We build website chatbots, internal knowledge assistants and retrieval-augmented generation systems that answer from approved sources instead of guessing from a generic prompt.
The assistant can search your documents, knowledge base or product information, cite the source it used, and hand off uncertain questions instead of pretending to know.
{
"answer": "Your return window is 30 days.",
"sources": [
"returns-policy.pdf · page 2"
],
"confidence": "grounded",
"handoff": false
}
A focused knowledge base is easier to test and more useful than a chatbot that claims to answer everything.
Choose the documents, pages or product data the assistant is allowed to use.
Chunk, index and retrieve relevant context with source references and sensible limits.
Give visitors a clear interface, useful empty states and a human handoff when needed.
Review real questions, document known limits, and deliver the code and deployment notes.
| Item | Detail |
|---|---|
| Typical scope | One website or internal assistant over an agreed knowledge base |
| Delivery | 1–3 weeks depending on sources and integrations |
| From | $800 build · maintenance available after launch |
| Output | Chat interface, retrieval service, source code and handover docs |
| Privacy | Credentials and data stay in your accounts; retention is agreed in writing |
| Boundary | No guaranteed accuracy, regulated advice or training on customer data without permission |
The current client work is still in progress, so we do not present it as a delivered case study. We can scope a separate assistant around your sources, users and handoff rules.
Retrieval-Augmented Generation connects a large language model to your own documents so it answers questions from your data, not from training-set guesses.
The architecture has three stages. First, your documents — PDFs, support tickets, product manuals, knowledge-base articles — are split into semantic chunks and converted into dense numerical vectors by an embedding model. Those vectors are stored in an indexed vector database such as Pinecone, Qdrant, or pgvector. Second, when a user submits a question, the same embedding model converts the question into a query vector and a similarity search retrieves the most relevant document chunks based on cosine distance. Third, the retrieved chunks and the original user query are assembled into a structured prompt sent to the LLM. The model reads the retrieved context and produces an answer grounded in your actual documents rather than its parametric memory.
The practical result is a chatbot that can accurately answer questions about your specific products, policies, or internal processes — and that can cite the source passage so users can verify the answer. It eliminates the hallucination problem that makes raw LLM responses unreliable for factual tasks.
We build these systems using LangGraph for orchestration, which allows multi-step reasoning loops and tool use beyond simple question-answering. We connect to OpenAI, Anthropic, Google Gemini, and local Ollama models — the architecture is model-agnostic so you are not locked to a single provider.
A focused single-domain chatbot — say, a support bot trained on your documentation — typically takes three to five working days from document ingestion to a tested, deployed endpoint. More complex systems with tool use, multi-step reasoning, or CRM integration take longer and are quoted individually.
PDF, Word (.docx), plain text, Markdown, HTML pages, Notion exports, CSV for structured data, and direct database connections. We write the ingestion pipeline as part of the build so it can be re-run when your documents update.
Yes. We commonly connect RAG systems to Supabase, PostgreSQL, and REST APIs so the chatbot can look up order status, account details, or product availability in real time alongside document retrieval.