AI chatbots that do more than answer — they qualify requests.

ATTAMATION develops controlled AI chatbots for B2B websites, support, internal knowledge workflows and lead qualification. The focus is not a friendly chat widget. The focus is usable output: qualified inquiries, structured handovers, better first responses and workflows that are triggered cleanly.

• They answer superficially but do not create a useful next step.
• They hallucinate because the knowledge base, boundaries and approvals are not defined.
• Leads still arrive incomplete in the inbox.
• Support requests are not triaged; they are only wrapped in another interface.
• Sales, support and back office teams do not receive structured data.
• Nobody can later understand why the bot recommended, answered or routed something.

1. We first define what the chatbot is allowed to do: inform, qualify, prepare, route or support internal teams.
2. The knowledge base is reviewed: website content, FAQs, documents, product data, policies, pricing logic or support articles.
3. The conversation flow is modeled as a controlled process: detect intent, ask follow-up questions, collect required fields, summarize and trigger handover.
4. The chatbot receives clear boundaries: allowed statements, no-go topics, escalation rules, human handoff and approvals.
5. Only after testing with real scenarios is the chatbot deployed to the website, customer portal, internal tool or support workflow.

• Website chatbot for B2B inquiries and lead qualification
• Support chatbot for frequent questions, status requests and pre-triage
• Internal knowledge assistant for documents, processes, product information or policies
• Quote preparation from chat conversations
• Handover to CRM, email, ticketing, calendar or workflow automation
• Chat transcript with summary, required fields and next step
• Multilingual chatbot for international inquiries when the knowledge base and processes are ready for it

• Companies with recurring website inquiries
• Manufacturers and distributors with complex products
• B2B teams that want better lead qualification before sales gets involved
• Support teams that want to reduce repetitive first-line questions
• Companies with many documents, FAQs, product details or internal knowledge sources
• Organizations that want chat to become an input to CRM, quote, ticket or workflow processes

Website, CMS, product data, PIM, FAQs, knowledge bases, PDFs, helpdesk, CRM, calendar, email, Microsoft 365, Google Workspace, Make, n8n, Zapier and custom APIs.

The goal is not for the chatbot to know everything. The goal is for it to use approved sources, recognize uncertainty and prepare the next step reliably.

The best starting point is a limited chatbot with measurable value, for example:

• qualify website inquiries and hand them over to sales
• answer product or service questions from approved sources
• pre-sort support cases and prepare tickets
• make internal documents searchable and provide answers with source context
• complete quote requests through targeted follow-up questions

AI chatbots need clear boundaries. ATTAMATION therefore plans with approved knowledge bases, source logic, human handoff, escalation rules, logging, privacy review, prompt/answer testing and defined no-go areas.

A chatbot should not make legally binding statements, promise final prices, finalize sensitive decisions or judge special cases conclusively unless that process has been explicitly reviewed and approved.

Is an AI chatbot just a ChatGPT window on a website?

No. A professional B2B chatbot needs roles, knowledge sources, boundaries, handovers, privacy logic, test cases and clear outputs. Otherwise it becomes just another uncontrolled interface.

Can the chatbot write leads directly into the CRM?

Yes, if required fields, duplicate logic, ownership and approvals are defined. For the first pilot, we often recommend: qualify and summarize first, then hand over to CRM or email in a controlled way.

Can the chatbot work with product data or documents?

Yes. Product data, PDFs, FAQs and support documents are strong foundations — if they are structured and reviewed.

What is the difference between a chatbot and a voice agent?

An AI chatbot works text-based on a website, portal or internal tool. A voice agent works in spoken conversation, for example by phone. Both become stronger when they use the same workflow and knowledge architecture.

Briefly describe which questions, website inquiries or support cases happen repeatedly today and what should happen after the conversation. We will assess whether they can become a controlled chatbot pilot.