AI Integration for Existing Products is Right.Link's service for adding LLM features to a product that is already live: assistants and copilots, search and answers over your own data (RAG), summaries, extraction and drafting. We fit the feature into your current architecture, respect your users' permissions and data rules, and ship it behind a flag so it can be tested and rolled back. It is built for product teams that have users and a codebase and want an AI feature that works inside them.
Who is AI Integration for Existing Products for?
It is for product companies with a working application, real users and an engineering team that is busy with the roadmap.
Typically that means a SaaS, marketplace or internal platform where users already search, read, write or decide inside the product, and where an AI feature could save them time. You do not need to rebuild anything. You need a clear user task and access to the data behind it.
If you are validating a new AI product idea, see AI Development. If the AI feature is already live and you need it measured, controlled and cheaper to run, see LLM and AI Engineering Foundations.
What AI features can be added to a live product?
These are the patterns we are asked for most:
- Answers over your own data (RAG): users ask a question and get an answer built from your documents, tickets, knowledge base or records, with links to the sources.
- Assistants and copilots: a panel inside the product that explains, drafts or guides, and can take actions the user is allowed to take.
- Summaries: long threads, documents, calls or histories turned into a short, checkable summary.
- Extraction and classification: pulling fields out of uploaded files or free text, and tagging or routing items.
- Drafting: first versions of emails, descriptions, reports or replies that the user edits.
- Natural-language search and filters: turning "overdue invoices over a month for client X" into the filter your product already supports.
The right pattern depends on the user task, not on what is fashionable. We start from the task.
How does Right.Link add AI without breaking the product?
We treat the AI feature as a new component with a clear boundary, not as a rewrite.
- Pick the user task. One task, one user type, one definition of a good result.
- Review the product and the data. Architecture, auth model, where the relevant data lives, how fresh it must be and who may see what.
- Design the boundary. A small service or module that your application calls, so the model provider can change without touching the rest of the product.
- Build the pipeline. Prompts, retrieval, structured outputs and validation, with the user's permissions applied before any data reaches the model.
- Build the interface. Inside your existing design system, with loading, error and "I don't know" states.
- Test on real examples. A set of real questions and expected results, run before release and after every change.
- Release behind a flag. To a small group first, then wider, with a way back.
How does retrieval over company data (RAG) work?
RAG means the model answers from your documents instead of from memory. The quality depends mostly on the retrieval, not on the model.
- Ingestion: getting the content from where it lives (files, SharePoint, a database, a help centre) into a searchable form, and keeping it up to date.
- Chunking and metadata: splitting content so that a passage makes sense on its own, and tagging it with source, date and access rules.
- Retrieval: finding the right passages. Search that matches similar wording is not always search that finds the correct answer, so we test retrieval separately from generation.
- Permissions: a user only gets answers built from content they may see. This is applied at retrieval time, not left to the prompt.
- Citations: each answer links back to its sources so the user can check it.
- Saying "I don't know": when nothing relevant is found, the feature says so instead of guessing.
For the technical story behind one retrieval system we built, read From Similar to Correct.
Which AI providers and models do you use?
We choose by task, data rules and cost, not by brand. We work with the major hosted model providers and, where your requirements demand it, with models running in your own cloud.
We keep the provider behind a thin interface in your product, so you can change model or vendor later without rewriting the feature. Before any of your data is sent to a provider, we check that provider's data terms against your requirements, including retention, training on your data and the region where it is processed.
What do you get at the end?
- A working AI feature inside your product, released behind a feature flag.
- The pipeline behind it: ingestion, retrieval, prompts, validation, and a clean boundary to your application.
- Permission handling that follows your existing access rules.
- A set of real test cases and a way to re-run them.
- Basic logging of inputs, outputs and errors for the feature.
- Documentation and a walkthrough for your team, or a sprint plan if we continue together.
What will Right.Link not do?
- We will not add AI to a product just to have AI in it. If a feature will not help a user task, we say so.
- We will not send your users' data to a provider before you have agreed to that provider's terms.
- We will not hide uncertainty. Where the model might be wrong, the interface shows sources or asks for confirmation.
- We will not claim a feature is ready without testing it on real examples.
Has Right.Link done this before?
One project that relates directly to retrieval over company documents:
- AI Construction Knowledge Platform: an AI-powered knowledge platform for construction asset documentation. It turns blueprints and technical files stored in SharePoint into a queryable system with verifiable answers and real-time data integration.
The case page has the details.
Book a free 30-minute intro call
If this sounds like your situation, Book a free 30-minute intro call. Bring a short description of the process or product and what goes wrong today. You will leave with an honest view of whether we can help and what a sensible first step would be. The quote comes after the call and depends on what is needed.