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Executive Summary
Mission Statement:
At Verbose AI, our vision is clear: a future where products learn you, not vice versa. We believe that AI has the potential to bring companies closer to their customers—by solving problems for customers accurately and efficiently directly inside products.
Verbose AI helps companies in transitioning more and more capabilities to AI, while maintaining tight control over user experience. We balance carefully what AI can do today, with the ability to seamlessly transition into a future of yet to be discovered advanced capabilities.
Company Overview:
The founders met in late 2023 while developing a zero-to-one MVP for a different startup. We shared a passion for the latest technologies and quickly turning ideas into problem-solving products. In the summer of 2024, having wrapped up the earlier project, we began exploring the combination of voice-based interfaces and in-product navigation, building a few prototypes, testing with potential users, and validating the idea. This led to Verbose AI— an in-product Assistant that gives businesses full control over the answers an AI gives, and navigates product UIs on behalf of the end-users. We see tremendous potential for AI to bring businesses closer to their end-users, making the product experience more intuitive, responsive, and efficient.
Investment Opportunity:
Verbose AI is seeking €0.8M in pre-seed funding through a SAFE with a 20% discount. This investment will enable us to bring our vision to life alongside our design customers. Our goal is ambitious yet achievable: to build the company to €1M ARR within 15 months through a Product-Led Growth (PLG) strategy.
At Verbose AI, our vision is clear: a future where products learn you, not vice versa. We believe that AI has the potential to bring companies closer to their customers—by solving problems for customers accurately and efficiently directly inside products.
Verbose AI helps companies in transitioning more and more capabilities to AI, while maintaining tight control over user experience. We balance carefully what AI can do today, with the ability to seamlessly transition into a future of yet to be discovered advanced capabilities.
Company Overview:
The founders met in late 2023 while developing a zero-to-one MVP for a different startup. We shared a passion for the latest technologies and quickly turning ideas into problem-solving products. In the summer of 2024, having wrapped up the earlier project, we began exploring the combination of voice-based interfaces and in-product navigation, building a few prototypes, testing with potential users, and validating the idea. This led to Verbose AI— an in-product Assistant that gives businesses full control over the answers an AI gives, and navigates product UIs on behalf of the end-users. We see tremendous potential for AI to bring businesses closer to their end-users, making the product experience more intuitive, responsive, and efficient.
Investment Opportunity:
Verbose AI is seeking €0.8M in pre-seed funding through a SAFE with a 20% discount. This investment will enable us to bring our vision to life alongside our design customers. Our goal is ambitious yet achievable: to build the company to €1M ARR within 15 months through a Product-Led Growth (PLG) strategy.
Problem Statement
Market Gap:
Today’s conversational AI solutions are primarily built to analyze general themes and sentiment from users, but they fail to translate user-specific intent into actionable results. For example, businesses face challenges when trying to interactively guide users through their digital interfaces based on each user’s unique needs, leaving many support issues unresolved. Chatbots—while useful for deflecting general inquiries—lack the deep integration necessary to help users complete tasks within a product’s UI. Existing solutions are often too expensive for small and medium-sized businesses (SMBs) that cannot justify the cost of platforms like Zendesk or Intercom outside of core support personnel. Additionally, most solutions force businesses to rely on specific AI models dictated by the platform, limiting flexibility.
Evidence of Need:
We conducted 30+ interviews with decision-makers across a variety of industries, including large-scale enterprises like Mercedes-Benz, HelloFresh, and Personio, as well as smaller startups such as Honeysales and Ghost.org. The feedback we received was consistent: businesses are struggling with a lack of control over how AI-generated responses are delivered, an inability to deeply understand user intent, and pricing models that inhibit true internal collaboration. Importantly, companies expressed frustration with chatbots that failed to help users solve problems within product interfaces, instead providing vague information that often requires further support. SMBs, in particular, voiced concerns over their inability to afford such tools as most AI features are reserved for the highest priced tiers.
Today’s conversational AI solutions are primarily built to analyze general themes and sentiment from users, but they fail to translate user-specific intent into actionable results. For example, businesses face challenges when trying to interactively guide users through their digital interfaces based on each user’s unique needs, leaving many support issues unresolved. Chatbots—while useful for deflecting general inquiries—lack the deep integration necessary to help users complete tasks within a product’s UI. Existing solutions are often too expensive for small and medium-sized businesses (SMBs) that cannot justify the cost of platforms like Zendesk or Intercom outside of core support personnel. Additionally, most solutions force businesses to rely on specific AI models dictated by the platform, limiting flexibility.
Evidence of Need:
We conducted 30+ interviews with decision-makers across a variety of industries, including large-scale enterprises like Mercedes-Benz, HelloFresh, and Personio, as well as smaller startups such as Honeysales and Ghost.org. The feedback we received was consistent: businesses are struggling with a lack of control over how AI-generated responses are delivered, an inability to deeply understand user intent, and pricing models that inhibit true internal collaboration. Importantly, companies expressed frustration with chatbots that failed to help users solve problems within product interfaces, instead providing vague information that often requires further support. SMBs, in particular, voiced concerns over their inability to afford such tools as most AI features are reserved for the highest priced tiers.
Solution and Technology
Product/Service Description:
Verbose AI is designed to intelligently analyze a company’s Help Center and Product UI to predict a network of common question-answer-solution pathways for companies to immediately verify and deploy in their assistant. Rather than relying on past support tickets, our system predicts user questions and generates answers and proactively suggests solutions. This ability to anticipate queries before they arise sets Verbose AI apart from traditional reactive support solutions. End-users benefit from verified answers that give them certainty over answer correctness.
Unique Value Proposition:
Our intent-to-action framework allows businesses an unprecedented level of control over how user queries are handled, and our system doesn’t simply provide information—it guides users interactively within the product UI, helping them solve issues directly without unnecessary deflection. This approach is contrary to the trend to cram all functionality into a chat window—instead making best use of every pixel available. Our customers are able to prioritize their automation efforts on the issue-level and decide whether to use non-verified answers without human review.
Technology Stack:
We use state-of-the-art prompting strategies such as Summarization, Keywording, Context Augmentation, Ground Truth, and Retrieval-Augmented Generation (RAG) for Issue Classification and Question Generation. Our unique intent-to-action framework is not bound to any specific language model, making our solution flexible and adaptable to various customer needs.
Verbose AI is designed to intelligently analyze a company’s Help Center and Product UI to predict a network of common question-answer-solution pathways for companies to immediately verify and deploy in their assistant. Rather than relying on past support tickets, our system predicts user questions and generates answers and proactively suggests solutions. This ability to anticipate queries before they arise sets Verbose AI apart from traditional reactive support solutions. End-users benefit from verified answers that give them certainty over answer correctness.
Unique Value Proposition:
Our intent-to-action framework allows businesses an unprecedented level of control over how user queries are handled, and our system doesn’t simply provide information—it guides users interactively within the product UI, helping them solve issues directly without unnecessary deflection. This approach is contrary to the trend to cram all functionality into a chat window—instead making best use of every pixel available. Our customers are able to prioritize their automation efforts on the issue-level and decide whether to use non-verified answers without human review.
Technology Stack:
We use state-of-the-art prompting strategies such as Summarization, Keywording, Context Augmentation, Ground Truth, and Retrieval-Augmented Generation (RAG) for Issue Classification and Question Generation. Our unique intent-to-action framework is not bound to any specific language model, making our solution flexible and adaptable to various customer needs.
Market Opportunity
Total Addressable Market (TAM):
The market opportunity is immense, with over $100B in market value ready for disruption. The Helpdesk SaaS market alone is valued at $12B, while customer support outsourcing is estimated at $93B. The first-gen chatbot market, currently valued at $4.5B, is expected to grow to $32B by 2030, with a CAGR of 21.3%. Similarly, the customer support outsourcing and Helpdesk markets are forecasted to grow at CAGRs of 9.1% and 9.4%, respectively.
Target Customer Segments:
Our initial focus is on SMB SaaS companies, of which there are approximately 30,000 globally. This segment is ripe for a solution like Verbose AI, with its flexibility, affordability, and effectiveness.
Market Trends:
While all the markets mentioned above are projected to grow over the next decade, we believe that the market for action-taking Contextual Intelligent Assistants, like Verbose AI, will grow even faster as it provides a distinct, more valuable offering than traditional chatbots.
The market opportunity is immense, with over $100B in market value ready for disruption. The Helpdesk SaaS market alone is valued at $12B, while customer support outsourcing is estimated at $93B. The first-gen chatbot market, currently valued at $4.5B, is expected to grow to $32B by 2030, with a CAGR of 21.3%. Similarly, the customer support outsourcing and Helpdesk markets are forecasted to grow at CAGRs of 9.1% and 9.4%, respectively.
Target Customer Segments:
Our initial focus is on SMB SaaS companies, of which there are approximately 30,000 globally. This segment is ripe for a solution like Verbose AI, with its flexibility, affordability, and effectiveness.
Market Trends:
While all the markets mentioned above are projected to grow over the next decade, we believe that the market for action-taking Contextual Intelligent Assistants, like Verbose AI, will grow even faster as it provides a distinct, more valuable offering than traditional chatbots.
Business Model
Revenue Streams:
We’ve developed a simple, scalable business model that aligns with the value generated for our customers:
Our pricing model is designed to align closely with the value our customers receive. For every issue resolved via AI, businesses pay $0.99—a price point significantly lower than a conservative estimate of $5/issue solved by humans. Additionally, we charge a €7k annual platform license fee, which equals to only 4 seats annually in Helpdesk SaaS incumbents. This low-cost approach allows us to cater to the SMB market, a segment often priced out by incumbents.
More importantly, given our strong margins, we have a lot of room to move with the market. We are able to also differentiate between different resolution classes—e.g., issues directly solved in the product UI can be priced lower. Verified answers can be priced lower, creating a strong incentive for companies to verify answers. Increased usage of the product will not directly incur prohibitive costs: we will make sure that pricing aligns with value also at scale—and we have the margins to deliver that.
Delivery Cost:
Our approach benefits from the ability to use traditional lexical matching with both Small and Large Language Models as well as Vision models for specific tasks. Our costs today without any optimization are exceedingly small. Additionally, we are confident that costs to run AI models will keep going down.
We’ve developed a simple, scalable business model that aligns with the value generated for our customers:
- $0.99 per issue resolved via AI.
- An annual platform license fee of €7k.
Our pricing model is designed to align closely with the value our customers receive. For every issue resolved via AI, businesses pay $0.99—a price point significantly lower than a conservative estimate of $5/issue solved by humans. Additionally, we charge a €7k annual platform license fee, which equals to only 4 seats annually in Helpdesk SaaS incumbents. This low-cost approach allows us to cater to the SMB market, a segment often priced out by incumbents.
More importantly, given our strong margins, we have a lot of room to move with the market. We are able to also differentiate between different resolution classes—e.g., issues directly solved in the product UI can be priced lower. Verified answers can be priced lower, creating a strong incentive for companies to verify answers. Increased usage of the product will not directly incur prohibitive costs: we will make sure that pricing aligns with value also at scale—and we have the margins to deliver that.
Delivery Cost:
Our approach benefits from the ability to use traditional lexical matching with both Small and Large Language Models as well as Vision models for specific tasks. Our costs today without any optimization are exceedingly small. Additionally, we are confident that costs to run AI models will keep going down.
- AI cost per resolution: €0.001–€0.005.
- AI cost per customer setup: €10–€100.
- Cloud costs: less than €5 per customer per month.
Go-to-Market Strategy
Marketing Plan:
To drive awareness and customer acquisition, we are adopting a multi-faceted marketing approach. This includes:
Our sales strategy leverages both direct sales and Product-Led Growth (PLG) to reach a wide range of customers:
We will showcase Verbose AI’s value proposition through demo assistants created using public customer materials before the initial sales call. This strategy is based on the principle that “Seeing is Believing”—prospective customers will immediately understand how our product solves their issues.
Growth Plan:
Our initial growth team will be structured into a pod composed of an analyst, creative, hacker, and sales expert. This structure allows us to rapidly iterate and experiment across different marketing and sales channels. To identify high-potential leads, we will use an automatic detection system that evaluates companies’ Help Center content, existing chatbot implementations, and product UI complexity. Our dual approach—mixing PLG and Sales-Led Growth (SLG)—will allow us to efficiently scale, acquiring SMBs through self-service while engaging enterprises through a more hands-on approach.
To drive awareness and customer acquisition, we are adopting a multi-faceted marketing approach. This includes:
- Paid Advertising: Targeted ads across platforms like Google, LinkedIn, and Facebook to reach key decision-makers in SMB SaaS companies.
- Content Marketing and Sponsorships: Partnering with relevant content creators and influencers who can effectively communicate the benefits of Verbose AI. By sponsoring podcasts and blog posts in tech communities, we aim to generate organic traction.
- SEO Optimization: We will develop content-driven strategies, including blog posts, case studies, and whitepapers focused on how our AI assistant is uniquely suited for SMBs. Optimizing our site for keywords that align with customer search behavior will further drive inbound traffic.
Our sales strategy leverages both direct sales and Product-Led Growth (PLG) to reach a wide range of customers:
- Direct Sales for Enterprise Accounts: For larger, more complex implementations, our sales team will engage in personalized outreach and lead nurturing to secure enterprise clients.
- PLG Approach for SMBs: SMBs will be able to sign up directly through our platform, offering them a seamless, self-serve experience. By focusing on PLG, we reduce the friction and costs typically associated with traditional sales cycles.
We will showcase Verbose AI’s value proposition through demo assistants created using public customer materials before the initial sales call. This strategy is based on the principle that “Seeing is Believing”—prospective customers will immediately understand how our product solves their issues.
Growth Plan:
Our initial growth team will be structured into a pod composed of an analyst, creative, hacker, and sales expert. This structure allows us to rapidly iterate and experiment across different marketing and sales channels. To identify high-potential leads, we will use an automatic detection system that evaluates companies’ Help Center content, existing chatbot implementations, and product UI complexity. Our dual approach—mixing PLG and Sales-Led Growth (SLG)—will allow us to efficiently scale, acquiring SMBs through self-service while engaging enterprises through a more hands-on approach.
Competitive Analysis
Competitor Overview:
Our direct competitors include companies like Command.ai and Ada.cx, which also offer AI-powered solutions. However, Command.ai focuses primarily on unstructured product guides and lacks the resolution-based pricing model that makes Verbose AI attractive to SMBs. Verbose AI’s automated onboarding and product-led growth strategies enable businesses to deploy AI assistants rapidly, while competitors rely on slower, license-based models. Incumbent players like Zendesk, Intercom, and HubSpot cater primarily to large enterprises, charging high fees and using seat-based pricing models that don’t align with SMB budgets. Verbose AI, with its clear focus on resolution-based outcomes and SMB adoption, is positioned to capture market share from these traditional solutions.
Competitive Advantage:
We stand out due to our solution-centric design, in-product guidance, and competitive pricing. Our approach is designed to maximize adoption and minimize barriers for SMBs.
Our direct competitors include companies like Command.ai and Ada.cx, which also offer AI-powered solutions. However, Command.ai focuses primarily on unstructured product guides and lacks the resolution-based pricing model that makes Verbose AI attractive to SMBs. Verbose AI’s automated onboarding and product-led growth strategies enable businesses to deploy AI assistants rapidly, while competitors rely on slower, license-based models. Incumbent players like Zendesk, Intercom, and HubSpot cater primarily to large enterprises, charging high fees and using seat-based pricing models that don’t align with SMB budgets. Verbose AI, with its clear focus on resolution-based outcomes and SMB adoption, is positioned to capture market share from these traditional solutions.
Competitive Advantage:
We stand out due to our solution-centric design, in-product guidance, and competitive pricing. Our approach is designed to maximize adoption and minimize barriers for SMBs.
Team and Advisory Board
Founding Team:
- Manu Raivio, CEO (Google, Amazon, Umbra3D, SuperAI).
- Piotr Dobrowolski, CTO (Visa, Scout24, NestJS, Disney+).
Financials and Projections
We are happy to share our detailed projections and budget upon request.
Funding Requirements and Use of Funds
Funding Requirements:
Verbose AI is seeking €0.8M in pre-seed funding. This capital will be allocated to strategic hires and growth initiatives, ensuring we reach key milestones. The funds will be used to:
Verbose AI is seeking €0.8M in pre-seed funding. This capital will be allocated to strategic hires and growth initiatives, ensuring we reach key milestones. The funds will be used to:
- Build Core Team: We will hire a Designer and a Full-stack Engineer to enhance the core product and ensure scalability as we acquire customers.
- Expand Growth Team: Hiring an Analyst, Creative, Growth Hacker, and Sales Lead will help us execute our go-to-market strategy and optimize customer acquisition funnels. These key hires will be critical to ensuring our platform can scale effectively and meet the demands of our growing customer base.
- Operational Milestones: Our primary goal is to achieve €1M ARR with 100 paying customers in Q4 of 2025.
Risk Analysis and Mitigation
Product & Technical Risks
Risk: Our system might struggle to parse and navigate customer user interfaces, which could hinder integration.
Mitigation: Carefully choose initial customers whose UIs are straightforward.
Likelihood: Medium
Impact: High
Risk: It proves challenging to predict the end-users questions, which may affect our ability to provide value.
Mitigation: Access past questions from pilot customers to improve prediction accuracy.
Likelihood: Medium
Impact: Medium
Risk: Automating processes requires integrating with customer systems, which can be complex.
Mitigation: Prioritize automation opportunities by volume to demonstrate impact.
Likelihood: High
Impact: High
Growth Go-to-Market Risks
Risk: Onboarding new customers might need significant support, slowing down growth.
Mitigation: Automate onboarding with LLMs and pre-qualify leads to ensure suitability.
Likelihood: Medium
Impact: High
Risk: Customers may have annual contracts with existing providers, delaying their ability to switch.
Mitigation: Offer usage-only pricing until their current contracts expire.
Likelihood: High
Impact: Low
Risk: Strong alternative products are available, making customer acquisition tougher.
Mitigation: Highlight differentiation and affordability to attract customers.
Likelihood: Medium
Impact: High
Managerial Risks
Risk: We might not have enough engineering resources to meet development needs.
Mitigation: Lean into founder network of freelancers we’ve worked with in the past.
Likelihood: Low
Impact: Medium
Risk: Founder-led sales are progressing too slowly to meet our goals.
Mitigation: Hire a personable sales rep to open and close, while founders handle substantive middle of sales cycle.
Likelihood: High
Impact: High
Risk: Our annual recurring revenue (ARR) growth may not hit our targets.
Mitigation: Scale hiring slower than projected to maintain run rate.
Likelihood: Medium
Impact: High
Risk: Our system might struggle to parse and navigate customer user interfaces, which could hinder integration.
Mitigation: Carefully choose initial customers whose UIs are straightforward.
Likelihood: Medium
Impact: High
Risk: It proves challenging to predict the end-users questions, which may affect our ability to provide value.
Mitigation: Access past questions from pilot customers to improve prediction accuracy.
Likelihood: Medium
Impact: Medium
Risk: Automating processes requires integrating with customer systems, which can be complex.
Mitigation: Prioritize automation opportunities by volume to demonstrate impact.
Likelihood: High
Impact: High
Growth Go-to-Market Risks
Risk: Onboarding new customers might need significant support, slowing down growth.
Mitigation: Automate onboarding with LLMs and pre-qualify leads to ensure suitability.
Likelihood: Medium
Impact: High
Risk: Customers may have annual contracts with existing providers, delaying their ability to switch.
Mitigation: Offer usage-only pricing until their current contracts expire.
Likelihood: High
Impact: Low
Risk: Strong alternative products are available, making customer acquisition tougher.
Mitigation: Highlight differentiation and affordability to attract customers.
Likelihood: Medium
Impact: High
Managerial Risks
Risk: We might not have enough engineering resources to meet development needs.
Mitigation: Lean into founder network of freelancers we’ve worked with in the past.
Likelihood: Low
Impact: Medium
Risk: Founder-led sales are progressing too slowly to meet our goals.
Mitigation: Hire a personable sales rep to open and close, while founders handle substantive middle of sales cycle.
Likelihood: High
Impact: High
Risk: Our annual recurring revenue (ARR) growth may not hit our targets.
Mitigation: Scale hiring slower than projected to maintain run rate.
Likelihood: Medium
Impact: High