Overall read
Ryan, your pitch at the AI Showcase Night received an average audience reaction score of 4.5/5 from 19 responses. The overall sentiment was highly positive, with the majority of respondents indicating "I'd want to talk to them" [Feedback 2, Feedback 3, Feedback 6, Feedback 7, Feedback 8, Feedback 9, Feedback 10, Feedback 11, Feedback 12, Feedback 14, Feedback 15, Feedback 17, Feedback 18, Feedback 19]. A smaller segment of the audience noted they found it "Interesting, but I need more proof" [Feedback 1, Feedback 4, Feedback 5, Feedback 13, Feedback 16].

Product and demo strengths
The live data and visual elements of your presentation were highly successful. Attendees highlighted "The demo with live data" [Feedback 2], "The visual" [Feedback 16], and "The graphing over time was amazing" [Feedback 10] as key strengths. The demo itself "built credibility" [Feedback 15] and provided a "Great explanation of how it works with a great visual" [Feedback 17]. Your delivery was also a strong point, with feedback noting you "spoke confidently" [Feedback 10] and used a "very good storytelling technique" [Feedback 9]. Specific business features like the "Time to deliver (48 hours)" [Feedback 5], "Deterministic inference" [Feedback 13], and the "Timeline for organization and financial transfers" [Feedback 14] also stood out positively.

Business clarity and credibility gaps
Several audience members raised questions about your market positioning and technical validation. One respondent asked, "With companies already investing in data unification, what is your competitive advantage? What is your selling point?" [Feedback 18], while another noted the pitch "Still left the big promise unclear" [Feedback 3]. On the technical and security side, attendees expressed uncertainty regarding "LLM Data hallucination" [Feedback 11], "How to validate or test deterministic behavior" [Feedback 13], "How can you ensure security around data access?" [Feedback 14], and "how you can prove the system isn’t making any mistakes" [Feedback 17].

Representative feedback
"He spoke confidently and had the data to demo. The graphing over time was amazing. Great work and good luck." [Feedback 10]
"Still left the big promise unclear" [Feedback 3]
"With companies already investing in data unification, what is your competitive advantage? What is your selling point?" [Feedback 18]
"Show more technical details (step by step process) of the implementation" [Feedback 5]

Suggested next experiments
- Refine your "Sales messaging" [Feedback 3] to clearly define your competitive advantage and core promise.
- Incorporate a brief "step by step process" [Feedback 5] of the implementation to address technical and security questions.
- Make the presentation "more relatable for example at the employee level" [Feedback 1].
- Clarify "The product vision and use cases" [Feedback 8] to help the audience understand the broader application of your technology.
- Edit the presentation flow to ensure it does not feel "a little redundant at some points" [Feedback 9].

Overall read
Ryan, your presentation at the AI Showcase Night was highly engaging, earning an average audience reaction score of 4.5/5 from 19 responses. The majority of attendees indicated they would want to talk to you after the pitch [Feedback 2, 3, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19]. While the overall reception was strong, a few audience members noted they still need more proof before being fully convinced [Feedback 1, 4, 5, 13, 16].

Product and demo strengths
The live data demo and visual elements were major highlights of your presentation [Feedback 2, 12, 15, 16]. Attendees praised the "graphing over time" [Feedback 10] and noted that you provided a "great explanation of how it works with a great visual" [Feedback 17]. Your delivery was highly effective, with comments highlighting your "very good storytelling technique" [Feedback 9] and noting that you "spoke confidently" [Feedback 10]. Specific product features that stood out included "Deterministic inference" [Feedback 13], the "Time to deliver (48 hours)" [Feedback 5], and the "Timeline for organization and financial transfers" [Feedback 14].

Business clarity and credibility gaps
Some audience members identified gaps in your business case and technical validation. A key concern was how you differentiate from existing solutions, with one attendee asking, "With companies already investing in data unification, what is your competitive advantage? What is your selling point?" [Feedback 18]. Others felt you "Still left the big promise unclear" [Feedback 3]. On the technical side, attendees raised questions about "LLM Data hallucination" [Feedback 11], "How to validate or test deterministic behavior" [Feedback 13], how you "ensure security around data access" [Feedback 14], and how you "prove the system isn’t making any mistakes" [Feedback 17].

Representative feedback
"The demo with live data" [Feedback 2]
"He spoke confidently and had the data to demo. The graphing over time was amazing." [Feedback 10]
"With companies already investing in data unification, what is your competitive advantage? What is your selling point?" [Feedback 18]
"Wondering how you can prove the system isn’t making any mistakes." [Feedback 17]

Suggested next experiments
To improve your pitch for the next event, consider the following adjustments based on attendee suggestions:
- Refine your "Sales messaging" [Feedback 3] to clearly articulate your unique competitive advantage [Feedback 18].
- Share more "technical details (step by step process) of the implementation" [Feedback 5] to address concerns around validation, security, and error prevention [Feedback 13, 14, 17].
- Clarify "The product vision and use cases" [Feedback 8] and make the presentation "relatable for example at the employee level" [Feedback 1].
- Streamline your delivery to avoid repetition, as one attendee noted it "felt a little redundant at some points" [Feedback 9].

Feedback report for Ryan Hanley

Taskd, presented by CEO Ryan Hanley, addresses the data-rich, answer-poor challenge faced by enterprises attempting to use AI on unstructured, siloed datasets. While many organizations have adopted LLMs, these models often struggle with real operational data due to a lack of underlying logic, leading to compounding errors. Taskd solves this by building a deterministic logic layer—Operational AI—underneath the AI models to harmonize data and enforce business rules.
The Taskd platform operates through a five-stage pipeline. First, it uses