DevelopmentRAG chatbots trained on your own documents and data. Every answer cites its source, and the bot hands off to a human when it isn't sure. Most builds cost $1,500–$12,000 and go live in 1–10 weeks.
Custom AI chatbots that answer from your documents, product data and systems, so customers and teams get accurate answers with sources, not guesses.
Website & Support Chatbots
Answer customer questions 24/7 from your help center, policies and product pages, with a smooth hand-off to a human.
RAG Knowledge Assistants
An AI chatbot trained on your own data. Your team asks questions of SOPs, contracts and wikis and gets answers linked to the source.
WhatsApp, CRM & Enterprise Assistants
Secure sign-in, role-based access and integrations with WhatsApp, CRM, HR and internal systems. Self-hosted models available.
(How It Works)
A generic chatbot guesses. A RAG chatbot looks up the answer in your documents first, then replies based only on what it found.
(Pricing)
Clear price ranges before you book a call. Running costs depend on usage, and we estimate them on the call.
Final quote after a free 15-minute call. Optional support plan after launch.
A RAG (retrieval-augmented generation) chatbot looks up the right passage in your own documents or database first, then writes its answer from that passage. That is why it can quote its source, stay accurate when your information changes, and say "I don't know" instead of guessing. Most business chatbots we build use RAG.
| RAG | Fine-tuning | |
|---|---|---|
| What it changes | What the model can look up | How the model writes and behaves |
| Best for | Facts, documents and data that change | A fixed tone, format or very narrow task |
| Updating knowledge | Replace a document, live in minutes | Retrain the model |
| Shows its sources | Yes | No |
| Typical starting point | Most business chatbots | Rarely needed on day one |
We recommend fine-tuning only when there is a clear reason for it, and we tell you on the first call if your case needs it.
| Industry | What the chatbot does |
|---|---|
| Enterprise and internal teams | Answers staff questions from SOPs, contracts and HR policies, with access by role. See our Abee case study. |
| Healthcare and research | Answers from published research instead of the model's memory, like Sakkar AI, which retrieves from medical research sources. |
| E-commerce | Order status, returns and product questions, with hand-off to support. |
| SaaS products | In-app help, onboarding questions and AI search over your docs. |
| Professional services | Client FAQs, policy questions and intake before a call. |
(Process)
Our Process
We learn what the chatbot must answer, review your documents and systems, and sign an NDA.
We index your content, build the chatbot and connect it to your website, CRM or WhatsApp.
We test it against your real questions, launch, and keep improving answers with an optional support plan.
(AI chatbots we've shipped)
Recent Works
Project Name
Abee
Secure AI assistant answering from company documents, connected to CRM and HR. 50% faster tasks, zero security incidents.
Industry
Enterprise
Project Scope
RAG AI Assistant
Powered By
Flutter, NodeJS
Origin
France
Project Name
Sakkar AI
Retrieval-based AI assistant that answers from medical research sources, on mobile and web.
Industry
Healthcare
Project Scope
AI Research Assistant
Platforms
Mobile & Web
Project Name
Pathy AI
An AI product with intelligent, assistant-style workflows.
Industry
AI Product
Project Scope
AI Workflows & Assistant
(Testimonials)
Helping you understand our process and offerings at Emergen.
In 15 minutes we'll look at your use case, tell you what's realistic and give you a price range.
(FAQs)
Straight answers about this service, our process and pricing.
A RAG (retrieval-augmented generation) chatbot looks up the relevant passages in your own documents or database before it answers, then writes a reply based on them. Answers are more accurate, can link to their sources, and you update the bot's knowledge by updating documents, not by retraining a model.
A website or FAQ chatbot typically costs $1,500–$3,000, a RAG knowledge chatbot $3,000–$6,000, and an enterprise AI assistant with sign-in, role-based access and CRM/HR integrations $6,000–$12,000. Running costs (AI usage and hosting) depend on token usage, and we estimate them with you before the build.
A website or FAQ chatbot usually takes 1–2 weeks, a RAG knowledge chatbot 3–5 weeks, and an enterprise AI assistant 6–10 weeks. Clean, up-to-date documents are the biggest factor in going faster.
The bot answers only from content it retrieved from your data, links to its sources, and hands off to a human when it isn't confident. Before launch we test it against a question set agreed with you and re-run that set after every change.
Yes. We use role-based access, encryption and per-user document filtering, so people only get answers from data they're allowed to see. We sign NDAs and data processing agreements, and can use private or self-hosted models for sensitive data.
Yes. We can deploy on your website, inside your web or mobile app, on WhatsApp through the WhatsApp Business Platform, and connect to CRMs, helpdesks and HR systems through their APIs.
We build with language models from OpenAI or Anthropic and a vector database such as Supabase pgvector or Pinecone, on a Next.js, React, Node.js and Supabase stack, chosen to fit your data, security needs and budget.
Usually not. Most business chatbots need accurate access to your information, which RAG handles. Fine-tuning helps when you need a very specific tone or output format, and we'll tell you if your case calls for it.
Yes. We connect the chatbot to your documents, help center, product data or database using RAG, so it answers from your information instead of general internet knowledge.
Yes. An optional support plan covers monitoring, improvements and updates to the knowledge base.
Let's Connect
Let's build your product.
Tell us what you're building. We'll come back with next steps, a rough timeline and a cost range.
Tell us what you're building. We'll come back with next steps, a rough timeline and a cost range.
