AI Chatbots That Actually Know Your Business
We build RAG-powered chatbots and AI agents trained on your data — so they give accurate, grounded answers instead of making things up.
Generic AI gives generic answers. Your customers deserve better. We build chatbots grounded in your actual documents, knowledge base, and business data using Retrieval-Augmented Generation (RAG).
Our AI systems retrieve the right context before generating a response — eliminating hallucinations and ensuring every answer is backed by your real data. We integrate with OpenAI, Anthropic, open-source LLMs, and custom fine-tuned models.
Whether it's a customer-support bot, an internal knowledge assistant, or a conversational interface for your product, we handle embedding pipelines, vector databases, prompt engineering, guardrails, and production deployment.
What We Deliver
- Custom RAG pipeline (embed → retrieve → generate)
- Vector database setup (Pinecone, Weaviate, pgvector)
- LLM integration (OpenAI, Anthropic, open-source)
- Document ingestion & chunking pipeline
- Chat UI (web widget or standalone)
- Guardrails & hallucination prevention
- API endpoint for integration
- Analytics dashboard for conversations
Tech Stack
Ideal For
- →Businesses wanting to automate customer support
- →Teams building an internal knowledge assistant
- →SaaS products adding a conversational AI feature
- →Enterprises with large document repositories
Frequently Asked Questions
What is RAG and why does it matter?
RAG (Retrieval-Augmented Generation) fetches relevant documents before the AI generates a response. This grounds answers in your real data instead of the model's training data — dramatically reducing hallucinations.
Can the chatbot be trained on our private data?
Yes. We ingest your documents, help articles, PDFs, databases — any knowledge source — into a vector database. The chatbot searches this data in real time to answer questions accurately.
Which LLM do you recommend?
It depends on your needs. GPT-4 and Claude are great for complex reasoning. For cost-sensitive or high-volume use cases, we use fine-tuned open-source models. We help you choose the right model for the job.
How do you prevent the AI from making things up?
We use RAG to ground responses in real data, add citation links, implement confidence thresholds, and set up guardrails that gracefully decline when the bot doesn't have enough context.
Explore Our Other Services
We build across the full stack. See what else we can do for you.
Ready to Get Started?
Book a free 15-minute call. We'll discuss your project, give you an honest assessment, and outline next steps — no obligation.