Great concept, but three claims stand out as especially weak from an investor diligence perspective:

---

1) “MindMeld AI reads your brainwaves to predict what you want to type before you think it.” (Slide 1)

Why this is weak

• It sounds scientifically implausible as written: “before you think it” is internally contradictory.
• It reads like marketing hype rather than a falsifiable claim.
• Investors will immediately ask: What exactly is being predicted? Intent? Motor planning? Next token? Without precision, credibility drops.

How to strengthen it

• Replace with a testable claim, e.g.:
  “Our non-invasive EEG model predicts intended character/word selection during pre-motor planning with X ms lead time in controlled tasks.”
• Define scope clearly:
  • task type (copy typing vs free composition),
  • lead time (e.g., 150–300 ms),
  • user training required,
  • confidence thresholds.
• Add third-party validation (university lab, peer-reviewed preprint, or independent benchmark).

---

2) “94% accuracy. Works with any language, any device.” (Slide 3)

Why this is weak

• “94% accuracy” is meaningless without context:
  • Accuracy of what? character, word, sentence?
  • Under what conditions? lab vs real-world?
  • With how much calibration per user?
• “Any language, any device” is an extreme generalization and likely false in early-stage BCI.
• EEG signal quality and decoding performance usually vary heavily across users, languages, and hardware environments.

How to strengthen it

• Break claim into measurable metrics:
  • Character error rate (CER), word error rate (WER), words per minute (WPM), latency.
• Segment results:
  • trained users vs new users,
  • quiet lab vs mobile setting,
  • language-specific performance (e.g., English 94%, Spanish 88%, Mandarin 81%).
• Narrow scope honestly:
  • “Currently optimized for English on iOS/Android via companion app; expanding to 3 languages in 12 months.”
• Show benchmark vs alternatives (typing, voice dictation, eye-tracking, EMG bands).

---

3) Market sizing: “BCI market $5.3B by 2030… targeting 3.5B smartphone users… TAM $180B.” (Slide 4)

Why this is weak

• Logical mismatch: citing a $5.3B BCI market but then claiming $180B TAM from smartphone users without showing conversion assumptions.
• “All smartphone users” is not a credible near-term customer base for a headband product.
• No pricing/adoption math is shown; looks like top-down inflation.

How to strengthen it

• Build a bottom-up TAM/SAM/SOM:
  1. Beachhead segment (e.g., accessibility users, high-frequency communicators, enterprise hands-free workflows).
  2. Expected penetration over 3–5 years.
  3. Pricing model (hardware margin + subscription ARPU).
• Example structure:
  • TAM (global eligible users × annual revenue/user),
  • SAM (regions + channels you can actually reach),
  • SOM (realistic share in first 3 years).
• Keep consistency with cited reports; if you claim expansion beyond BCI, explain category logic (assistive tech, productivity hardware, etc.).

---

If you want, I can also rewrite the deck language slide-by-slide so it keeps ambition but sounds investor-grade credible.