AI Chatbots
AI Chatbot Development Services
We build AI chatbots that actually know your business — grounded in your content with RAG, not generic and hallucinating.
The challenge
Generic chatbots make things up and frustrate users. You want an assistant grounded in your real content — accurate, on-brand, and safe to put in front of customers.
We build AI chatbots that actually know your business — grounded in your real content with RAG, not a generic model that confidently makes things up. Support assistants, sales bots, and internal knowledge tools that answer from your data or say they don't know, rather than hallucinating.
A chatbot is only as good as its guardrails and its grounding. We keep answers sourced from your content, add tone control and escalation to humans when it matters, and evaluate answer quality so you can see and improve it over time.
They live where your users already are — your website or app, Slack, WhatsApp, or Intercom — with analytics and content controls so you stay in charge of what the bot says.
Most chatbot projects land in one of three shapes: a scoped prototype in 1–2 weeks that proves answer quality on your real content, a production support or sales assistant in 4–6 weeks, or a multi-system agent that takes actions across your tools in 8–10+ weeks. We tell you which one your use case actually needs on the first call — including when the honest answer is that an off-the-shelf tool would serve you better than a custom build.
The builds we see most often: customer-support assistants that deflect the repetitive tickets and escalate the rest with full context, sales bots that qualify leads and book calls, and internal knowledge assistants that answer staff questions from docs, wikis, and tickets nobody can find.
Sound familiar?
If any of this rings true, you're in the right place.
You tried a generic chatbot and it confidently gave customers wrong answers.
You want an assistant that knows your business, not the open internet.
You're worried a bot will go off-brand or off-topic in front of customers.
Your support team answers the same twenty questions every single week.
Your knowledge is scattered across Notion, PDFs, and help articles, and nobody can find anything.
You've been quoted wildly different prices and can't tell what's realistic.
How we solve it
Your problem, and exactly how we remove it.
The problem
Generic bots hallucinate and frustrate users.
How we solve it
We ground the bot in your real content with RAG, so it answers from your data or says it doesn't know.
The problem
Off-brand or unsafe replies are a real risk.
How we solve it
We add guardrails, tone control, and human escalation so the bot stays safe and on-brand.
The problem
You can't tell if it's actually helping.
How we solve it
We add analytics and evaluation so you can see answer quality and improve it over time.
The problem
The same questions consume your support team every week.
How we solve it
We ground a support assistant in your help content so it deflects the repetitive tickets and escalates the rest to your team with full conversation context.
The problem
Your knowledge lives in tools nobody searches.
How we solve it
We ingest docs, wikis, PDFs, and past tickets into one retrieval layer, so staff and customers get the same consistent answer.
What we deliver
Scope of work
Tech stack for this service
- OpenAI
- Anthropic
- Next.js
- Node.js
- Supabase
Why CodeBaxh
What sets this work apart.
Grounded in your data
RAG keeps answers accurate and sourced from your real content, not the open internet.
Safe and on-brand
Guardrails, tone control, and escalation to humans when it matters.
Measured quality
Evaluation and analytics so you can see and improve answer quality over time.
Model-agnostic by design
Swapping between OpenAI and Anthropic models is a config change, not a rebuild — so you are never locked to one vendor's pricing or roadmap.
We tell you when not to build
Under a few hundred conversations a month, an off-the-shelf tool usually beats a custom build. We say so on the first call rather than selling you a project.
How we work
How a AI Chatbots project runs
A calm, visible rhythm from the first call to launch — short loops, weekly demos, and clear updates throughout.
Discovery
We map the use case, the content to ground on, and the channels.
Ground & design
We ingest your content into a retrieval layer and design the conversation.
Build & evaluate
We build the bot with guardrails and test answer quality on real questions.
Launch & tune
We deploy to your channels with analytics, then tune over time.
Proof
Relevant work.
A real project that shows this service in production.
Fixed-scope, retainer, or staff-augmentation engagements available. See engagement models or book a discovery call.
From the blog
Related guides
FAQ
AI Chatbots — FAQs
We ground it in your own content using RAG, add guardrails and fallbacks, and evaluate answers — so it answers from your data or says it doesn't know, rather than inventing.
On your website or app, and in channels like Slack, WhatsApp, or Intercom. We build for the channels your users already use.
Yes. We build escalation paths so the bot hands complex or sensitive conversations to your team smoothly.
A production chatbot grounded in your own data typically takes 4 to 6 weeks. A scoped prototype you can click through takes 1 to 2 weeks, and a multi-system agent that takes actions across your tools runs 8 to 10 weeks or more. The spread comes from the state of your content, how many systems the bot must touch, and how much proof you need that it will not say something wrong to a customer.
Custom chatbot builds are quoted against scope rather than a fixed price list, because the same interface can sit on a single help centre or on six integrated systems. The three tiers above are the honest way to think about budget: a prototype proves answer quality cheaply, a production assistant is the common case, and multi-system automation costs more because the integrations and audit requirements are where the work is. We give you a fixed quote after a free discovery call.
We ground it on your data rather than training a model on it, and the distinction matters. Retrieval-augmented generation looks your content up at question time and answers from the passages it finds, so there is no multi-week training run and updating the bot means re-indexing content, not retraining. Fine-tuning helps with tone and format, not facts, so most production business chatbots ship with a stock model, good retrieval, and careful prompting.
Your content stays yours and is not used to train public models. We use enterprise API tiers from OpenAI and Anthropic, which exclude API traffic from model training by default, and we keep your indexed content in infrastructure you own or control. Where you need it, we scope data retention, access logging, and regional hosting before the build starts.
Yes. Modern models handle multilingual conversation natively, so the bot can answer in the language a customer writes in even when your source documentation is only in English. Where accuracy in a specific language matters commercially, we add that language to the evaluation set so quality is measured rather than assumed.
You can, or we can. We hand over a documented pipeline so your team can re-index content and adjust prompts without us. Most clients keep us on a light monthly retainer to review unanswered questions, refresh the index as documentation changes, and tune the answers that are scoring badly, which is the work that keeps quality improving after week one.
If you handle under a few hundred support conversations a month, an off-the-shelf tool or a well-organised help centre usually beats a custom build on cost. Custom becomes worth it when volume, integration needs, or answer quality outgrow what a template product can do, particularly when the bot needs to read from systems only you have. We will tell you which side of that line you are on before you commit a budget.