01Step 1 of 4
We gather what it should know
Docs, FAQs, past tickets, policies. The bot is only ever as good as its sources.
AI Chatbot DevelopmentAnswered
Answering the same twenty questions over and over?
Support volume is mostly repetition, and repetition is what a chatbot is genuinely good at. The trick is building one that answers from your actual documentation instead of improvising - and that passes people to a human the moment it's out of its depth.
We build chatbots that answer from your own documentation rather than improvising, and that hand a conversation to a person the moment they're out of their depth. Support volume is mostly the same questions repeated, which is the part worth automating - and the part customers are happiest to have answered instantly.
Fixed priceagreed before we start
- Sound familiar?
- Your team answers the same questions every single day.
- Customers ask at midnight and wait until morning.
- You tried a scripted bot and customers hated it.
Deliverables
What you get.
- 01Chatbot trained on your docs, site & FAQs
- 02Website widget, WhatsApp or in-app placement
- 03Human handover with full conversation context
- 04Lead capture & qualification flows
- 05Multilingual replies where you need them
- 06Transcript review & answer-quality tuning
Tools & approach
- Claude API
- OpenAI API
- pgvector
- Twilio
- WhatsApp Business API
- Next.js
The work
How this runs.
02Step 2 of 4
Grounded answers, with limits
It answers from your material and says so when it doesn't know - rather than inventing a policy.
03Step 3 of 4
Handover designed in
Escalation to a human with the whole conversation attached, so nobody repeats themselves.
04Step 4 of 4
Read the transcripts, fix the gaps
The first month of real conversations tells you what's missing. We tune from that.
Detail
Worth knowing first.
How is this different from the scripted bots people hate?
Older website bots followed decision trees: fixed buttons, fixed paths, and a dead end the moment your question wasn't one of the anticipated ones. That's what trained people to type 'agent' immediately.
A language-model chatbot reads the question as asked and answers from your material, so phrasing doesn't have to match a script. The difference customers notice most isn't fluency - it's that an unusual question gets a useful reply instead of a menu.
What does it need to know before it goes live?
Whatever your team already uses to answer customers: help documentation, FAQs, policy pages, product details, and - most valuable of all - resolved support tickets, because those contain the real questions in the customer's own words.
The bot is only ever as good as those sources. Where the documentation is thin, the honest fix is writing it rather than hoping the model fills the gap, and that gap is usually visible within the first week of transcripts.
What happens when it can't answer?
It says so and escalates to a human, carrying the full conversation with it so the customer never repeats themselves. Designing this path first is what separates a useful bot from an infuriating one.
A bot that guesses to avoid admitting ignorance does real damage - an invented refund policy or delivery promise becomes your problem the moment a customer acts on it. We'd rather it hand over slightly too often than confidently make something up.
Where can it run, and how do you tell if it's working?
The same knowledge base can serve a website widget, WhatsApp, and in-app chat, so a customer gets consistent answers wherever they ask instead of three subtly different ones.
Judge it on resolution rather than conversation count: what share of chats ended without a human, how often it escalated, and which questions it repeatedly failed. We review transcripts with you after launch, because the first month of real conversations is the most useful thing you'll get for tuning it.
Questions
Answered plainly.
It says so and hands over to your team with the full conversation attached. A bot that guesses is worse than no bot - that's the failure mode we design against first.
Start with ai chatbot development.
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Get in touch
Start with ai chatbot development.
Tell us what you're working on and what's slowing you down. A short call is usually enough to know whether we're the right fit - no proposal required first.
