Conversational AI for customers and employees: answers grounded in your policies and records, with the sources shown; actions only where you allow them; and a clean handoff to your team when a question goes beyond what the assistant should decide.
behind the answer · retrieved
01 / What we mean by an AI chatbot
Definition · AI chatbot
A conversational application that understands questions in natural language and answers them - using a language model grounded in your approved content and data - and that can hand the conversation to a person or, where permitted, carry out a requested action.
Policies, help articles, product data and the user's own records - retrieved at the moment of the question, and cited so the reader can check.
When the sources don't cover a question, the assistant says so instead of improvising. A shorter, honest answer is worth more than a fluent guess.
Complaints, exceptions and decisions go to a person, with the full conversation and the relevant records attached, so nobody has to repeat themselves.
02 / What we build
The audience decides most of the design: what the assistant may see, what it may do, and who takes over when it stops.
For customers
Answers about orders, accounts, policies and products on your website or in your app, connected to your order and helpdesk systems.
Good fit when a large share of contacts are questions your content already answers.
For employees
One place to ask about policies, procedures, product details and past cases - in Microsoft Teams, Slack or your intranet - limited to what each employee may read.
Good fit when the same questions reach HR, IT or senior staff every week.
Inside your software
An AI copilot that helps users with the task on screen - summarising a record, drafting a response, finding a setting - built into your product or internal tool.
Good fit when users switch between screens to assemble information.
Answers plus actions
Assistants that also perform requested actions - rescheduling, updating details, creating cases - with confirmation and permissions. For agents without a conversation, see AI agent development.
Replacing an existing bot
If you run a scripted or intent-based chatbot that customers avoid, we can rebuild it on a language model while keeping the integrations and analytics that work - moving one topic at a time rather than switching everything at once.
Conversation examples are illustrative with fictional data.
03 / Conversation design
Every assistant we build is designed for all three situations - not only the happy path in the demo.
The assistant retrieves the relevant passages and answers only from them. If sources conflict, it uses the newest approved version.
Actions use your systems' APIs with the user's own permissions, and nothing changes until the user confirms.
Out-of-scope questions, complaints and low-confidence cases go to your team in the helpdesk they already use.
04 / Knowledge and grounding
Most chatbot problems are content problems: outdated articles, contradictory policies, answers that live in one person's head. We treat the knowledge base as part of the project.
The retrieval architecture behind this is described under AI and LLM integration.
05 / Channels and integrations
A chat widget on your site or a native panel in your web or mobile app, signed in where account data is involved.
Microsoft Teams, Slack or your intranet for internal assistants, with single sign-on.
Drafted replies for your team to send, using the same knowledge and rules as the chat.
Tickets created and updated on handoff, with the transcript attached.
Account, order and contract data read with the user's permissions. For wider CRM connectivity, see CRM integration services.
Topics, resolution, handoff reasons and unanswered questions, reviewed regularly.
06 / Quality
Conversational AI services are judged by their worst answers. These checks are how we find them before your customers do.
07 / How a project runs
Our AI chatbot development services start from your real conversations, not from a template - the questions people actually ask decide what the assistant must handle.
Samples of typical questions, current answers and where the content lives.
out →Topic list, content gaps
Sources connected and cleaned, owners agreed, access rules defined.
out →Grounding sources
The assistant answers your test questions; quality and cost are measured.
out →Scores, handoff rules
Widget, app or workplace tool; helpdesk and CRM connections; sign-in.
out →Tested release
Staged rollout, weekly transcript review, content fixes and tuning.
out →Live assistant, reports
08 / Budget
We estimate build and running cost separately, after looking at your content and a sample of conversations.
Well-maintained help content is quick to connect; scattered or outdated material needs work.
Each action in another system adds integration, confirmation flows and tests.
Every channel and language adds interface work and quality checks.
Running cost follows the number and length of conversations.
09 / Choosing a partner
Most AI chatbot solutions look similar in a demo. The differences appear on the questions your content doesn't cover - so these are the questions to put to any AI chatbot development company.
10 / Questions
The build cost depends on how much content has to be prepared, whether the assistant only answers or also performs actions in your systems, the number of channels and languages, and the integrations with your helpdesk or CRM. Running cost depends on conversation volume and length. We estimate both after reviewing your content and a sample of past conversations.
A chatbot answers people in a conversation, typically customers or employees asking questions. A copilot sits inside an application and helps a user with the task on their screen - drafting, summarising, finding. An agent works towards a goal by taking actions through tools, often without a conversation at all. One product can combine them; the AI agent development page covers agents in depth.
Yes. Typical integrations read order or account status, create or update tickets, and pass the full conversation to a person on handoff, so customers do not repeat themselves. Access uses the narrowest permissions required, and actions that change data ask the user to confirm.
It answers only from approved sources and shows which ones it used. When the sources do not cover a question, it says so and offers a person instead of guessing. Before launch it is tested on a set of real questions, including ones it should refuse; after launch, conversations are reviewed and gaps are fixed in the content or the instructions.
Current language models handle many languages well, and an assistant can answer in the user's language from sources written in another. Quality still has to be checked per language on actual questions, and legal or policy wording may need approved translations.
On your website as a chat widget, inside your web or mobile application, in workplace tools such as Microsoft Teams or Slack for internal assistants, or behind email for drafted replies. The same assistant logic can serve several channels with different permissions.
Your team, with tools that make it practical. We connect the assistant to the systems where content already lives - help centre, wiki, policy documents - so updates there reach the assistant automatically, and we flag questions it could not answer so content owners know what is missing.
Start node · your brief
Tell us who it is for, what they ask most often, and where the answers live today. We'll suggest what a first version should cover - and what should stay with your team.
