Project Overview Statement¶

(Supersedes v1. Revised after advisory meeting with Davide (Tetra Pak / CBI) and the first round of validation, which showed the "DPP-as-marketing-asset" value proposition was not felt by consumers. The project pivots from brand-first to consumer-first.)

Problem/Opportunity People between 20 and 35 increasingly set goals about how they eat — getting fit, following a diet, eating more sustainably — but staying adherent to those goals is hard. Adherence is a well-documented problem in medicine, and it is just as real for food: labels are complex, intentions fade at the shelf, and good food gets thrown away at home. Meanwhile, EU regulation (ESPR, PPWR) is forcing a Digital Product Passport behind a QR code onto every product in Europe — an entire infrastructure of structured, trustworthy product data that consumers currently ignore because no one has made it personally useful.

We see the opportunity in connecting these two facts. The scan in the supermarket can become the moment where the data works for the person holding the product: does this fit what I'm trying to become? Will I actually use it before it expires? That is value people can feel in 15 seconds — not loyalty points.

Goal Launch PackyTrace as a personal food companion: scan a product and instantly know whether it fits your goals and profile, then manage what you bought so nothing is wasted. Within 6 months, prove that real consumers in our target range use it and come back — and that this consumer engagement produces anonymized insights brands will pay for.

Objectives 1. Define exactly who we serve and why we're unique: interview 20–30 people aged 20–30, and complete the RAMI analysis to identify what we build vs. integrate, and how we differ from existing apps (Yuka, MyFitnessPal). 2. Pitch at Startup Day (June 24) and use it for feedback, visibility, and the AlmaCube accelerator prize. 3. Ship a focused MVP — no gamification: consumer side, scan → personalized "does this fit me" guidance plus expiry/waste tracking; brand side, a deliberately simple dashboard of anonymized aggregates. 4. Get at least 50 real consumers (not friends) using it, with measurable return engagement. 5. Validate the B2B side second: confirm through interviews and at least one signed pilot/LOI that brands will pay for the insights our consumer engagement generates. 6. Apply for non-dilutive funding that lets us move from part-time to full-time.

Success Criteria By month 6, we will have: * At least 50 real consumers with accounts, ≥30% returning in week 2 — proof people use it and come back. * A validated, written unique value proposition for a defined target user (20–30), grounded in interviews and the RAMI analysis — proof we're not a clone. * At least one brand with a signed LOI or pilot at a real price point — proof someone will eventually pay. * Total spend under €500 — proof we can validate without burning capital. * Enough evidence to decide: do we go full-time, or do we stop?

Assumptions, Risks, Obstacles We're betting on three things being true: that diet/health adherence is a pain strong enough to change scanning behavior; that DPP/GS1 data gives us something existing apps can't easily replicate (batch-level, regulation-backed product data); and that consumer engagement converts into insight value brands will pay for.

The biggest risk is now consumer-side: if people in our target range don't feel the adherence problem the way we think they do, the thesis fails. That's why interviews with 20–30 year olds come before any new feature work — and why we killed gamification: trivial rewards don't drive behavior, real personal value does.

The second risk is differentiation. Yuka proves people will scan products spontaneously — which validates the behavior but also means we must offer something Yuka doesn't. The RAMI exercise exists to answer this in writing before we build further.

The third risk is us. We're a part-time team. The scope is planned around what part-time execution can achieve, but if signals are strong, the funding objective makes the full-time jump possible. Regulation timing (ESPR roll-out) is now a tailwind rather than a dependency: our consumer value works on today's data sources and gets stronger as DPP arrives.