AI chatbot development
Easital Technologies Ltd. is an AI chatbot development company. We build custom chatbots that answer from your own content, complete tasks in your systems and pass a conversation to a person when they reach their limit. Each chatbot is tested on real conversations before release and measured after it.
An AI chatbot is a conversational interface driven by a language model: it reads what a person writes or says, works out what they want and replies in natural language, drawing on the content and systems it has been connected to.
The earlier generation of chatbots followed decision trees. A person picked from buttons or typed a phrase the bot recognized, and anything else led to a dead end. An AI chatbot accepts a question in the person’s own words and composes an answer from your help content, policies and account data.
That flexibility is the reason to build one and also the main risk. A scripted bot can only say what was written for it. An AI chatbot can say something fluent and wrong. Custom chatbot development services are mostly the work of narrowing that risk: grounding answers in your content, limiting what the bot may do and deciding when it hands over to a person.
| Rule-based chatbot | AI chatbot | |
|---|---|---|
| How it understands | Buttons and keyword matches | Free text in the person’s own words |
| Where answers come from | Replies written in advance | Composed from your content when the question is asked |
| Unexpected questions | Falls back to a menu | Attempts an answer, or says it does not know and hands over |
| Upkeep | Every new path is scripted by hand | Update the content, then run the conversation tests again |
Six kinds of chatbot, which share one engine: retrieval over your content, a language model, tools for actions and a rule for handing over.
Answers from your help center, policies and order data. When the bot cannot resolve an issue, it passes the conversation to your team with the transcript and what it has already checked.
A bot that answers product questions, asks the qualifying questions your sales team would ask, books a meeting and writes the lead to your CRM.
A chatbot for staff over policies, runbooks and product documentation, with every answer linked to its source and limited to what that employee may read. Built on RAG.
A chat panel inside your SaaS product that knows the user’s account and current screen, explains features and carries out the steps the user asks for.
Chatbots with single sign-on, role-based access to content, a log of every conversation and, where data rules require it, a private model on your own servers.
The same conversation logic behind a website widget, an in-app panel, messaging apps and the telephone. For phone calls, see voice AI agent development.
A chatbot build runs in eight steps and begins with your existing conversations, not with a model. The questions people already ask decide what the bot must know and do.
We group past tickets and chats by question type and choose the types the bot will handle. We also list what it must stay out of, and agree what counts as a resolved conversation.
Real conversations with the correct outcome for each, including the ones where the right outcome is a refusal or a handover. Every release is tested against this set.
Help articles, policies and product data are cleaned, de-duplicated and indexed for retrieval. Gaps and contradictions in the content surface here and go back to their owners.
Tone, the clarifying questions the bot asks, what it remembers within a conversation, the languages it speaks and the exact rule for handing over to a person.
Order lookups, bookings and account changes through tools with their own permissions. Changes that matter are confirmed with the user before they are made.
The bot stays on its topics, treats user messages and documents as data and not as instructions, makes no commitment it is not authorized to make, and masks personal data in logs.
History is trimmed or summarized as a conversation grows, easy turns go to a smaller model and common answers are cached. We report cost per conversation from the prototype onward.
A staged rollout, then four numbers reviewed regularly: resolution rate, handover rate, wrong answers found in transcript review and cost per conversation. Questions the bot could not answer become a list of content to write.
Four live systems hold conversations in text or by voice. Three are Easital products, and the fourth was built by Easital for a client.
Easital product
Built and run by Easital
Customer conversations for online stores. The mAutomate site describes supporting customers from one dashboard and publishing and replying across Facebook, Instagram, WhatsApp, Telegram, X and LinkedIn.
Easital product
Built and run by Easital
A chat over the user’s own content. The StepVideo site describes “Ask your library”: ask a question in chat and get the answer from your own guides, with every source linked.
Easital product
Built and run by Easital
AI chat inside a paid product. In the Manob.ai workspace a user describes a change in plain language, and the chat works with an agentic code editor that edits the project.
Client project
Built by Easital for a client in the United States
The same conversation design on the phone. The Calldone site describes voice agents that answer questions, book appointments and qualify leads, with a transcript and a summary of every call.
Chatbot development cost depends on what the bot has to do behind the chat window: the systems it connects to, the state of your content, the channels and languages, and how much assurance a wrong answer calls for. We do not publish a price list, because two chatbots with the same interface can differ widely in the work behind them.
A bot that sits in the left column on every row is a small project. Each move to the right adds engineering and testing.
| Cost driver | Lower cost | Higher cost |
|---|---|---|
| Knowledge | One clean help center | Many sources, scans, tables and per-user permissions |
| Actions | Answers only | Reads and changes records in your systems |
| Channels | One website widget | Web, in-app, messaging apps and phone |
| Languages | One | Several, each with its own test conversations |
| Handover | A transcript sent by email | Live transfer into your help desk with context |
| Assurance | Internal use, low stakes | Public use, sensitive topics, full audit trail |
Running cost is a separate line: model usage per conversation, hosting, any messaging or telephony fees, and upkeep of the content and the tests. We estimate cost per conversation from measured token counts during the prototype, before you commit to the full build. Reducing it later is covered under LLM cost optimization.
A subscription chatbot platform is the faster choice when your needs match what it offers. A custom build is worth it when the bot has to act inside your own systems, live inside your own product or follow rules a platform cannot.
A custom chatbot makes sense when at least one of these is true:
A chatbot is assembled from a model, a knowledge layer, channels and integrations. Each part is chosen for the project, so the list is by function.
We build on all major commercial model providers and on open-weight models. Easy turns and hard turns can go to different models, and the bot is not locked to any one of them.
Commercial and open speech engines, picked for the languages your customers speak, response time and price.
Three ways to engage Easital for chatbot work: a full build, people for your team, or a review of a bot that is already live.
One chatbot for a defined set of question types and channels, taken through the eight steps above and handed over with its conversation set and runbook, or operated by us.
Best for: a support, sales or internal use case with existing conversations to learn from.
You can hire chatbot developers from Easital who work in your repository, backlog and review process. See hire AI developers.
Best for: product teams building a chatbot into their own software.
We read a sample of real transcripts, test the bot against a conversation set and deliver a written list of fixes covering wrong answers, missed handovers and cost.
Best for: chatbots that users have started to avoid.
An AI chatbot development company designs, builds and maintains chatbots driven by language models. The work covers choosing which conversations the bot should handle, connecting it to your content and systems, designing the conversation and the handover to people, testing it on real conversations and measuring it after launch.
It depends on six things: how many sources the bot answers from and how clean they are, whether it only answers or also acts in your systems, the number of channels, the number of languages, how handover works and how much assurance the use case needs. Running cost is separate and is driven by model usage per conversation. We give an estimate after scoping and do not publish fixed prices.
Use a platform when its connectors, channels and controls already cover your case. Choose custom chatbot development when the bot must act in systems the platform cannot reach, when it is part of your own product, when conversations may not go to a third party, or when platform fees at your volume exceed the cost of running your own.
Answers are grounded in your content through retrieval, the bot is instructed to say when it does not know, and it hands over on the topics and signals you define. Every release is tested against a set of real conversations, and transcripts are reviewed after launch. Wrong answers cannot be ruled out, so the design aims to make them rare, visible and quick to correct.
The model work is the same. The difference is in access and accountability: single sign-on, answers limited to what each user may read, a log of every conversation, integration with internal systems, and often a private model so that conversations stay inside the company network. Easital has set up local and on-premise models.
Yes. Engineers from Easital can join your team and work in your codebase on the chatbot you are building. See hire AI developers.
Describe who asks, what they ask and which systems hold the answers. We reply by email with questions and a proposed first step.
Easital is an AI and SaaS engineering company that takes AI software to production, and runs AI products of its own. Founded in 2019.