InsightFlow

Turn raw customer feedback into decisions: what to fix first, and why.

Live app: https://insightflow-blond.vercel.app Built for Build Battle 2026 (Frontend Arena, AI × Web Development).

What it does

Requirement	Where it lives
Feedback management: submit, import CSV, search, filter, sample data	New session popup (upload CSV, paste, add one), /feedback, "Use sample data" on /import. Each import can be its own session with its own dashboard, tracker and reports
AI-powered analysis: sentiment, topics, grouping, Adaption	Issue groups and sentiment for all 320 distinct reviews; a key-free matcher for new feedback; Adaption cleaning on /import
Analytics dashboard: metrics, sentiment, issues, trends, filters	/overview: category, sentiment and period filters drive every widget
Recommendations: priorities with a reason	/priorities: ranked issues, the score arithmetic, quotes, and actions
Reports and tracking: save/track, export	/tracker (drag cards between columns), /reports (print to PDF, CSV export, copy summary to share in chat or email, saved reports you can reopen and delete)

The dataset, and why cleaning comes first

The sample file has 7,000 reviews but only 320 distinct ones. A few sentences repeat hundreds of times, so treating rows as customers gives the wrong ranking.

• Every distinct review keeps its original repeat count, so a complaint said 295 times weighs 295.
• The file was run through Adaption (Prompt Deduplication + Checklist verification). Quality went 2.0 → 9.6 out of 10 (grade E → A, percentile 0.6 → 57.7), and 7,223 rows became 441. See docs/ADAPTION.md for the full explanation, including where Adaption fell short.

How priorities are decided

score = 100 × (0.40 × volume + 0.35 × negative share + 0.25 × severity)

• volume: negative reviews for the issue ÷ the most-reported issue, using original repeat counts
• negative share: how one-sided the feedback on that issue is
• severity: set by hand (money loss and fraud rank highest, inconvenience lowest)

It is a ranking heuristic, not a measurement, and the app shows the arithmetic for every issue.

What is real, and what is not

Being plain about this matters more than sounding impressive.

• No model runs in the browser or on the server at request time. The deployed app needs no API keys.
• Sample labels (sentiment, issue group, severity) come from scripts/label.mjs: rules written after reading all 320 distinct reviews. I cross-checked them with a live language model (DeepSeek, scripts/validate-llm.mjs): sentiment agreed on 318 of 320 reviews (99%). The model used finer issue names (74 vs my 21), so the curated groups are kept for stability. The model output is committed in data/llm-labels.json.
• New feedback is matched against the 320 labeled reviews with TF-IDF similarity (src/lib/match.ts), with no key. Leave-one-out accuracy on the sample: 88% on issue, 96% on sentiment. When nothing is close it says so, and those reviews are grouped with each other by similarity into new issues (src/lib/group.ts). Those groups are named after their most-repeated review and get a severity from keywords (money and fraud words rank highest), so any dataset gets a ranking. The names are rough, not written by a model.
• Trends are simulated. The sample has no dates, so the weekly timeline is a seeded random spread across 13 weeks, and the chart is labelled "Demo timeline (simulated)".
• src/lib/analyze.ts has a server-side model labeling function (AI SDK + DeepSeek, schema-validated) that is not wired into the UI.
• Data you add, track or save stays in your browser (localStorage). Nothing is sent anywhere. The avatar menu (top right) clears the current session or resets everything, so the app can be reset between demos.
• There is no shareable link: reports are shared by copying the summary, printing to PDF or exporting CSV, because a link would need a server to store the report.

Details worth knowing

• Large files and removing the sample: CSVs up to 100 MB are read in the browser in chunks, keeping only the review and category columns and merging repeats as it goes, so a 150,000-row file is fine. A session stores up to 3,000 distinct reviews (browser storage is about 5 MB), starting with the most repeated, and the pre-flight check says so when a file has more. The sample dataset is built into the app, so you can remove it from your sessions and bring it back with one click; nothing is lost.
• Duplicate protection: uploading the same file again is detected ("320 of 320 distinct reviews are already in your dashboard") and skipped, so counts never double.
• Personal data: emails, phone numbers and card-like numbers are masked as [email], [phone], [card] before anything is stored.
• Honest limits: auto-grouped issues have plain names (a representative review, not a written label) and keyword-based severity, so check them before acting. Imported data has no dates, so only the sample shows a (simulated) weekly timeline.

First visit. The root URL opens a short welcome: real reviews on a board that you tap to sort into their issue, and the path from 7,000 reviews to one decision. After you continue once, the root URL goes straight to the dashboard. The welcome can be reopened from the avatar menu.

Run it

npm install
npm run dev          # http://localhost:3000
npm run build && npm start

No environment variables are required. To re-run the offline model cross-check, put DEEPSEEK_API_KEY in .env.local and run node --env-file=.env.local scripts/validate-llm.mjs.

Tests

A browser test walks the full flow (filters, tracking, drag and drop, search, CSV export, saved reports, the import popup, personal-data flagging, duplicate protection):

npm start                                  # in one terminal
node scripts/smoke.mjs                     # or: BASE=https://insightflow-blond.vercel.app node scripts/smoke.mjs

A second test covers the phone layout (bottom bar, bottom-sheet popup, card lists, swipeable rows, no sideways scroll at 320 to 414px wide):

node scripts/phone.mjs                     # or: BASE=https://insightflow-blond.vercel.app node scripts/phone.mjs

Stack

Next.js 16 (App Router, TypeScript), Tailwind CSS 4, Recharts, PapaParse, zod, Playwright (tests). Deployed on Vercel.

Repository map

src/app/           pages: overview, feedback, priorities, tracker, reports, import
src/components/    shell, import popup, filters, shared UI
src/lib/           data layer, store, matcher, scan, Adaption results
scripts/           dedupe → label → build-analysis pipeline, Adaption summary, tests
data/              sample.csv, analyzed.json, Adaption summary + trimmed output
docs/              ADAPTION.md, DEMO.md, screens/, planning/ (early notes)

Data pipeline: node scripts/dedupe.mjs && node scripts/label.mjs && node scripts/build-analysis.mjs regenerates data/analyzed.json from data/sample.csv.