I will be honest, the domain of research is outside of my zone of expertise so it's hard for me to fully judge this. However I will try to give useful critical feedback:
1) If you're going to pitch a record album / company next to this, try to integrate it in the long term vision and make that the focus of the pitch, with the current hardware measurement tooling & research as the first steps towards it. otherwise cut it and make separate pitches, because it makes the pitch feel unfocused and erratic.
2) I think the structure and detail of the pitch are great, but I think that it reads too much as AI generated which causes red flags to the reader, because if the text is AI style written slop, then why wouldn't the ideas also be slop? (I'm not saying they are, but you should write it in your own words for people to take it seriously). It's ok to be concise!
3) I do think that the level of work shown in your substack and github are good indicators!
4) Make sure you tie more clearly all these things together. What would be enabled by your theoretical breakthroughs and by this new measurement tool you'd build? Not just what the failure cases for the test are, but why they are significant, and what they would say about the theory, and if correct, what they could enable. Utility, meaning, reason. all of these would make the pitch more clear and coherent. By the way I do think measuring conciousness would be very useful; but it's not coming across well here. I think part of the reason is bc there's too much information. Good pitches, and good research tend to be narrow scoped and explain these in detail, and then build them together.
5) Question: who is seraphina AI
6) Question: What was your research process? I see a lot of domain famous authors and terms cited and combined; and I wonder how did you get there (from one scientist to another!) it's a very wide synthesis, with a major biophysics focus.
7) a thought I have is: why build a new hardware if existing geometric theoretical results were achieved with current hardware? Before spending 20k+ on building hardware, can't you run your software / theoretical tests on more biophoton etc data sets? if you did this and the results were corroborated, it would make the pitch stronger for the hardware step instead of parallelizing it.
Given the use of LLMs; I did use them a little to assist in parsing your pitch, you might find their pushback/Qs interesting (I asked for specific claim feedback from a scientific lens, these are highlights from some of the As to my Qs):
A) Twenty models total, roughly five per condition across four conditions, is a very small sample. With sample sizes that small, Cohen's d can be inflated dramatically — a d of 2.73 from five observations per group is not the same evidential weight as d = 2.73 from fifty per group. The permutation test helps (it's non-parametric, which is appropriate for small samples), but p = 0.0048 with this few observations should be treated as "interesting and worth replicating" rather than "confirmed." The pitch treats it closer to the latter.
B) (I agree w/ this one, but it might be a limitation of a concise pitch deck... although a more clear explanation of the mechanism, connections, and utility are important): The scope-to-evidence ratio is concerning. One statistical result on one dataset of plant tissue biophotons under four chemical conditions is being asked to support a theoretical framework that spans consciousness, quantum biology, microtubule dynamics, bioelectric fields, geometric algebra, and the eventual instantiation of consciousness in non-biological hardware. Narrow claims supported by extensive evidence are easier to evaluate, gradually widening as evidence accumulates.
C) if you run the analysis on more datasets, with more compounds, with known mechanisms, and see whether the geometric signatures sort by coupling mechanism rather than just by chemical identity. If they do, the coupling interpretation strengthens enormously. If they sort by chemical identity regardless of mechanism, you have a useful classification tool but not evidence for the theoretical framework.