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AI AGENTS · RAG · MULTI-AGENT
AI that answers from your data, not guesses.
Production-grade chatbots and multi-agent systems trained on your documents, policies and products, with guardrails, source citations and monitoring built in.
Typical timeline: 2–6 weeksor chat on WhatsApp
In plain words
What is a RAG chatbot?
A RAG chatbot (retrieval-augmented generation) is an AI assistant that searches your own documents before it answers. Instead of guessing, it finds the relevant policy, manual or product page, writes an answer from it and shows the source. That keeps answers accurate, checkable and easy to update when your documents change.
What changes
What changes for your business?
Customers wait hours for answers that already sit in your documents.
They get an accurate answer in seconds, with the source attached.
Generic AI tools make things up about your products and policies.
Answers come only from your approved data, and it says so when the answer is not there.
Your team spends hours researching and drafting the same reports.
A team of AI agents researches, writes and reviews drafts for you to approve.
Who it's for
Built for businesses like yours

Customer support
Resolve common questions instantly and route the rest with full context.

SaaS & e-commerce
Answer product, order and policy questions from your help centre and catalogue.

Professional services
Search contracts, policies and past work in seconds.
Features
Everything your AI agent can handle
RAG chatbots on your knowledge base
Answers from your documents, help centre and product data.
Multi-agent workflows
Specialised agents research, write and review, like a small team.
Source-cited answers
Every answer shows where it came from, so anyone can check it.
Guardrails & data protection
Stays on topic, protects private data and refuses risky requests.
Human handover
Hands the conversation to your team when it is unsure.
Usage & quality analytics
See what people ask, how well it answers and what to improve.
Deliverables
What you get
- A chatbot or agent system trained on your documents
- Source citations on every answer
- Guardrails and data-protection rules
- Human handover flow
- Usage and quality dashboard
- Test results on your real questions before launch
- Documentation and team handover
Process
From idea to deployment
Step 1: Use-case discovery
We pick the questions or tasks worth automating and agree what a good answer looks like.
Step 2: Knowledge & agent design
We organise your documents and design how the agent, or team of agents, should work.
Step 3: Build & integrate
We build the system and connect it to your website, app or internal tools.
Step 4: Evaluate & launch
We test answers against your real questions, add guardrails and go live with monitoring.
Proof
Built by engineers who've shipped this before
Projects our founders built personally or in previous roles. They are not Upaay Labs client work.
FAQ
Questions about AI Agents & Chatbots
Will it make things up?
It is built to answer only from your approved documents, and to say so when the answer is not there. Every answer shows its source, so you can check it.
Is my data safe?
Your documents stay in accounts you control wherever possible, and access is limited by role. We never use your data to train public AI models.
What can it connect to?
Websites, apps, WhatsApp, Slack, Google Drive, Notion, help desks and databases. If a tool has an API (a way for software to talk to it), we can usually connect it.
How do answers stay up to date?
When you update a document, the agent picks up the change. We can also schedule automatic syncs from your drive or help centre.
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Have a project in mind?
Tell us what you're looking to build. We'll understand your requirements, recommend the right solution and share a tailored proposal.
No fixed packages. No unnecessary commitments. Just solutions built around your needs.