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★ 45.0 / 100 • Rank #51 Originality: Incremental Improvement Judge: Discard AI Optional Medium Sustainable and Connected PH 30-Hour Build Plan

SibuyasSight: Bakit ₱200 pa rin ang sibuyas?

#Open Data #Agriculture #Price Transparency #Charts
Executive Elevator Pitch

"SibuyasSight traces the onion and rice price you pay back to farmgate, cold storage, and import timing — using PSA price situationers and DA bulletins — then explains in Tagalog who earned what. Built for the sibuyas shocks and rice swings that return every year."

Suggested Stack: Next.js + charts, FastAPI, LLM gap-explainer, PSA/DA scrapers, Postgres.
Strict Judge Evaluation — 0–100 (Innovation 25 / Fit 25 / AI 20 / Feasibility 15 / Impact 15) Methodology & rankings →
Innovation & Creativity
35
Problem–Solution Fit
55
AI Application & Value
30
Feasibility & Potential
60
Impact & Scalability
50

Weighted total ★ 45.0/100 — ranked #51 of 56. Concept-stage assessment only: no prototype has been built or tested. Scores are comparative, not official PLDT marks.

Problem Statement

Red onion swings ₱150-₱230/kg and rice ₱43-₱56/kg while farmers say farmgate never moved. Consumers blame vendors; vendors blame biyaheros; cold-storage hoarding hides in between. PSA publishes price situationers but nobody reads PDFs at the palengke.

Proposed Solution

Pick item + week: SibuyasSight charts retail vs farmgate vs import arrivals, flags gaps (“Retail +38% habang farmgate flat — check storage releases”), and writes a one-paragraph Tagalog explainer with sources. Vendors can show “patas ang patong ko” proof; coops can time releases.

Target Market

  • Primary users: Shoppers, vendors, youth agri reporters.
  • B2G: DA price teams, LGU market masters.

How AI Is Used

  • Gap detector: Time-series flags abnormal retail-farmgate spreads.
  • Explainer LM: Turns charts into plain-Tagalog “saan napunta ang patong” notes.

PLDT / Smart / ePLDT / Rokid Angle

  • Smart: SMS price query for palengke (“SIBUYAS QC” returns today’s range).
  • ePLDT: Hosts price archive for DA/LGU planning.

Demo Scenario (3 minutes for judges)

  1. Select red onion Jan 2026; chart shows retail spike while farmgate flat.
  2. Tagalog explainer appears with PSA citation.
  3. Vendor-proof view shows fair markup for a mock stall.
  4. SMS returns same range on basic phone.
  5. Close: “Hindi haka-haka ang presyo.”

30-Hour Build Plan

Hours Task Role
0–3 Pull PSA/DA samples, define items All
3–12 Gap detector + explainer AI lead
3–10 Charts + SMS Backend
10–20 Palengke-friendly UI Frontend
20–26 Explainability check All
26–30 Pitch, disclosure PM / Pitch

Suggested stack: Next.js + charts, FastAPI, LLM gap-explainer, Postgres. Public data / APIs: PSA price situationers, DA bulletins.

Risks & Privacy Notes

  • Market harm: Explains gaps, never accuses named traders. Demo data from public reports; no private trader data into AI tools.
  • IP: Public data paraphrased with citations.

AI Use Disclosure (draft)

Uses time-series detection plus LLM explainers. Coding assistants helped with charts. All demo claims cite public sources and were human-checked.

Rubric Scoring

Criterion Weight Score (1–5) Why
Innovation & creativity 25% 4 Farmgate-to-retail explainer in Tagalog is overdue
Problem–solution fit 25% 4 Yearly price-shock pain
AI application & value 20% 3 Detection + writing, modest AI
Feasibility 15% 5 Public data + charts
Impact & scalability 15% 4 Every palengke
Weighted total 4.20

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