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Case study · Protego

How we built the SP-API data platform behind Protego's 5-minute risk scores

We were engaged to architect and build the complete backend data infrastructure behind Protego: SP-API integration, report ingestion, risk-signal normalization and the scoring pipeline.

  • Amazon marketplace · SaaS · Enforcement risk
  • Ongoing partnership
  • Amazon Seller Central
  • Amazon SP-API
  • AWS
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Results

What changed, in numbers.

30+

Seller accounts connected and analyzed securely via SP-API.

20+

Enforcement risk signals tracked and normalized across Amazon reports.

5 min

Typical delay between signal ingestion and risk score update.

Inside the build

Screens, flows and designs from the build.

6 images from the Protego project. Select any image to see it larger.

Protego enforcement risk dashboard on a laptop
Protego risk dashboardSelect an image to open the viewer, then use the arrow keys or swipe to move through all 6 images.
Protego user flow diagram
User flow diagramWe designed a production-ready data pipeline to securely ingest, clean, and structure Amazon SP-API data across multiple report types and endpoints.
Protego risk score dashboard next to the sign-up screen
Enforcement risk scoreOn top of this data foundation, Protego surfaces enforcement risk through a unified dashboard that highlights key signals, risk drivers, and historical context.
Protego risk score, risk trend and top risk drivers on a laptop
Operations intelligence dashboard
High-fidelity Protego screens
High-fidelity design
Protego risk score history with trend chart and pre-emptive actions
Risk score history

About the project

About Protego.

Protego is an Amazon-focused SaaS platform that detects enforcement risk before it leads to account suspensions or revenue loss.

The platform connects directly with Amazon Seller Central via SP-API and continuously analyzes operational, catalog, and order-level signals to generate a unified enforcement risk score.

The backend had to securely ingest, normalize, and analyze high-volume Amazon data while remaining scalable, reliable, and compliant with Amazon's API constraints.

Client
Protego
Industry
Amazon marketplace · SaaS · Enforcement risk
Engagement
Ongoing partnership
Services
Amazon SP-API, ETL pipelines, Data engineering, Risk analytics, AWS

The challenge

  • Amazon enforcement risk signals are fragmented across multiple Seller Central reports and APIs, making it nearly impossible for sellers or tools to identify risk early.
  • Key challenges included no single source of truth for enforcement-related activity, SP-API rate limits and throttling, complex report workflows, need for near real-time alerts without false positives, secure handling of OAuth tokens at scale, and designing a system that could grow from early adoption to production-scale SaaS usage.
  • Any delay or inconsistency in data could mean missed alerts, late reactions, and irreversible account actions.

What we built

We designed and delivered a production-grade Amazon ETL and analytics backbone on AWS, purpose-built for scale and reliability.

  • Secure SP-API OAuth integration with encrypted token storage and refresh handling
  • Scheduled and event-driven ingestion pipelines for Orders, Listings, Catalog, and Reports APIs
  • Full Reports API workflow with retry, throttling, and idempotency safeguards
  • Event normalization layer to convert raw API responses into structured enforcement signals
  • Rules-based risk engine to compute deterministic risk scores with historical tracking
  • AI-assisted explanation layer to translate raw signals into clear, actionable insights
  • Scalable AWS architecture using Lambda, SQS, S3, PostgreSQL, EventBridge, and IAM best practices

The result

Protego now operates on a robust data infrastructure that can grow with customer demand without compromising accuracy, performance, or compliance.

Architecture

From the source data to the system on top.

Sources

  • Amazon Seller Central
  • Amazon SP-API
  • AWSAWS
  1. 01Amazon SP-API
  2. 02AWS Lambda
  3. 03SQS
  4. 04S3
  5. 05PostgreSQL
  6. 06EventBridge
  7. 07ETL pipelines
  8. 08Risk engine

The difference

Before and after.

Before

  • Enforcement signals fragmented across Seller Central reports and APIs
  • No single source of truth for enforcement activity
  • SP-API rate limits and throttling
  • OAuth tokens to handle securely at scale

After

  • 20+ enforcement risk signals tracked and normalized
  • Risk scores typically updated within 5 minutes of a signal
  • 30+ seller accounts connected securely via SP-API
  • Infrastructure that grows with demand without compromising accuracy or compliance

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