Looking for 2 AI Startups Preparing Demo Day
I’m looking for 2 early-stage AI startups who want to avoid the nightmare moment:
Everything is going fine
You’re demoing the product
And suddenly the LLM leaks something internal
Or an endpoint shows up
Or the system prompt flashes for half a second and the investor notices.
I help founders make sure that never happens.
I’m an AI cloud security engineer and I prevent embarrassing security leaks during demos and investor's due diligence.
I normally charge $97 for the red-flag audit and $650 for the full demo-safe security sprint, but I’m offering it free to 2 AI founders this week.
why?
I’m building fresh case studies around “demo-safe AI security” and just want honest feedback in exchange.
what you’ll get:
• quick LLM red-flag scan
• AWS or GCP misconfig check
• “demo safety” stress test
• a 1–page PDF you can drop straight into your investor deck
• simple fixes you can implement in under 48 hours
All done for you without slowing you down
Ideal if you’re:
• Pre-seed or seed
• Preparing for demo day, a raise, or a partner call
• Shipping fast with no security hire
• Silently worried something might leak at the wrong time
If your product is investor-facing soon and you’re not 100 percent confident in its security story, drop what you’re building below or DM me.
I’ll choose the 2 that fit best.
This is a really practical angle — most founders focus so much on features and polish that demo-safe behavior (like hiding prompts, internal endpoints, or config leaks) gets overlooked until it’s too late. Even small leaks in logs or UI flashes can really shake investor confidence.
One thing I’ve seen help teams tighten this early is pre-demo checklist automation — automatic scans of environment variables, prompt leaks, and public endpoints as part of every commit build before staging. Curious — beyond the red-flag scan and stress test, do you recommend teams integrate any continuous checks into their CI/CD pipelines so they don’t have to think about it manually?
I developed Truecheckia to detect images and texts created by AI. In the world we live in, all information can be produced simply by a prompt. We want to separate these worlds and possibly put a label on them. We try to make the AI learn from various standard interactions. We could also verify all authenticity and visually display the worthy content on a public and auditable blockchain. We would have API resources and extensions for users to verify whether videos on social networks are genuine or made by AI.
I'm in - prepping for investor demos.
Built an intelligent web scraping system powered by LLMs and knowledge bases. It
autonomously finds data sources, adapts to site changes, and validates output.
Main concern: During demos, the LLM makes autonomous decisions about what/where to
scrape. Want to make sure it doesn't leak prompts, endpoints, or internal logic when
I'm showing investors how it works.
Exactly the "something leaks at the wrong time" scenario you described.
Pre-seed, p but have revenue. moving fast, no security hire. This would be perfect timing.
Please send me a mail: @cyberdammiy@gmail.com
Awesome Hackcraft, thanks for jumping in. Apologies for the late reply.
Your use case (autonomous LLM scraping + investor demo prep) is exactly the scenario I designed this sprint for. I’ll DM you so we can set up a quick kickoff.
Solid offer a lot of AI founders underestimate how one small leak during a demo can destroy investor confidence
I work with early stage SaaS and AI teams on Reddit, helping them position what they do clearly, attract real users, and communicate their value without sounding spammy or risky. Your demo safe angle is exactly the kind of problem founders in my space are stressing about
If you’re open, I can share a few Reddit growth frameworks that help founders turn posts like this into warm leads and investor-ready visibility. It could complement the security work you’re doing and bring you more qualified founders fast
Happy to collaborate or share ideas feel free to DM me
Love this—demo-day leaks can kill a round fast. I’m a Flask dev who just open-sourced a booking portal; ran a quick grep -R SYSTEM_PROMPT scan on my own repo after reading your checklist and caught a leftover debug print. Curious: do you scan Docker layers as well, or stick to live endpoints?
oh that made me smile a bit, funny how those leftover prints never show up until the worst moment.
And yeah, I don’t limit myself to live endpoints.
For early-stage teams, half the leaks hide in places nobody expects.
so I usually do a quick layered sweep:
• docker layers for cached env vars and baked-in creds
• image history for accidental secrets
• container metadata (people forget how much ends up there)
• then the live endpoints and LLM surfaces last, since that’s what investors see
Happy to take a look at your repo or the deployment setup.
Please send me a mail: @cyberdammiy@gmail.com