your data for training models for other clients.
If you need the model itself to run in a private setup, see our guide to private LLMs.
engagement
How does an engagement run?
We offer three shapes: a scoped pilot, a rapid proof-of-concept, and a production deployment. Each starts by framing the use case and its risk and deciding between RAG, fine-tuning or both. Then we prepare the data and design the architecture, build and validate with your experts, run a pilot with monitoring and feedback, and move to production with ongoing governance.
Scope and price are set after a short discovery call.
not first
What do we not recommend?
We do not recommend sending sensitive data to unvetted endpoints, building ungoverned shadow AI, or launching a system with no named owner and no guardrails. We also do not recommend starting with a full rollout before a pilot has shown that the answers hold up on your own questions.
how kriv helps
How Kriv AI helps
Kriv AI is a consultancy. This page describes our delivery practices, not certifications. Our delivery aligns with frameworks like the NIST AI RMF at a practical level, and we design for your security requirements with encryption, role-based access and audit logging.
To talk through your documents and your use case, book a discovery call.
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Straight answers
Frequently asked questions about RAG services: governed retrieval-augmented generation, designed and built for you
What is RAG as a service?
RAG as a service means a specialist team designs and builds a retrieval-augmented generation system for you and sets up its monitoring and governance. At Kriv AI it is a service engagement, not a hosted product: we scope it, build it with you, test it and hand it over.
What is retrieval-augmented generation?
It is a way to make a language model answer from your own documents. The system finds the relevant passages when a question is asked and gives them to the model, so updating a document changes the next answer with no retraining.
When should I use RAG instead of fine-tuning?
Add retrieval when the model needs facts it does not have, such as your policies or current records. Consider fine-tuning for a consistent style or a narrow task. Many teams use both, and you should test accuracy either way.
Will my data be used to train models for other clients?
No. We never use your data for training models for other clients. For RAG, data typically stays in your vector stores, and we agree where it lives with you during design.
How do you make RAG answers trustworthy?
We plan for permissions, masking before embedding, citations to the source document, and tests that must pass before each release and after each index update. We also test with your own questions and subject experts.
What does a RAG development engagement cost?
It is scoped and priced after a short discovery call, because it depends on your content, your platform and the use case. Book a call and we will scope it with you.
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