Solutions
RAG Systems & AI Knowledge Retrieval
A RAG (retrieval-augmented generation) system lets users ask questions in plain English and get answers grounded in your own documents and data, not the model's general knowledge. We build the ingestion, retrieval, and answer-quality checks as one piece, not three separate projects.
What goes into a RAG system
Three pieces, built as one pipeline rather than three disconnected projects:
Ingestion. Getting your documents and data into a searchable form, whatever shape they're currently in.
Retrieval. Finding the right passages for a given question, using PostgreSQL or vector storage depending on your scale.
Answer quality checks. Scoring real answers against what “correct” looks like, so you know the system is actually working, not just responding.
Proof this works
Appliance GPT, part of the OnSense AI platform, is a standalone consumer AI chat product that answers questions about appliance error codes, parts, and repairs by retrieving from appliance-specific documentation rather than guessing from general knowledge. It was built solo in React and Next.js and runs live today — the same pattern we'd apply to your own documents and data.
A sample timeline by plan
40 hrs / month
A focused RAG system over one well-defined document set, such as a product manual or support knowledge base.
80 hrs / month
Ingestion, retrieval, and answer-quality evals for a larger or more varied document set, plus the UI to search it.
160 hrs / month
A full knowledge retrieval product: multiple document sources, ongoing quality monitoring, and ownership of the feature as your content grows.
Proof from past work
FAQ
Frequently asked questions
What's the difference between RAG and just using a chatbot API?
A generic chatbot answers from what the model already knows, which means it can confidently invent answers about your product. RAG retrieves the relevant passages from your own documents first, then generates an answer grounded in them.
What counts as 'our own data'?
Product documentation, support history, internal wikis, PDFs, anything structured or unstructured that your users or team need answers from — we handle the ingestion pipeline for the formats you actually have.
How do you store and search the data?
PostgreSQL or vector storage, depending on scale and what you're already running. We don't default to a new piece of infrastructure if your existing database can do the job.
How do you know the answers are actually good?
Answer quality checks: a test set of real questions scored against what a correct answer looks like, so retrieval and generation quality is measured rather than assumed.
Can this plug into a product we already have?
Yes — most RAG work we do ships as a feature inside an existing product, not a standalone tool, which is why AI and full-stack engineers work on it together.