AI Chatbot Development Services and RAG Chatbots
We build custom AI chatbots that answer from your own content, cite their sources and take real actions in your systems. They run on your website, WhatsApp and the tools your team already uses, in any language. The code and the data are yours.
- years building software
- 9+
- years building software
- clients
- 100+
- clients
- projects
- 150+
- projects
- countries
- 30+
- countries
- Your contenthelp centre, PDFs, wiki, database, tickets
- Ingestion and chunkingcleaned, split, tagged with access rules
- Embeddings + vector searchpgvector, Qdrant, Pinecone and others
- Retrieval and re-rankinghybrid keyword and semantic search
- LLM with guardrailsanswers only from what was retrieved
- Answer with sourcesor a handover to a person
Quick answer
What is AI chatbot development?
AI chatbot development is building a chatbot that understands free-text questions and answers them from your own content using a large language model. CelloIP builds custom RAG chatbots that cite their sources, take actions in your systems and hand over to a person, on web, WhatsApp and other channels, in any language.
AI chatbots we build
Each chatbot starts with one clear job, measured against real questions, then grows into new tasks and channels.
Customer support chatbot
Answers product, order and policy questions from your help centre and documents, creates tickets, and hands over to an agent with the conversation attached.
RAG knowledge-base assistant
Searches manuals, PDFs, wikis and databases, answers with links to the exact source, and says so when the answer is not in your content.
WhatsApp and social messaging bots
AI chatbots on the WhatsApp Business Platform, Instagram, Messenger and Telegram for enquiries, bookings, order updates and lead capture.
Internal assistant for teams
An assistant in Slack, Microsoft Teams or your intranet that answers HR, IT and process questions, with access limited by each person's role.
AI agents that take actions
Chatbots that call your APIs to check stock, book slots, update the CRM or raise refunds, with confirmation steps and limits on what they may do.
One assistant for chat and voice
The same knowledge base and tools behind your chatbot and your AI phone agent, so answers match on every channel.
One knowledge base, every channel
Your content is indexed once. Every channel uses the same retrieval, rules and analytics, so customers get the same answer wherever they ask.
- Website chat widget
- WhatsApp Business Platform
- Instagram and Facebook Messenger
- Telegram
- Slack and Microsoft Teams
- Mobile apps (iOS and Android)
- Help desks such as Zendesk and Freshdesk
- Voice, through an AI phone agent
Accuracy and safety, measured
A chatbot that is wrong confidently does more harm than no chatbot. These controls are part of every build.
- Answers grounded in retrieved sources, with citations users can open
- A clear “I don't know” and a handover when the answer isn't in your content
- Evaluation sets built from real questions, re-run before every change
- Prompt-injection and jailbreak defences on user input and retrieved text
- Personal and card data redacted from logs and prompts
- Role-based access, so each user only sees content they are allowed to see
Hosted LLM or self-hosted model?
We are not tied to one provider. We test candidate models on your questions and recommend one based on answer quality, data rules and running cost.
| Hosted LLM API | Self-hosted open-weight model | |
|---|---|---|
| Typical use | Most customer-facing chatbots | Data that must stay on your servers |
| Examples | OpenAI GPT models, Anthropic Claude, Google Gemini | Llama, Mistral, Qwen and similar open-weight models |
| Where data is processed | The provider's cloud, under its data terms and region options | Your cloud account or on-premise servers |
| Running cost | Pay per token; low at small volume | GPU hosting; can be cheaper at high, steady volume |
| Answer quality | Strongest reasoning and language coverage | Good for focused tasks; needs more tuning |
How much does an AI chatbot cost to build?
Typical project ranges by type of build. You get a fixed quote after a discovery call. Model usage and hosting are separate running costs that scale with message volume.
| Type of build | Typical scope | Typical range | Timeline |
|---|---|---|---|
| Knowledge-base chatbot MVP | One channel (usually the website), one content source, hosted LLM, basic analytics | $6,000 – $15,000 | 3–5 weeks |
| Production RAG chatbot | Several content sources, two or three channels such as web and WhatsApp, CRM or help-desk integration, actions, evaluation and monitoring | $15,000 – $40,000 | 6–10 weeks |
| Enterprise or self-hosted assistant | Role-based access, single sign-on, self-hosted models or private cloud, many languages, chat and voice together | Scoped after discovery | Agreed in the project plan |
Ways to work with us
Fixed-scope project
A defined chatbot delivered against agreed scope, milestones and acceptance tests.
Dedicated AI developer
A CelloIP engineer working inside your team and tools, backed by a 30-day money-back guarantee.
Build, then run
We build the chatbot, then keep content synced, review conversations, update prompts and models, and add new tasks.
How we build an AI chatbot
- 1Week 1
Discovery
Top questions from tickets and chats, content sources, channels, systems to connect, languages and the success metric.
- 2Weeks 1–2
Content and design
Content audit, access rules, conversation design, handover rules and the model and hosting choice.
- 3Weeks 2–6
Build
Ingestion pipeline, retrieval, prompts, tools and channel integrations, with a test link you can use from week two.
- 4Weeks 4–8
Evaluate and pilot
Scored test sets, a staged rollout to some users, and fixes to content gaps the chatbot finds.
- 5Ongoing
Launch and improve
Dashboards for resolution and handover rates, weekly conversation review, and new content and actions.
Project outcome and client reviews
Client names are kept private. Reviews are quoted word for word from our Upwork profile.
Project outcome · RAG knowledge-base chatbot
Built an AI chatbot that answers customer questions from the company's own knowledge base using RAG and OpenAI models. Answers are grounded in the source documents, and conversations the chatbot can't resolve are handed to a human agent.
Read the case study: RAG AI chatbot for a healthcare provider →
- Retrieval over the client's own documents
- Source-grounded answers
- Handover to a person
“It was a pleasure working with Kaushik and the rest of the knowledgeable team at Celloip. I would highly recommend them for anyone looking for expertise in the VoIP, cellular or mobile application field.”
“Great experience working with Kaushik. He provided valuable insights, quick solutions, and a clear understanding of VoIP infrastructure.”
Custom chatbot or an off-the-shelf builder?
Off-the-shelf chatbot builders are a good start for a simple FAQ bot on one channel. A custom build makes sense when the chatbot must use private or complex content, act in your own systems, run on your infrastructure, follow strict data rules, or become part of a product you sell.
- No per-seat or per-resolution platform fees: you pay for models and hosting
- Your choice of model, vector database and hosting region
- Full ownership of code, prompts and conversation data
Frequently asked questions
What is a RAG chatbot?
A RAG (retrieval-augmented generation) chatbot looks up the most relevant passages in your own content before it answers, then asks the language model to answer only from those passages. That keeps answers current and specific to your business, lets the chatbot cite its sources, and greatly reduces made-up answers compared with a model answering from memory.
How much does AI chatbot development cost?
As a guide, a knowledge-base chatbot MVP on one channel is typically $6,000–$15,000 over 3–5 weeks, and a production RAG chatbot with several sources, channels and integrations is typically $15,000–$40,000 over 6–10 weeks. Enterprise and self-hosted assistants are scoped after discovery. Model usage and hosting are separate running costs that depend on message volume.
How do you stop the chatbot from making things up?
We ground every answer in retrieved sources, show citations, tell the model to say it doesn't know when the sources don't cover a question, and hand those conversations to a person. Before launch and after every change we score the chatbot against a test set of real questions, so accuracy is measured rather than assumed.
Which LLM should our chatbot use: OpenAI, Claude, Gemini or open-source?
Hosted models from OpenAI, Anthropic and Google give the best answer quality and language coverage and suit most customer-facing chatbots. Open-weight models such as Llama or Mistral, hosted on your own servers, suit data that must not leave your environment. We keep the model behind an abstraction so you can switch later, and we compare candidates on your own questions.
Can the chatbot work on WhatsApp?
Yes. We build AI chatbots on the WhatsApp Business Platform, alongside Instagram, Messenger, Telegram and your website, all using one knowledge base. WhatsApp's own rules on message templates, opt-in and the 24-hour customer service window are built into the conversation design.
Which languages can the chatbot handle?
Any language the client needs. Modern models handle most languages well; we test retrieval and answers in each language you use, including mixed languages such as Hinglish, and right-to-left scripts such as Arabic and Hebrew.
Can it connect to our CRM, help desk or database?
Yes. Through tool calls the chatbot can read and update systems such as your CRM, help desk, booking system, order database or ERP. Each action has limits and, where it matters, asks the user to confirm before it runs.
How long does it take to build an AI chatbot?
A focused knowledge-base chatbot usually goes live in 3–5 weeks. A production chatbot with several channels, integrations and an evaluation process usually takes 6–10 weeks. Content quality is often the biggest factor: well-organised help content speeds things up.
Who owns the chatbot, the code and the data?
You do. Source code, prompts, pipelines and infrastructure scripts are handed over, and conversation logs and your content stay in accounts or servers you control. NDAs are signed before discovery.
Related services and guides
AI Voice Agent Development
Phone agents that use the same knowledge base and tools as your chatbot.
Contact Centre Software
Omnichannel contact centres where chatbots and human agents share one queue.
Chat & Messaging App Development
Custom secure chat apps with AI assistants built in.
Hire AI Developers
A dedicated LLM and RAG engineer working inside your team.
Tell us what your chatbot should answer
Share your top customer questions, where your content lives and the channels you need. We'll reply with a recommended approach, a timeline and a fixed quote.
Contact CelloIPBuilding a voice agent too? See AI voice agent development.