Engage — Middle of Funnel
Here's the brutal math on B2B chatbots: 74% of website visitors prefer using them. But 87% abandon conversations with bots that can't answer their actual questions. Your prospects want to engage. They're trying to buy. And your generic chatbot—with its decision trees, scripted responses, and "let me connect you with a human"—is actively pushing them toward competitors who can answer their questions right now.
We build lead generation chatbots that actually know your products. Trained on your specifications, your use cases, your differentiators. When a prospect asks a technical question at 11pm, they get a real answer—not a form to fill out and wait.
74%
of B2B buyers prefer chatbots for initial research
87%
abandon when bots can't answer real questions
23x
more likely to convert when questions answered instantly
You added a chatbot to your website because everyone said you should. It's not working. Here's why.
Traditional chatbots are decision trees dressed up as conversations. They follow branching logic: if visitor says X, respond with Y. The problem? B2B buyers don't follow scripts. They have specific, technical questions about your products—compatibility requirements, integration capabilities, performance specifications, compliance certifications.
When your chatbot hits a question outside its script, it does one of two things: loops back to the menu ("I'm sorry, I didn't understand that. Would you like to...") or surrenders immediately ("Let me connect you with a human representative"). Either way, the prospect learns your chatbot can't help them. They leave. They don't come back.
The script trap is particularly devastating for B2B companies with complex products. Your buyers aren't asking "what are your business hours?" They're asking "does your solution support multi-tenant deployments with SOC 2 compliance?" Scripts can't handle that.
Problem #1
Most chatbot platforms give you a generic AI and tell you to "train it yourself." Upload some FAQs. Add some product descriptions. Connect your knowledge base. What they don't tell you: this is a full-time job that nobody on your team has time for.
So your chatbot launches half-trained. It knows your company name and maybe your top three products. It doesn't know the technical specifications that matter to engineers. It doesn't understand the compliance requirements that matter to procurement. It can't explain why your solution beats the competitor your prospect is also evaluating.
The knowledge gap means your chatbot confidently gives wrong answers, vague non-answers, or—worst of all—admits it doesn't know when your website clearly contains the information. The prospect wonders: if your chatbot doesn't know your products, does anyone at your company?
Problem #2
Here's what happens when a generic chatbot "captures a lead": it collects a name and email address, maybe a company name, and passes it to your sales team with zero context. Your rep has no idea what the prospect actually wanted, what questions they asked, what products they're interested in, or how qualified they are.
So your sales team treats every chatbot lead the same—with a generic follow-up email that probably lands in spam (see: Salesperson CONVERT). Hot prospects who were ready to buy get the same treatment as tire-kickers who were just browsing. The hot prospects, insulted by the generic response, go silent. Your team marks them as "unresponsive" and moves on.
Meanwhile, your reps waste hours chasing unqualified leads because the chatbot couldn't tell the difference between an enterprise buyer with budget and a student doing research for a class project.
Problem #3
Your prospects aren't just talking to you. They're evaluating alternatives. They're comparing your solution to competitors. And when they engage with your chatbot, they often reveal exactly who else they're considering—if anyone is paying attention.
Generic chatbots don't capture competitive intelligence. They don't flag when a prospect mentions a competitor by name. They don't trigger displacement-specific content. They don't alert your sales team that this prospect is actively comparing options and needs a fast, targeted response.
Every competitive mention that goes unnoticed is a deal you're fighting blind.
Problem #4
We don't give you chatbot software and wish you luck. We build, train, and continuously optimize a lead generation chatbot that represents your company as well as your best sales rep—available 24/7, in every language, on every page of your site.
What Makes This Different:
We manually build your AI's knowledge base from your approved content—product specifications, technical documentation, case studies, whitepapers, compliance certifications, competitive differentiators. Not a quick FAQ upload. A comprehensive training process that typically covers 200+ pages of source material.
The result: your chatbot answers the same technical questions your sales engineers answer. Specification-level detail. Integration requirements. Compliance standards. Use case-specific recommendations. When a prospect asks "does this support Modbus TCP and can it operate in -40°C environments?"—they get an accurate answer, not a redirect.
Our chatbots don't follow decision trees. They have actual conversations. A prospect can ask follow-up questions, change topics, go deep on technical details, then circle back to pricing—just like they would with a human rep.
More importantly, the AI guides these conversations toward qualification. It asks the questions your sales team would ask: What's your use case? What's your timeline? Who else is involved in the decision? What are you using today? The conversation feels helpful to the prospect while gathering the intelligence your team needs to close.
Every conversation generates a qualified lead profile—not just contact information. Your sales team receives:
• Complete conversation transcript
• AI-generated summary of the prospect's needs
• Specific products discussed and questions asked
• Qualification signals (budget indicators, timeline, authority, pain points)
• Competitive mentions and displacement opportunities
• Recommended next steps and talking points
Your rep walks into the first call already knowing what the prospect needs, what objections they'll likely raise, and why they're better than the competitor they're also evaluating.
When a prospect mentions a competitor—or when we detect competitive signals from their questions—the system responds intelligently:
• Serves targeted content addressing known competitive weaknesses
• Flags the lead for priority follow-up
• Provides your sales rep with relevant battle cards
• Triggers displacement-specific nurture sequences
You stop fighting blind against competitors you didn't know were in the deal.
AI can't be accountable. Humans can. Our team continuously monitors your chatbot's conversations, catches errors before they become patterns, and refines the AI's responses based on real performance data.
Every month, we analyze your chat transcripts, identify optimization opportunities, and update your chatbot's training. It gets smarter over time—not because we set it and forget it, but because professionals are actively improving it.
A detailed look at what happens when a prospect engages with your lead generation chatbot.
Step 1
A visitor lands on your product page. Instead of a generic "How can I help you?" popup, your chatbot opens with context-aware messaging based on the page they're viewing: "I see you're looking at our industrial sensors. Are you evaluating these for a specific application?"
The conversation starts relevant. The prospect feels understood, not interrupted.
Step 2
As the conversation unfolds, your chatbot gathers qualification data naturally. It doesn't feel like a form—it feels like a helpful discussion with someone who knows the product line.
The AI adapts its questions based on the prospect's role and responses:
Complete conversation transcript
Procurement gets pricing and compliance information
Executives get business outcomes and ROI data
Throughout, the chatbot answers questions with specification-level accuracy, recommends relevant products, and surfaces case studies from similar applications.
Step 3
Behind the conversation, the system is building a complete prospect profile:
Firmographic data enriched from our 200M+ company database
Industry vertical and company size
Specific use case and application requirements
Current solution (if any) and pain points
Timeline and urgency indicators
Budget signals and decision-making authority
Competitive mentions and evaluation status
This isn't data entry. It's intelligence gathering through natural conversation.
Step 4
When the prospect is ready to talk to a human—or when the AI determines they're sales-ready—the handoff happens seamlessly:
Your sales rep receives an instant notification
Complete conversation transcript attached
AI-generated summary highlighting key qualification data
Recommended approach based on prospect's stated needs
Direct contact information with permission to follow up
Step 5
After launch, we don't disappear. Our team:
Reviews conversation transcripts weekly
Identifies questions the AI struggled with
Expands training content to fill knowledge gaps
Refines qualification logic based on sales feedback
Provides monthly performance reports and optimization recommendations
Your chatbot improves continuously because humans are actively making it better.
There's no shortage of chatbot software. The problem isn't access to AI—it's everything that comes after.
You sign up for an enterprise chatbot platform. They give you access to a dashboard, some documentation, and maybe a few hours of onboarding. Then you're on your own.
Now someone on your team—who already has a full-time job—needs to:
• Upload and organize all your product content
• Write conversation flows for every scenario
• Test the AI's responses across hundreds of questions
• Fix the wrong answers (there will be many)
• Monitor ongoing conversations for quality
• Update the knowledge base as products change
• Analyze performance data and optimize
• Handle escalations when the AI fails
This is a full-time job. Multiple full-time jobs, actually. And it requires expertise in AI training, conversation design, and your own products that rarely exists in a single person.
So the chatbot launches half-finished. Nobody has time to improve it. It generates complaints instead of leads. Eventually, someone turns it off.
We do the work. All of it.
DIY Chatbot Platforms
Automation doesn't mean abdication. We build in the oversight that keeps AI helpful, not harmful.
End-to-end encryption: All data in transit and at rest encrypted (TLS 1.2+, AES-256)
Data ownership: The AI only answers from approved source content—no hallucinated specifications or made-up claims
Regional compliance: Your conversation data is yours. Never shared with third parties. Never used to train other clients' chatbots.
Audit trails: Complete conversation logs with timestamps for compliance verification
Role-based access: Control who on your team can view transcripts, modify training, or access analytics
SSO integration: Connect with your existing identity provider
Custom data retention: Configure how long conversation data is stored based on your policies
API access: Pull conversation data into your existing analytics and BI tools
Human oversight: Trained professionals review conversations and correct AI errors
Accuracy guardrails: The AI only answers from approved source content—no hallucinated specifications or made-up claims
Escalation protocols: Complex or sensitive conversations route to human support
Continuous monitoring: We catch problems before they become patterns
Your lead generation chatbot connects to your existing tech stack. No rip-and-replace.
Native integration with lead, contact, and opportunity objects
Full sync including custom properties and workflows
Enterprise-ready integration
Direct integration with lead scoring sync
Automatic deal and contact creation
Contact information and company data
Complete conversation transcript
AI-generated qualification summary
Lead score with transparent reasoning
Products discussed and questions asked
Competitive mentions and displacement triggers
Recommended follow-up actions
Install through Google Tag Manager in minutes. No IT project required.
Works with all themes and page builders (Elementor, Divi, WPBakery, Beaver Builder)
Supports Drupal 8, 9, and 10
Supports all modern Sitecore versions
Installs via theme editor or Shopify app
Native integration available
Full compatibility with Magento 2.x
Installs alongside native HubSpot forms
Full support including custom domains
Works with code injection
If it can run JavaScript, it can run our form
Beyond website chat, your AI can engage prospects across:
Personalized email outreach that gets opens, replies, and booked meetings.
High-impact SMS messages designed for fast responses and follow-ups.
Automated WhatsApp conversations that feel personal and convert faster.
Engage and qualify leads instantly with automated Messenger chats.
Start real B2B conversations with targeted, personalized LinkedIn outreach.
Same AI. Same training. Same qualification logic. Every channel.
Real scenarios where AI chatbots capture opportunities that static websites miss.
The scenario: An engineer visits your website at 9pm researching sensor solutions for a new project. They have specific questions about operating temperature ranges, communication protocols, and certification requirements. Your sales team is offline.
They submit a contact form. Your rep responds 18 hours later. By then, they've found a competitor who published specs clearly and answered their questions via chat.
The AI engages immediately, answers their technical questions with datasheet-level accuracy, recommends the specific product configuration for their application, and captures their project requirements. Your rep gets a qualified lead with complete context the next morning. The prospect is impressed that someone "knew" their stuff.
The scenario: A procurement manager needs to identify the right solution across your 500-product catalog. They're not sure which product line applies to their use case, let alone which specific SKU.
They click around your website for 10 minutes, get overwhelmed, and leave. Maybe they download a PDF catalog that sits unopened in their downloads folder.
The AI asks about their application, narrows down the relevant product family, recommends specific models based on their requirements, and offers to send a comparison of the top three options. By the end of the conversation, they've self-selected into the right solution—and your sales team knows exactly what they need.
The scenario: A VP of Operations is researching solutions for a problem they're not even sure how to define. They know something's broken in their process, but they don't know what to search for.
They read your homepage, skim a few case studies, and add you to a mental list of "companies to maybe call later." They forget within a week.
The AI asks about their challenges, helps them articulate the problem, explains how similar companies have solved it, and shares a relevant case study. By the end, they understand their problem better—and they associate that clarity with your company. The AI captures their contact info because they genuinely want to continue the conversation.
Metrics from companies that replaced generic chatbots with trained, managed AI.
Chatbot engagement rate
Conversation completion
Leads captured per month
Lead quality (sales-accepted)
Time to first sales contact
Sales cycle length
Rep time on unqualified leads
"Our old chatbot was an embarrassment. Prospects would complain to our sales team about it. Now they compliment us on how helpful the AI is—and our reps actually want the leads it generates because they come with real context."
We don't hand you a login and disappear. Here's exactly what happens.
Week 1-2
• Audit your current website and existing chat (if any)
• Interview your sales team about common questions and qualification criteria
• Collect and organize your product content: specs, documentation, case studies, FAQs
• Map your competitive landscape and displacement opportunities
• Define conversation objectives and lead scoring criteria
Week 2-3
• Structure your content into the AI knowledge base
• Design qualification-focused conversation flows
• Build product recommendation logic
• Configure competitor detection and response triggers
• Set up CRM integration and lead routing rules
Week 3-4
• Run 1,000+ test conversations across all scenarios
• Identify and fix knowledge gaps
• Tune AI responses for accuracy and tone
• Test CRM integration and lead handoff
• Create private preview for your team to challenge the AI
Week 4
• Deploy to your live website
• Monitor real conversations in real-time
• Make rapid adjustments based on actual prospect interactions
• Establish baseline metrics
• Train your sales team on the new lead flow
Ongoing
• Weekly conversation review and AI refinement
• Monthly performance analysis and optimization
• Quarterly strategy sessions to align with business changes
• Ongoing knowledge base updates as products evolve
Right now, someone is on your website with a real question about your product. They're evaluating you against a competitor. They're ready to engage—if someone can just answer their question.
Your generic chatbot can't. Your sales team is asleep, or in meetings, or working other deals. The prospect leaves. They find a competitor who could answer. You never knew they were there.
Every day without a real lead generation chatbot is another day of missed conversations, lost intelligence, and deals that went to competitors who showed up when you didn't.
