AI chatbot for B2B that qualifies the lead, not just answers it
Your existing customer asks where an order is, what discount their contract gives them, whether an invoice has been settled. Your next customer asks for a quote on Friday at 9 pm and wants to know whether you do projects like theirs. An AI chatbot answers from your company data in seconds, asks about industry, scale and budget, and hands sales a qualified contact in the CRM.
- 7 timesroughly how much more often a company that replies inside the first hour qualifies a lead than one that waits an hour longer. Audit of 2,241 companies, Harvard Business Review
- 5 to 10 secondshow long an AI agent takes to answer from your company data: the price list, the contract, the order status, the contact history in your CRM
- 4 weeksfrom workshop to a chatbot in production, after a pilot on real conversations with your own customers
The problem
Why is a B2B chatbot a different job from a chat window on a shop?
A B2B purchase does not end with one click. The enquiry lands on Friday evening, on Wednesday the technical director comes back with follow-up questions, a week later finance asks about payment terms, and the signature comes from someone who has never seen your website. A chat that can only point at a contact form drops out of that conversation after the first question.
The other half of your traffic is customers you already have. They are not asking about the product, they are asking about their own order: where the pallet is, what discount the framework agreement gives them, whether the invoice has been settled. Those answers sit in the order system and in finance, not in a FAQ list. Your support team pulls them out by hand, hundreds of times a month.
The cost of a slow reply can be counted. In an audit of 2,241 companies published in Harvard Business Review the average response time to a web enquiry was 42 hours, and 23 percent of companies never replied at all. Companies that got back within an hour qualified a lead close to 7 times more often than those that waited an hour longer, and more than 60 times more often than those that waited a day.
- A Friday evening enquiry waits for an answer until Monday
- The salesperson calls cold, because the form gave them a first name and an email address
- Support answers the same order status question for the third time this week
- The chat conversation is stored nowhere, so the next person from that company starts from zero
Why now
What changed, so that a chatbot can answer from your own data?
Until recently a chatbot ran on a decision tree: someone wrote the questions and the answers in advance, and anything outside the script ended with "I do not understand". Today a language model receives the question together with fragments of your documents and answers from those, not from memory. The same technique sits under BOND, our own company AI agent, which answers from company data in 5 to 10 seconds.
The second change is on the integration side. Order status in your commerce system, the customer price list in your ERP, contact history in HubSpot and documents in Google Workspace are all reachable over an API. The chatbot does not need to hold a copy of that data, it asks the systems mid conversation, so it never answers from a snapshot taken last quarter.
The third thing is qualification. The criteria a salesperson uses to judge an enquiry (industry, headcount, volume, timing, budget) can be asked inside the conversation and written into the CRM as fields rather than as a note someone has to read. The salesperson gets a brief instead of a raw form, and knows whether to call today or next week.
What changes
What do sales and customer support actually get?
A Friday evening enquiry gets an answer on Friday evening
The chatbot answers questions about scope, terms of work and comparable projects from your own material, and asks for whatever is missing. On Monday the salesperson finds a conversation, not a note to call somebody back.
An answer in 5 to 10 seconds, at any hour, weekends included
A lead with industry, scale and budget, not just an email address
The conversation follows your qualification criteria and ends with a contact in the CRM carrying tags, a summary and a fit score. An enquiry below your threshold gets a polite answer and does not eat an hour of selling time.
Qualification fields written into HubSpot, Pipedrive, Salesforce or LiveSpace
The customer checks their own order
Once it is clear who is writing, the chatbot reaches into the order system and into finance and says which stage the job is at, which discount applies and whether the invoice has been settled. No phone call to the account manager and no waiting for them to come back from holiday.
Data pulled mid conversation from BaseLinker, an ERP or an SQL database
Conversation history stays with the company, not with a browser session
The thread is saved on the customer record in the CRM together with the transcript. When the technical director from the same company writes two weeks later, both the chatbot and the salesperson know what was said before.
Several people from one company in one thread, joined on the email domain
If it does not know, it says so
A question outside the knowledge base, or one that needs a human decision, is handed to the account manager together with the whole conversation. The chatbot does not guess a price, a date or a clause in a contract.
Escalation to Slack, Microsoft Teams or email, with the transcript attached
Questions about the pipeline and about support, in plain sentences
"How many manufacturing enquiries came in this week", "what do German customers ask about most", "which enquiries have been waiting longer than a day". The AI agent answers from the CRM and from the conversation log.
An answer in English or Polish, with no report and no CRM login
Proof
Do you handle your own enquiries this way?
Yes. Since March 2026 enquiries and data questions inside our founders' agency have been handled by an AI agent built by the same method, the same one behind the numbers on our AI for business page. Below is the scale of that work, together with what the number does NOT prove.
Built for ourselves. These are not client deployments but implementations inside the founders' own companies. We say so plainly, because proof you cannot verify is not proof.
JustBottheir agentJustIdea Agency, a performance marketing agency of around 30 people
- 23
- people from the company work with the agent
- 38
- systems wired in permanently
- 112
- processes run without a human
- 475
- thousand lines of the agent’s code
The agent is wired into HubSpot, the website forms, Slack and the data warehouse. It answers questions about clients and campaigns from company data, catches inbound enquiries and creates records in the CRM. First commit 1 March 2026, today 23 people use it.
8 of 38 systems it works in
Google Ads
Meta Ads- HubSpot
- ActiveCollab
- Ahrefs
GA4
Search Console
BigQuery
- Before
- A form submission was read by a person, who then created the contact and the company in the CRM by hand and decided who should pick it up. A question about client data meant logging into two systems and assembling a report.
- After
- The agent catches the submission, creates the contact and the company in HubSpot, checks for a duplicate on the email domain and notifies the team on Slack. Data questions get answered in a plain sentence, in seconds, with no report.
1,301 operations on form submissions and 3,432 on leads in 30 days, with no human involved
And in your company
What if our sales process is more complicated than that?
Four sentences we hear most often on the consultation call, and how we answer them.
Our product is too complex for a bot to describe.
The chatbot is not there to sell, it is there to qualify and hand over. Qualification rests on the same questions a salesperson asks on the first call, and the technical specification is discussed by a person anyway. If the workshop shows that every enquiry really is different, we say so and the scope shrinks to routing enquiries.
We are afraid the bot will quote a customer the wrong price.
Prices and contract terms are either pulled from the system during the conversation, or the chatbot is not allowed to state them at all. That is a decision taken in the workshop, not a guess made during the rollout. We write the scope down before the pilot: what the chatbot states outright, what it confirms from a system, and what it hands to the account manager.
We have 40 PDF files of documentation and nobody keeps them tidy.
That is a normal starting point and you do not have to sort out the whole drive first. In the workshop we pick 20 to 50 documents that answer most of the questions, and the first knowledge base is built from those. The rest arrives later, and your own person adds updates in a panel, without a developer.
We have two salespeople and a dozen or so enquiries a month.
Then the chatbot solves a problem you do not have yet, and we will tell you that on the call. At that volume a decent form and a fast follow-up are cheaper. We come back to it when enquiries start waiting, or when serving existing customers takes a day a week out of your sales team.
- CRM
- HubSpot, Pipedrive, Salesforce, LiveSpace
- Conversation channels
- Website widget, WhatsApp Business, Slack, Microsoft Teams, forms, email
- Orders and sales
- BaseLinker, Shopify, WooCommerce, PrestaShop, Xentral
- Finance and invoicing
- Fakturownia, sevDesk, Xero, Comarch Optima
- Documents and knowledge base
- Google Workspace, Microsoft 365, Notion
- Data and automation layer
- BigQuery, Google Sheets, SQL databases, n8n, Make, Zapier, custom API integrations
What B2B buyers ask, where the answer comes from, what stays in the CRM
Seven questions that keep coming back in B2B companies. The middle column matters most, because it decides whether an answer can be checked or only sounds right.
| Question from a lead or a customer | Where the answer comes from | What stays in the CRM |
|---|---|---|
| Do you run projects in our industry | case descriptions and sales material in the knowledge base | industry and a fit score against your profile |
| How much does it cost | ranges and terms from the price list, an exact quote from a person only | budget and timing as given by the lead |
| What discount does our framework agreement give us | the customer price list in the ERP or in the CRM | the topic of the conversation on the company record |
| Where is my order | the order system, BaseLinker, an SQL database | a task for the account manager when there is a delay |
| Has our invoice been settled | Fakturownia, sevDesk, Xero, Comarch Optima | nothing, until the company has been verified |
| When can you start | the rollout calendar and the limit of two projects at a time | the start date the lead expects |
| I need to talk to someone technical | outside the chatbot scope, handed to a person | a task for the account manager with the transcript |
The scope of every row is set in the workshop. A first rollout usually covers three or four rows, and the rest is added once the transcripts show what people actually ask about.
How we do it
Five steps from a support inbox to a chatbot in production
Every step ends with something you can judge before you decide on the next one.
Workshop
We collect the questions of the last few months: from the support inbox, from chat, from phone calls, from forms. We group them and check which ones are answered by a document, which by a system, and which have to stay with a person. We also write down the qualification criteria your salespeople currently keep in their heads.
Knowledge base and boundaries
We pick 20 to 50 documents to start with and organise them around questions rather than around your folder structure. We agree what the chatbot states outright, what it confirms from a system and what it does not touch. We choose the channels and the integration layer: n8n, Make or a direct API connection.
Pilot on real conversations
The chatbot runs on part of your traffic, alongside your current support, without switching anything off. We read the transcripts, correct answers and close gaps in the knowledge base. We test separately what it does with a question it cannot answer, because that is the case that destroys trust fastest.
Production and training
We switch it on for all traffic, with monitoring of errors and escalations. Your own person learns to add documents and change qualification criteria without us: a new service, a different budget threshold, a new market.
Measurement after 30 and 90 days
We look at the share of conversations closed without a human, the number of qualified enquiries and the topics that most often ended in a handover. We tune the knowledge base to what people ask about, not to what we predicted in the workshop.
Before and after
How the week changes for sales and support
Before
- A weekend enquiry sits in the inbox until Monday morning
- The salesperson calls cold, because the form gave them a first name and an email address
- Support answers the same order status question for the third time this week
- The chat conversation disappears with the closed browser tab
After
- A weekend enquiry gets an answer straight away and a brief waiting for Monday
- The salesperson starts from the industry, scale and timing the lead already gave the chatbot
- The customer checks the status themselves, and the account manager hears only about delays
- The whole conversation history sits on the company record in the CRM, visible to everyone who serves that account
You judge the first conversations during the pilot week, on transcripts. The share of conversations closed without a human is measured after 30 days, the effect on sales after 90.
For the person signing this off
What a B2B sales director gets out of it
You stop explaining to the board why a Thursday enquiry was answered on Monday. First response time and the number of qualified enquiries become figures in the CRM rather than an impression from the weekly review. Qualification criteria stop being one person's knowledge and stop leaving with them.
The second point weighs more in a budget conversation. From the transcripts you know what people ask before they buy, and in what order. That is material for the website, for the offer, and for the argument that paid traffic does not vanish outside office hours.
- First response time counted in seconds, not in working days
- Qualification criteria written down and versioned, independent of any single person
- A pilot on part of the traffic, so current support keeps running until testing ends
- You approve the answer scope and the escalation list before the pilot starts
What it costs
The workshop has a published price, the rollout is quoted after it
The process workshop costs EUR 3,500 net per week and runs from one to seven weeks depending on scope. For a B2B chatbot one week is usually enough, and a second is added when product documentation has to be sorted out before anything can answer from it. We quote the rollout after the workshop, because only then is it clear how many channels, how many systems and how deep an integration are in play.
You pay for language model and tool usage directly to the provider, according to your own traffic, with no margin from us. Typical cost sits between EUR 0.005 and 0.07 per query depending on the model and the length of the answer, and we show an estimate of that bill before launch so the first invoice is not a surprise.
Process workshop
EUR 3,500 net
per week, usually one to two weeks for a B2B chatbot
A self-contained piece of work. It ends with a question list, qualification criteria and a system map, a plan you can execute without us.
- Questions from recent months collected and grouped by where the answer lives
- Lead qualification criteria written down with thresholds and exceptions
- System map: what can be read over an API and what needs work on your side
- Channel recommendation and the metrics to measure after 30 days
- Most popular
Workshop and chatbot rollout
Quoted after the workshop
scope follows from the number of channels and the depth of integration
The option most companies choose. A chatbot in production 4 weeks after the workshop, following a pilot on real conversations.
- Everything in the workshop
- Knowledge base built from your documents plus a panel for updating it yourselves
- Lead qualification with fields and a summary written into the CRM
- Integration with orders and finance, data pulled during the conversation
- Pilot on part of the traffic, escalation to Slack or Microsoft Teams, monitoring
Chatbot and an AI agent for the team
Quoted after the workshop
for companies where someone asks for data a dozen times a day
Everything in the rollout plus BOND, the company AI agent that answers questions about customers and the pipeline in plain sentences.
- Everything in the chatbot rollout
- BOND wired into the CRM, orders and the data warehouse, roughly 2 months to deploy
- Alerts on enquiries waiting longer than an agreed threshold
- Access for sales and support, in English and Polish
The workshop ends with a document that stands on its own without us: the question list, the qualification criteria and the system map. If it turns out your questions do not repeat often enough to give a chatbot anything to do, we say so and we do not sell you a rollout.
Frequently asked questions
How is an AI chatbot for B2B different from a chat window on an online shop?
A B2B conversation runs longer than one session and involves several people from the same company, each with a different question. So a B2B chatbot has to recognise the customer, join threads on the email domain and save everything on the company record in the CRM rather than in an anonymous session. On top of that it answers from company data: the price list, the contract, the order status, instead of a list of prepared replies.
How long does it take to roll out an AI chatbot for B2B?
One week of workshop and 4 weeks to production: knowledge base and boundaries in week two, a pilot on part of the traffic in week three, full launch in week four. Integrating an ERP, a single company sign-on or several channels at once adds a few weeks. You read the first conversations during the pilot week, so you do not wait until the end to see the quality of the answers.
How much does an AI chatbot for B2B cost?
The process workshop costs EUR 3,500 net per week and for a chatbot one week is usually enough. The rollout is quoted after the workshop, once we know the number of channels, the list of systems to integrate and the state of the documentation. Language model and tool usage is paid directly to the provider according to your traffic, with no margin from us, typically EUR 0.005 to 0.07 per query.
How does the chatbot qualify a lead when the decision takes several weeks?
It collects what can be established in the first conversation: industry, scale, volume, expected timing and a budget range. That is written into the CRM as fields, so later contacts from the same company attach to the same opportunity instead of creating a new one. The salesperson sees which stage the conversation is at and what is still missing before a decision.
What happens when the chatbot does not know the answer?
It says so plainly and hands the conversation to the account manager, through Slack, Microsoft Teams or email, together with the full transcript. The person picking it up sees the context from the first second and does not have to ask the same thing twice. We configure the chatbot so that it never guesses a price, a date or a contract clause, because one invented answer costs more than ten missing ones.
Will the chatbot recognise that several people from one company are writing to us?
Yes, on the email domain, and for existing customers additionally on the customer number or the VAT number. Threads are joined on the company record in the CRM, so a technical director writing two weeks after the purchasing enquiry does not start from zero. The joining rules are agreed in the workshop, because inside a group of companies one domain is often shared by several legal entities.
Is our customer data safe?
Only the fields needed to answer are passed into the conversation, and that list is agreed in the workshop. Customer data stays in your systems, the chatbot queries them over an API during the conversation instead of holding a copy. We sign a data processing agreement before the pilot under <a href="https://eur-lex.europa.eu/eli/reg/2016/679/oj" rel="noopener">GDPR 2016/679</a>, and for companies with stricter requirements we set up processing inside the European Union.
How do you measure whether the chatbot actually works?
With four numbers: the share of conversations closed without a human, first response time, the number of qualified enquiries in the CRM, and the number of conversations where the chatbot refused to answer. The last one is there on purpose, because a high closing rate on bad answers is a worse result than an honest escalation. The first review is after 30 days, the second after 90.
Let us look at what your customers actually ask
The free consultation takes 30 minutes. You describe where enquiries come from and what your support team answers most often, even from memory. We show which of those questions a chatbot closes from day one, and which need the documentation sorted out first or access to the order system. At this stage you do not have to give us access to anything.
We run at most two rollouts at a time, so that someone is genuinely sitting on each of them.
Book a free consultationPS. If someone at your company answered the same order status question for the third time this week, and the weekend enquiry waited until Monday, that is exactly this problem. An AI chatbot for B2B answers from your data in 5 to 10 seconds and hands sales a qualified enquiry instead of a raw form.