SignalBoard

A lightweight feedback triage tool that turns messy seller feedback into structured insights and flags high-risk issues for review.

Built as a minimal proof-of-concept demonstrating structured AI-assisted decision support tool for commerce platforms and feedback triage with human-in-the-loop safeguards.

Why This Exists

Sellers receive feedback everywhere:

• Reviews
• Support tickets
• Refund notes

The problem isn’t reading feedback.
It’s knowing what matters.

SignalBoard extracts structured friction signals from unstructured text, scores confidence, and routes uncertain or high-risk cases to human review.

It separates noise from operational risk.

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What It Does

1. Accepts pasted feedback (reviews, tickets, refund notes)
2. Uses DeepSeek (OpenAI-compatible API) to extract structured signals
3. Applies deterministic review rules locally
4. Aggregates results into a visual summary:
   • Top issue
   • High churn risk percentage
   • Payment-related flags
   • Human review required percentage
5. Displays detailed per-item insights with confidence scoring

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System Design

AI Layer

• Extracts structured JSON signals from messy text
• Returns:
  • friction_type
  • sentiment_score
  • churn_risk
  • payment_issue
  • chargeback_risk
  • confidence_score

Deterministic Rules Layer

After model output, the system:

• Flags low-confidence outputs
• Escalates payment-related negative sentiment
• Routes medium/high chargeback risk
• Handles malformed responses safely

The model assists.
The system decides.

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Trust & Safety Considerations

• LLM outputs are probabilistic.
• Confidence scoring is required.
• Payment-related issues are treated conservatively.
• Low-confidence predictions trigger human review.
• The UI never crashes on malformed model output.

This design assumes AI is an assistant, not an authority.

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Tech Stack

• Next.js (App Router)
• TypeScript
• Tailwind CSS (light/dark mode)
• DeepSeek API (OpenAI-compatible)
• Vercel (deployment)

Deployment

Deployed on Vercel using a server-side API route to protect API keys.

Environment variables are configured in the Vercel dashboard.

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Product Tradeoffs

• Batch processing vs real-time streaming
• False positives vs false negatives in payment flagging
• Strict JSON enforcement vs model flexibility
• Automation vs human oversight

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Future Directions (Not Included in MVP)

The current version is intentionally constrained to a single-page proof-of-concept demonstrating core signal extraction and triage logic.

• Trend detection over time
• Seller dashboard mode
• Integration with ticketing systems
• Risk prioritization scoring
• Model comparison or ensemble scoring

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Why This Matters

Commerce platforms operate at scale.

Surfacing the right signals early helps:

• Reduce churn
• Prevent chargebacks
• Improve checkout experience
• Protect seller trust

This project demonstrates AI-native product thinking:

• Structured outputs
• Guardrails around model uncertainty
• Human-in-the-loop design
• Clear separation between AI extraction and business logic