nasaq.id

Case studies

Proof from real builds, with the business context still visible.

This page is not a gallery of random screenshots. It is a closer look at what was weak before, what changed in the system, and why the output mattered to the team or the business after launch.

7

production systems

7

live demos available

7

documented case studies

22+

before/after proof points

2024-2026

shipping window

Business context first

Each project is framed around the operational or conversion problem, not just the tech stack.

Before versus after

The strongest proof is not visual polish alone, but the shift in speed, clarity, or workflow friction.

Real usage matters

These are meant to show what changed when the build was actually used in day-to-day work.

Featured proof

A case study worth scanning first

Start here if you want the fastest picture of how nasaq.id frames operational pain, turns it into a system, and then measures the change.

Read full case study
Serat QC — Selisih Berat J&T Express
Operations proof

Serat QC — Selisih Berat J&T Express

A clear example of how a repetitive field workflow was turned into a much faster, auditable system.

Challenge

Tim operasional J&T Express harus memverifikasi selisih berat paket setiap hari. Setiap resi membutuhkan 2 foto bukti yang harus di-rename manual satu per satu dengan format tertentu (nomor resi + keterangan). Rata-rata 500 resi dan 1.000 foto per hari.

Waktu per 500 resi

Before

4-5 jam

After

< 30 menit

Rename foto

Before

Manual 1 per 1

After

Otomatis via barcode scan

Total data diproses

Before

Scattered di folder lokal

After

80.187 resi & 160.374 foto terpusat

Live operational QC system untuk J&T Express80.187 entries & 160.374 photos in production

Browse each case by the kind of problem it solved

Each card below keeps the business problem visible, so the page reads more like proof and less like a portfolio catalog.

Qohira — E-Commerce Manual Payment
Commerce workflow2026production

Qohira — E-Commerce Manual Payment

Production e-commerce untuk client nyata — manual payment verification, real-time inventory, admin dashboard. 16 produk, 6 orders, 5 users aktif.

Problem seen on the ground

Client butuh toko online dengan alur pembayaran manual (transfer bank) dan verifikasi oleh admin sebelum pesanan diproses

What changed

E-commerce platform dengan manual payment verification flow — customer upload bukti transfer, admin konfirmasi via dashboard, notifikasi otomatis via email & web push

Kelola produk & pesanan

BeforeChat WA manual, data tersebar
AfterDashboard terpusat — produk, pesanan, stok

Verifikasi pembayaran

BeforeCek rekening manual via WA
AfterCustomer upload bukti → admin verifikasi di dashboard
Next.jstRPCPrismaPostgreSQL
Visit liveOpen full case study
Serat QC — Selisih Berat J&T Express
Operations proof2024production

Serat QC — Selisih Berat J&T Express

Production logistics QC untuk J&T Express — 80K+ resi, 160K+ foto GPS-watermarked. Waktu proses: 4-5 jam → <30 menit per 500 resi.

Problem seen on the ground

Tim operasional J&T Express harus memverifikasi selisih berat paket setiap hari. Setiap resi membutuhkan 2 foto bukti yang harus di-rename manual satu per satu dengan format tertentu (nomor resi + keterangan). Rata-rata 500 resi dan 1.000 foto per hari.

What changed

Dibangun sistem web-based (PWA) dimana user cukup scan barcode resi, ambil foto langsung dari kamera — sistem otomatis memberi nama file, menambahkan GPS watermark (tanggal, waktu, koordinat, lokasi), dan upload ke cloud. Semua data langsung masuk dashboard real-time.

Waktu per 500 resi

Before4-5 jam
After< 30 menit

Rename foto

BeforeManual 1 per 1
AfterOtomatis via barcode scan
Next.jsTypeScriptSupabaseGPS
Visit liveOpen full case study
SignalFlow Agent — AI Trading Signal Dashboard
System build2026production

SignalFlow Agent — AI Trading Signal Dashboard

AI trading signal dashboard — 5-Layer Signal Engine V2, multi-timeframe confluence, paper futures trading. SoSoValue Buildathon 2026 submission.

Problem seen on the ground

Trader retail kesulitan mengintegrasikan data dari banyak sumber (ETF flows, sentiment, macro events, technical indicators) menjadi satu keputusan trading yang koheren. Platform existing hanya chart viewer tanpa signal classification atau automated execution.

What changed

5-Layer Signal Engine V2 yang menggabungkan Trend, Momentum, Volatility, Volume, dan Structure analysis dengan market regime detection. Setiap signal dianalisis di 3 timeframe (1H, 4H, 1D) dengan alignment scoring. Paper futures trading dengan virtual USDC, auto TP/SL/liquidation, dan per-type performance stats.

Signal analysis

BeforeManual cek 5+ indikator di berbagai platform
AfterAuto 5-factor confluence + 3-timeframe alignment

Trade execution

BeforeManual open/close, rawan emosi
AfterPaper futures dengan auto TP/SL/liquidation
Next.jsTypeScriptPrismaPostgreSQL
Visit liveOpen full case study
ShadowBid — Encrypted Sealed-Bid Auction (FHE)
System build2026production

ShadowBid — Encrypted Sealed-Bid Auction (FHE)

On-chain sealed-bid auction dengan Fully Homomorphic Encryption (FHE) — bid tetap terenkripsi, smart contract compute winner tanpa decrypt. 47 tests.

Problem seen on the ground

On-chain auctions biasa (English, Dutch, Sealed-bid) semuanya vulnerable: bid visible di mempool, MEV bots bisa snipe, dan price manipulation mudah dilakukan. Tidak ada privacy untuk peserta auction.

What changed

Fully Homomorphic Encryption (FHE) memungkinkan smart contract compute pada encrypted data. Bid di-encrypt di browser via Fhenix CoFHE SDK, disimpan on-chain sebagai euint64. Smart contract bandingkan encrypted bids via CMUX operations — tidak pernah decrypt individual bids. Hanya winning bid yang di-reveal setelah finalization via Threshold Network.

Bid privacy

BeforeVisible di mempool — MEV extractable
AfterFHE encrypted — zero knowledge sampai settlement

Winner selection

BeforeOn-chain comparison (plaintext)
AfterCMUX on encrypted bids — never decrypts losers
ReactSolidityFHEFhenix
Visit liveOpen full case study
TraceFlow — Real-Time GPS Fleet Tracking
System build2026production

TraceFlow — Real-Time GPS Fleet Tracking

GPS fleet management dashboard dengan real-time tracking, geofencing, multi-provider GPS integration, dan comprehensive reporting — untuk perusahaan logistik dan transportasi

Problem seen on the ground

Perusahaan logistik kesulitan memantau armada kendaraan secara real-time. Data GPS tersebar di berbagai platform vendor, tidak ada dashboard terpusat untuk monitoring, geofencing, dan reporting.

What changed

Full-stack fleet management dashboard dengan real-time tracking via Socket.IO, multi-provider GPS webhook integration, geofencing engine, dan automated reporting system.

Monitoring armada

BeforeManual via WhatsApp/telepon ke supir
AfterReal-time dashboard dengan posisi live di peta

Geofencing

BeforeTidak ada — tidak tahu kendaraan masuk/keluar zona
AfterAuto-alert 9 tipe: speeding, geofence, SOS, ignition, dll
Next.jstRPCPrismaSocket.IO
Visit liveOpen full case study

If one of these problems feels familiar, the next step is not guessing.

Bring the current bottleneck, the team reality, and the target outcome. The first job is to narrow the right scope, not to sell the largest build by default.