Digital AI Transformation
Put the first two weeks in a chatbot. Every new hire arrives with roughly the same fifteen questions — where the handbook lives, when the badge and laptop arrive, when the first payday lands — and today someone stops working to answer each one. The bot answers them instead, identically every time, in your employee portal or in Telegram, and searches your handbook for whatever the checklist never anticipated.
15 years. 100+ projects. The same chatbot engine, pointed at your own handbook.
The Fifteen Questions Everyone Asks
Nothing about a new hire's first two weeks is unpredictable. They ask where the handbook is, who their buddy is, how to get a laptop, when health insurance begins, whether they can work from home. The list barely changes between hires, or between companies.
What changes is who answers. The buddy answers some, the manager others, and policy questions go to whoever picks up first. Three people give three slightly different versions of the same rule, because each is recalling it rather than reading it. Some rules aren't written down at all — they live in one person's memory, and when that person is away the new hire waits.
That is the cost worth naming: not the minutes, but the drift. An answer that varies by who you asked isn't a policy at all.
A Tree, Plus the Handbook
AI onboarding splits into two halves that are different shapes, so the build is deliberately hybrid.
The predictable fifteen get a dialogue tree. Each topic is written once, reviewed once, and returned identically from then on: first day, equipment and accounts, payday, time off, remote work. A tree is right here precisely because these questions don't need intelligence; they need consistency.
Everything else gets free-text search over your handbook. A new hire types "what if I'm sick during PTO" and the assistant answers from the handbook article that covers it, with a link back to the source so they can read the whole rule. This is the half a tree can never cover, because the tail of what people ask is genuinely infinite.
And the honest boundary: if a rule was never written down anywhere, the bot cannot invent it. Questions it can't source still go to a person. That is not a workaround — it is how the handbook grows, because the questions the bot couldn't answer are exactly the list of gaps you didn't know you had.
Try It Live
Employee portal — the Daisy LLC demo. A fictional company; every policy in it was written for the demo. It has a first-week checklist, the handbook, company news — and an assistant you can ask in your own words.
The demo prints its own example questions: when does health insurance kick in? · what if I'm sick during PTO · when's the first payday · can I work from home. Ask one of those, then ask something it doesn't list, and watch which path answers you.
The same bot on Telegram — @daisy_hr_bot. Same topics, same handbook, different door.
Demo assistant. No personal data is collected.
Pricing
An AI onboarding build is priced like any other chatbot build we do. The tiers, what separates them, and the maintenance model live on the AI chatbot development page — one price list, so there is no second one to drift out of sync.
Frequently Asked Questions
Who is this for, and who is it not for?
Automated employee onboarding fits companies hiring often enough that the same questions recur — typically 20 to 150 employees with steady intake. If you hire twice a year, a written handbook alone will serve you better than a bot on top of one.
What do you need from us before we start?
Your rules have to exist in writing somewhere — a handbook, a wiki, a folder of documents. The bot answers from your material; it doesn't author policy, and it will repeat a stale article as faithfully as a current one. If most of your rules are undocumented, the first useful step is writing them down, and we'll say so rather than sell around it.
How long does it take to launch?
Automated onboarding usually reaches a working build in a few weeks: scripted topics, handbook search, and the portal and Telegram channels, depending on how much of your material needs restructuring before it can be searched.
Where does the bot live, and what does it read?
A widget in your employee portal or intranet, a Telegram bot, or both — one assistant behind them. It reads the handbook or knowledge base you point it at, and it answers with links back to those articles.
What does the bot remember, and what does it store?
That is a decision you make at build time, not a default you inherit. Our public demo deliberately has no contact-collection step at all, so there is no personal data to hold. For your own deployment we scope storage to what you need and document it.
What does it cost?
An onboarding build follows our standard chatbot tiers — see the full pricing table on the chatbot service page. Every project starts with a free consultation and a fixed estimate.