Meet CaseForge AI—the intelligent simulation platform that turns workplace knowledge into confident, measurable performance.
Designed for technical-support teams, CaseForge transforms company-approved SOPs, knowledge-base articles, product documentation, and real support cases into realistic, AI-powered customer conversations. Instead of moving directly from courses and quizzes into live customer interactions, new hires gain a safe environment where they can practise diagnosing unfamiliar problems, asking effective questions, finding relevant evidence, explaining decisions, and recognizing when escalation is necessary.
CaseForge evaluates more than the final answer. Using expert-defined rubrics, it examines each learner’s reasoning, communication, evidence use, and decision-making process. This gives managers meaningful evidence of workplace readiness, reveals recurring skill gaps, and supports more focused coaching.
By adding a controlled practice layer between training and live work, CaseForge helps organizations develop capable agents, improve service consistency, reduce avoidable mistakes, and ensure new hires are truly ready before the real customer becomes the test.
Click to view my Elevator Pitch
Click to view my Venture Pitch
Click to view my Venture Pitch Slides
REVIEW: Great job Erika, I enjoyed listening to your VP!
Here are the strengths and weaknesses of your VP outlined below:
1. Your problem is clearly outlined. This is a relevant and useful venture pitch idea, I am unfamiliar with the tech support industry but with a lot of workplaces nowadays, when it comes to being trained- there seems to be minimal training at first and then a “sink or swim” mentality. So this helps support those new hires who may feel overwhelmed at first being new to the job. What stood out is your reasoning-based assessment and seeing that being practiced in real-time in your video, mimicked a real interaction between a tech support agent and customer making it plausible.
2. “The missing practice layer” is compelling, and CaseForge does not present as just another AI chatbot.
3.The pilot strategy makes the venture feel more realistic and I’m glad you included a timeline of how it will play out.
Weaknesses:
1. Who exactly is your market? I think you plan to start with department licenses and then to enterprise? This was stated on the last slide, but I had to watch your VP two times to understand this. Being more specific in general on who the first customer is and how large the market would be for that would help me understand the market better before I decide to fully invest.
2. Your marketing strategy is strong (Land–> Prove–> Expand) but it lacks a specific customer profile and pricing model. This would make it stronger.
3. You are clearly very capable with using AI, but I wish the Elevator Pitch and VP had the same AI sounding voice (if that makes sense), so it felt like the same person pitching the product to me and more personal.
Overall it was great and just a few tweaks would make it fully ready to be invested in!
Overall, I thought this was a really strong and professional pitch. The problem was easy to understand, especially the idea that completing training does not necessarily mean someone is ready to handle a real situation. I also liked how CaseForge was positioned as a practice layer rather than just another chatbot. The screenshots of the practice library, simulation, and feedback report made the product feel much more developed and helped show how it could actually be used.
One thing I would have liked to see a little more of was the actual business model after the pilot stage. The pitch does a good job explaining the 8-week pilot and the move from a department licence to enterprise expansion, but I was still wondering what organizations would actually pay, what the pricing might look like, and what the recurring revenue model would be. I think adding that would make the investment side of the pitch even stronger
Feedback
Strengths – Seems like something that could really be effective in improving call quality and experience. Good supporting images and presentation makes me interested in learning more about it.
Weaknesses – Many companies do not care if they have good service, and are also moving to AI workers. Those that care about good service probably have good training already, so not sure if this would be filling a gap in the market.
Overall I think it is a good idea in terms of improving service, but not sure if it meets the current needs of the market. The elevator pitch does make me interested in learning more though
Hi Erika,
I really enjoyed exploring CaseForge AI. What stood out to me was the idea of creating a practice layer between completing training and actually working with real customers. There can be such a big difference between knowing something and being able to confidently apply that knowledge in a real situation, and I think CaseForge addresses that gap in a really practical way. I also liked that the simulations are grounded in an organization’s own knowledge and real situations rather than relying on a generic AI chatbot.
I was particularly interested in the way CaseForge looks at how someone arrives at an answer, rather than just whether they get the answer right. Looking at how employees use evidence, communicate, make decisions, and recognize when they need to escalate could give managers a much better understanding of where support or coaching is actually needed. I could also see those patterns helping organizations improve the training itself.
As an EVA, I would invest in CaseForge AI. I think you have identified a genuine gap between completing training and being ready to apply that learning in the workplace, and I can see a lot of potential in the idea.
Your pitch also left me wondering:
Could the patterns CaseForge identifies across employees eventually become one of the most valuable parts of the platform for organizations?
Could you see CaseForge eventually moving beyond technical support into other areas where people need to practice judgment and decision-making before working independently?
Great work, Erika! I would be really interested to see where you take this next.
This was a REVIEW
REVIEW: Hi Unidon,
I think CaseForgeAI presents a really strong solution to the gap between completion training and being ready to apply that learning in real customer interactions. I liked that CaseForge evaluates how employees arrive at an answer, including their reasoning, communication, evidence use, and escalation decisions, rather than simply whether they reach the correct outcome. This use of organization-specific knowledge also makes the simulation feel much more valuable than a generic AI training tool.
From an EVA lens, that strength also raised a question for me around data governance and security. Because CaseForge would use company SOPs, knowledge-based articles, product documentation, and potentially real support cases, organizations could be providing commercially sensitive data/information or customer data to the platform. As a potential investor/customer, I would want greater transparency and clarity around how that information would be stored, protected, and used, including whether it could be used for model training.
Additionally, I also wondered about transparency in the assessment itself. If CaseForge is using AI to evaluate an employee’s reasoning, communication, evidence use, and readiness for live work, how would employees and managers understand or challenge how those judgments were reached? For example, NIST’s AI Risk Management Framework identifies transparency, explainability, privacy, fairness, and accountability as important characteristics of trustworthy AI systems (Tabassi, 2023). Canadian guidance on AI in the workplace similarly emphasizes transparency, human oversight, explainability, and opportunity for redress when AI contributes to workplace decisions (Employment and Social Development Canada, 2026). I would be hesitant about relying on a black-box assessment if the results could somehow influence workplace decisions. Building explainability, clear evaluation criteria, and meaningful human oversight into CaseForge would strengthen my confidence in your venture.
References
Employment and Social Development Canada. (2026). Compendium of best practices for a human-centered development and use of artificial intelligence in the world of work. Government of Canada. https://www.canada.ca/en/employment-social-development/corporate/reports/2025-best-practices-artificial-intelligence
Tabassi, E. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
Great job. Your project is clear, thoughtful, and strongly connected to the course concepts. CaseForge addresses a real gap between completing training and being ready for real workplace situations. Its use of company-based simulations and expert-designed assessment makes it more valuable than a basic AI chatbot.
From an investment perspective, the pilot-first Land → Prove → Expand strategy is practical and lowers early risk. I would like to see clearer pricing, a more defined target customer, and stronger attention to data privacy. Overall, I see CaseForge AI as a promising venture and would consider investing
Feedback
FEEDBACK
Great job Erika! I really enjoyed exploring CaseForge AI and thought the idea of creating a “missing practice layer” between completing training and actually working with customers was very strong. I think this addresses a real problem in many workplaces where new employees can go from training directly into a “sink or swim” environment. I especially liked that CaseForge looks at the reasoning behind an employee’s decisions, rather than simply whether they got the correct answer. That could provide managers with much more useful information for coaching and identifying where additional training is needed. The simulated customer interaction also made the concept feel practical and realistic rather than just another AI chatbot.
My biggest area for improvement would be making the target market more obvious in the Elevator Pitch. I wasn’t sure who the first customer would be and how you planned to move from department-level licenses toward enterprise adoption. I think clearly identifying your initial customer, pricing model, and market size would make the investment opportunity much easier to understand. I also thought your Land–Prove–Expand strategy was strong, but connecting it more directly to a specific customer profile would make it even stronger.
Overall, I think CaseForge has a lot of potential and I would be interested in seeing it move forward. A few changes to clarify the market and make the pitch feel more personal would make me much more confident in investing.
REVIEW
Hi Erika,
CaseForge AI is well presented to addresses a gap between completing workplace training and demonstrating readiness in authentic customer interactions. The “missing practice layer” stood out in your pitch because o the connection to education and other professions that have relied on this model for decades. Teacher candidates complete practicum placements before leading a classroom independently, physicians complete clinical residencies, and pilots spend hours in simulators before flying passengers. CaseForge applies the same evidence-informed progression to workplace onboarding, making the educational rationale behind the venture compelling.
The emphasis on evaluating reasoning, communication, evidence use, and escalation decisions rather than simply checking the final answer also differentiates the platform from more generic AI training tools.
One interesting connection I kept thinking about was Lyrea, another venture from our cohort. Rather than competing, I could see the two concepts complementing one another. Lyrea’s three interconnected sources of intelligence: software knowledge, expert workflows, and user interaction insights could strengthen the realism and adaptability of CaseForge’s simulations, creating an enterprise learning ecosystem where contextual guidance and deliberate practice work together.
My remaining question relates to the initial pilot. The examples of organizations helped illustrate the opportunity, but I would have liked a clearer picture of the first industry and customer segment being targeted before broader expansion.
I would recommend funding CaseForge AI, with the condition that the initial pilot clearly defines the first industry and validates adoption before expanding into additional sectors.
FEEDBACK
CaseForge AI is a really good pitch. The concept of a practice layer between training and live customer interactions makes sense, and the focus on evaluating reasoning and decision-making rather than just final answers is a smart differentiator from generic AI chatbots.
I would not invest in this venture as it is. I am curious how this will be affected by the trend of companies replacing human support agents with AI entirely, which could shrink the target market over time.
REVIEW
Hi Unidon, CaseForge AI is a compelling solution to the workplace readiness gap you’ve identified between completing technical support training and applying it effectively in live customer interactions. Positioning it as a critical interactive practice layer is valuable differentiation, and your experience as a tech support instructional designer makes you a credible founder to execute on it.
As an EVA, I’m inclined to invest, pending more clarity on a few points:
First, the CAD $250K ask doesn’t include a use-of-funds breakdown across Build, Pilot, Validate stages, making capital efficiency hard to assess. I’d also want to know whether the enterprise pilot partner pays for the pilot, or whether the $250K subsidizes it, meaning the ask is effectively capital plus a strategic partner.
Second, the Scenario Setup slide shows a manual, per-scenario build process. I’d want to understand the cost to build a new scenario set per vertical or client, and whether there’s a roadmap to automate it, since that determines whether CaseForge scales as software margin or drifts toward a lower-margin, consulting-heavy model.
Third, the competitive landscape is framed by category rather than by named competitors, even though market alternatives such as Zenarate and Second Nature already offer AI-simulated customer-support training. I’d want to see how CaseForge differentiates from them specifically.
Lastly, the pitch doesn’t include any market sizing, only third-party validation examples like Walmart and Klarna. I’d want to see a bottom-up sizing of the serviceable market for this category, along with the ownership percentage and early-stage investment instrument behind the ask, to get a clearer sense of expected investor return.
Overall, a strong pitch with meaningful differentiation potential. I’d recommend investing, contingent on the points above being addressed.
Review:
Hi Erika,
I will echo the same sentiment as many other people on this thread: Wonderful job on this venture pitch!
I think your venture has great potential. You have identified a common training challenge across multiple sectors and developed an effective solution through CaseForge AI. The venture also aligns well with the growing use of AR and VR technologies for training in fields such as health care, where learners benefit from having a safe environment to practice before working with real patients or clients. This type of experiential training has become increasingly common in post-secondary institutions across Canada, and research has shown its effectiveness in developing competencies and practical skills. This gives me greater confidence in the potential of CaseForge AI as an investment.
What stood out to me most about the platform is its practice library, especially the workplace-specific scenarios and competency-building activities tailored to each learner’s workplace needs. I also like the instant feedback learners receive after completing a simulation. In a real-world situation, it’s not always possible to receive immediate feedback when something goes wrong. I think this is what makes CaseForge AI an excellent product.
Overall, I would feel confident investing in this CaseForge AI. The inclusion of an 8 week pilot to develop and test an MVP is appealing because it provides an opportunity to validate the concept, gather user feedback, and refine the platform before launch.
Well done, Erika! 🙂
Edith
FEEDBACK: Convincing pitch! CaseForge AI seems like a very practical training tool for tech support teams who need a safe space to practice customer interactions. Your background gives this pitch a clear focus towards the readiness gap and the simulation solutions you propose.
Through an EVA lens, the concept is strong because it evaluates how a learner thinks rather than just checking a multiple choice answer. As a teacher, I know that designing authentic assessments is incredibly tough and time consuming, so I can see the value in automating that process for managers. While I see the potential to replace sink or swim onboarding practices, marketability and future growth are my biggest questions. I believe the biggest risk here is that your target market might be shrinking as businesses automate their help desks entirely. Because of that uncertainty, CaseForge feels like a supplementary purchase for a large enterprise rather than a necessary purchase.
Funding recommendation: Do not fund at this time. While your pilot and expand strategy makes sense, I would need to see data proving it actually reduces escalations and a clearer plan for competing against generic AI integrations before I could comfortably invest.
As a corporate instructor that also onboards new workers, I can see the value in this venture. It is very well thought out and contains many of the features that are relevant for that niche. I can also see it being adapted for other niche areas.
I found your information well organized and easy to follow. Your videos were polished and professional. I found the elevator pitch a little long and detailed but still valuable and polished. Your project was enjoyable to review.