A RAG agent that knows your business, not just the internet.
Retrieval-augmented generation means it answers from your real content, your docs, specs, and pricing, instead of making things up. We build custom RAG agents for chat and voice.
What a RAG agent actually is.
A normal AI model answers from whatever it was trained on, so it guesses about your business and gets it wrong. A RAG agent does something different. Before it answers, it retrieves the relevant facts from a private knowledge base of your real content, then answers from those. The reply is grounded in your business, with the model only deciding how to say it.
A raw model will confidently get you wrong.
It guesses your prices
Ask a plain model what you charge and it’ll make up a number, stated with total confidence.
It invents specs
It fills gaps with plausible details that aren’t true, which is the last thing you want in front of a customer.
It can’t point to a source
There’s nothing real behind the answer, so you can’t trust it and neither can the person asking.
Retrieve first. Then answer.
We ingest your content
Your website, docs, specs, FAQs, case studies, and even your videos go into a private knowledge base it can draw from.
We index it for retrieval
That content is turned into a searchable index (we use Gemini embeddings and a Pinecone vector store), so it can find the right passage in milliseconds.
It retrieves, then answers
When someone asks, it pulls the relevant facts from your knowledge base first, then answers from those, in your voice.
It hands off when it should
If the answer isn’t in your materials, it routes the person to a human instead of guessing. No invented facts.
Grounded answers, wired into your business.
Trained on your content
A private knowledge base from your docs, specs, FAQs, and case studies. Its answers come from your material, not the open internet.
Retrieves before it answers
Every reply is grounded in a real passage it pulled from your knowledge base, so the facts are yours.
Won’t hallucinate
When the answer isn’t in your materials, it says so and hands off, instead of inventing a price or a spec.
Chat and voice
The same retrieval brain works over text chat and a natural phone voice.
Surfaces your sources
It can pull up the exact product page, doc, or video a question maps to, right in the conversation.
Captures and scores leads
It spots buying intent, collects contact details, scores the lead, and syncs it to your CRM.
One-line install
A single script tag drops it onto any website, with no replatforming.
Gets sharper over time
Add new content and it knows it immediately. Unlike a custom GPT, it isn’t frozen the day you build it.
A generic model guesses. A RAG agent retrieves.
That’s the line between a clever demo and something you can put in front of real customers. We built one for a manufacturer’s full catalog, specs, pricing, case studies, and a library of product videos, answering buyer questions in chat and voice without ever inventing a number it couldn’t back up.
Pulled straight from your spec sheet. Not a guess.
Questions, answered.
What is a RAG agent?
RAG stands for retrieval-augmented generation. Instead of answering from whatever a model was trained on, a RAG agent first retrieves the relevant facts from a private knowledge base of your real content, then answers from those. So the answer is grounded in your business, not a guess.
How is it different from a custom GPT?
A custom GPT runs off a short set of instructions and is frozen the day you build it. A RAG agent answers from a live, updatable knowledge base of your real content, so it stays accurate and gets smarter every time you add material.
Will it make things up?
No. It answers from the passages it retrieves, and when the answer isn’t in your materials it hands off to a human instead of inventing a spec or a price. That’s the whole point of grounding it in retrieval.
What does it run on?
We build it on a vector knowledge base (Gemini embeddings with a Pinecone vector store) paired with a GPT-class model, deployed on secure infrastructure. You don’t have to think about any of that, but it’s real engineering, not a wrapper.
Can it do voice as well as chat?
Yes. The same retrieval works behind a text chat widget and a natural phone voice, so callers and website visitors get the same grounded answers.
How long does it take to build?
Most builds go live in one to two weeks after a short discovery call. We work under an NDA and on secure infrastructure.
Often deployed together.
Conversational AI
One agent that answers every call, text, and chat in your business's voice, around the clock.
AI Operating System
You already have AI tools. What you don't have is a system. We connect every agent, automation, and dashboard into one operating layer your whole business runs on.
Virtual Receptionist
Answers every call and books the job, with no per-minute answering-service bill.
See where the leaks are. Free strategy session.
30 minutes. We map where time and money slip through the cracks and show you exactly what would be possible with the right systems in place. No pitch, no pressure.