AI chatbot development company for support, sales and operations
We design, build and run chatbots and AI agents that answer from your own content, take actions in your systems and hand off to your team when they should.
ZedBlu is an AI chatbot development company that builds assistants for real work: answering customer questions from your help center, qualifying leads, booking appointments, checking order status and updating your CRM. We connect large language models from OpenAI, Anthropic Claude and Llama to your own documents and systems, so replies come from your content rather than the open internet.
A chatbot that sounds fluent is easy to demo. Getting it right on your actual questions, keeping it within clear limits and handing over to a person at the right moment is the hard part. That is where most of our engineering time goes: retrieval tuned to your data, tested flows, guardrails, human handoff and analytics on what customers really ask.
Our custom AI chatbot development work covers startups adding a first support bot, growing companies automating sales and service on WhatsApp, and enterprises that need private deployment and audit trails. You own the source code, prompts and data pipelines, and we stay involved after launch to monitor quality and improve answers.
What our AI Chatbot & Agent Development team delivers
Customer support chatbots
A website chatbot or in-app assistant that resolves common questions about orders, billing, policies and product use. We build it as a RAG chatbot, so it retrieves passages from your help center, PDFs and past tickets before writing a reply. When a question falls outside that knowledge, it says so, collects details and passes the chat to a live agent with the transcript.
WhatsApp and messaging bots
Our WhatsApp chatbot development covers the full Business Platform setup: message templates, opt-in flows, session rules and payment links. We also build bots for Messenger, Instagram and Telegram. Typical jobs include order updates, appointment booking, payment reminders and lead capture, with each bot connected to your order system or CRM so customers finish tasks inside the chat.
AI agents that take action
Our AI agent development goes past question answering. Agents look up an order, reschedule a booking, raise a ticket, draft a quote or update a record through your APIs. Every tool an agent can call has defined permissions, input checks and logging, and sensitive steps such as refunds can wait for a human to approve them.
Internal knowledge assistants
Private assistants for your team that search policies, SOPs, contracts, product specs and wikis, then answer with a link to the source document. Access mirrors your existing permissions, so staff only get answers drawn from files they are allowed to open. Common uses are HR helpdesks, sales enablement, onboarding and first-line IT support.
Voice bots and call automation
Voice assistants for inbound calls that understand natural speech, handle routine requests, capture the reason for the call and route callers to the right team. We combine speech-to-text, a language model and text-to-speech, and design for interruptions, accents and noisy lines. Each call can be summarized automatically into your CRM or helpdesk.
Upgrades for existing chatbots
If you already run a rule-based bot that frustrates customers, we study its transcripts, find where conversations break down and rebuild it on a language model stack. We keep the flows that work, replace rigid decision trees with retrieval and agent tools, and carry over your analytics so you can compare the old and new versions side by side.
What you get
Answers grounded in your content
Replies are generated from your approved documents and live data, with source links where they help. When there is no reliable answer, the bot says so rather than guessing, which protects customer trust.
Clear limits on what it can do
Guardrails control topics, tone and the actions an agent may take. Personal data is masked in logs, and high-impact actions such as cancellations or refunds can be set to need approval from a person.
Freedom to switch models
We put a model-agnostic layer between your bot and the provider, so you can move between OpenAI, Claude, Llama or a self-hosted model as prices, quality or data rules change, without a rebuild.
Visible performance after launch
A dashboard shows resolved conversations, handoff reasons, unanswered questions and user ratings. We review it with you and use it to add missing content, refine prompts and fix weak flows.
Our process
Tools & tech we use
Use cases
- E-commerce order tracking, returns and product questions
- Appointment booking and reminders for clinics and salons
- Lead qualification and demo booking for B2B software companies
- Employee helpdesk for HR, IT and policy questions
- Admission, fee and course queries for schools and colleges
- Bill payment and account queries for utilities and fintech apps
Ready-made solutions to launch faster
Related services
AI Chatbot & Agent Development: FAQ
How much does AI chatbot development cost?
AI chatbot development typically costs between $3,000 and $25,000+, depending on scope. A bot that answers from one help center on one channel sits at the lower end. Agents that take actions in several systems, work in multiple languages, handle voice or need private hosting cost more. Model usage is a separate running cost that grows with conversation volume. A fixed quote follows a free consultation in which we review your channels, content and integrations.
How long does it take to build an AI chatbot?
A focused chatbot on one channel with a single knowledge source typically takes three to six weeks from kickoff to pilot. Agents with several integrations, voice support or approval workflows usually need eight to twelve weeks. The main factors are the state of your content, API access to your systems and how quickly your team reviews test conversations.
Will the chatbot make up answers?
Language models can sound confident while being wrong, so we build in several safeguards. The bot answers only from retrieved content, shows sources where useful and hands off politely when it is unsure. Before launch we test it on real questions from your logs, and after launch we review flagged and low-rated conversations to close the gaps we find.
How do you protect customer data in a chatbot?
Protection is designed in from the start. We pick model providers and settings that do not use your data for training, mask personal details in logs, encrypt data in transit and at rest, and restrict access by role. For stricter requirements, we can deploy an open model such as Llama inside your own cloud account so conversations stay in your infrastructure.
Which questions should we ask an AI chatbot development company?
Ask whether answers are tested on your real questions before launch, how wrong or unsafe replies are limited, and how the bot will integrate with the tools your team already uses. Also ask who owns the code, prompts and data pipelines, how human handoff works and what monitoring happens after go-live. With ZedBlu, you own everything we build and see working demos every week.
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