RAG pipelines
Ingestion, chunking, embeddings and retrieval tuned to your content so answers cite real sources.
Overview
A language model knows nothing about your business until you teach it
Out of the box, a language model knows a lot about the world and nothing about your product or policies. Retrieval-augmented generation (RAG) fixes that. Your documents are indexed, the right passages are pulled up for each question, and the model answers from those instead of guessing.
We build the whole chain: ingesting and splitting your content, embeddings and a vector store, retrieval, prompts, testing, and then the chatbot or API people actually use. We pick between OpenAI, Anthropic and others per task, based on accuracy, speed and cost.
Tools & platforms we use
- OpenAI API
- Anthropic API
- LangChain
- LlamaIndex
- pgvector
- Pinecone
What's included
More than a prompt in a text box
Chatbots & assistants
Customer-facing or internal assistants with a clear scope and a handoff to a person.
LLM API integration
Works with any provider, with caching, retries, cost tracking and test sets built in.
Questions
Common questions about generative AI projects
6 questions
What is RAG and why use it?
RAG looks up the relevant passages in your own content and hands them to the model along with each question. Answers stay current, can cite their sources, and there's less invention. No retraining needed.
How is this different from a generic chatbot widget?
A generic widget answers from general training data and will happily make up details about your product. Ours searches your actual documentation first, and passes the conversation to a person when it isn't sure.
Does an assistant replace our support team?
Usually not. It takes the repetitive, well-documented questions so your team can spend time on the hard ones.
Should we fine-tune a model instead?
Rarely as a first step. Good retrieval and prompting solve most business cases for far less money. We'd only suggest fine-tuning if testing shows it's needed.
How do you control hallucinations?
We tie answers to retrieved content, require citations, test on real customer questions and give the system a proper way to say 'I don't know'. That cuts the problem down a lot, but no system removes it completely.
Is our data sent to the model provider?
Only what each request needs, under the provider's API terms. We go through data handling with you before building, including private deployment options.
Quick question?
Ask us about Generative AI & LLM Solutions
Not ready for a full brief? Send a question and a developer who works on this will answer it, usually within one business day. No sales call, no obligation.
Already have designs or a scope? Send a full project brief instead.
Ready to get started?
Tell us what your assistant needs to know.
Send us your content and the questions people ask. We'll scope it and give you a fixed estimate.
We'll redo the first milestone at no cost if it doesn't match the brief.
NDA signed before we see anything. Delivered under your brand.