Lenny Daume
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QYRAGY GmbH Logo

QYRAGY GmbH

100% Remote / Hannover

Hands-on CTO & Chief Product Owner for AI Matching Platform

Hands-on Chief Technology Officer & Chief Product Owner

July 2020 - April 2023
2 years 11 months
Full-time
Product work
100% Remote / Hannover

Relevance

Why this case matters

The case shows how problem understanding, responsibility, decisions, and implementation came together in this context.

Impact

Built an AI-powered matching platform from 0 to 1 and combined product, technology, and team design inside an early-stage startup.

What carries forward today

AI matching was part of the product model early and stayed connected to market learning, data logic, delivery, and human decisions.

0→1AI matchingProduct ownershipProduct + techEnd-to-end deliveryPlatform operationsStartup buildCypress

Defensible proof

0→1

platform built

Connected product model, market validation, team, delivery, and operations inside an early-stage startup.

AI matching

k-Means + NLP

PWA + AWS

scalable base

Where this experience creates value

  • For early product teams that need market learning, product model, AI logic, delivery, and operations connected in one responsibility.
  • For marketplace and SaaS contexts where web, apps, data, and cloud services need to work as one product.

Case context

Overview

Smartjobr started as an AI-powered freelancer matching platform that had to move from first product model to real delivery and operations. I connected matching logic, technical architecture, team building, and platform work in an early-stage startup with fast learning loops, product-market fit work, and direct delivery pressure. Its database-centered backend contained about 100 PL/pgSQL function files, 58 Flyway migrations, and 87 SQL tests. Within that system, I personally worked on schema changes, functions, tests, statistics and recommendation logic, and release fixes.

QYRAGY covered two customer-facing web products: the company website with slug-specific landing pages and marketing data, and Smartjobr as the marketplace. Smartjobr used k-means and NLP to match freelancers with projects and shipped through web, Android, and iOS applications. Those clients belong to one product. Next.js/PWA, AWS infrastructure, and automated BI dashboards connected product usage, technical foundation, and data-informed decisions.

Responsibility

Activities

  • Hands-on startup CTO & CPO: Product strategy, technical direction, and team building across engineering, design, and marketing
  • QYRAGY website: Company website with landing pages for different slugs and associated marketing data
  • AI matching platform: k-Means algorithm, NLP, intelligent freelancer-project matching
  • Next.js/PWA development: Cross-platform user experience across web, Android, and iOS applications; migrated the native Android app to a React-based PWA while keeping distribution through the existing Play Store listing
  • AWS infrastructure: Lambda, S3, RDS, SQS, CloudFront for scalable architecture
  • Database logic: an established PostgreSQL/PL/pgSQL system with about 100 function files, 58 Flyway migrations, and 87 SQL tests under my CTO/CPO responsibility; personally worked on schema changes, functions, tests, statistics and recommendation logic, and releases
  • Platform operations: CI/CD, Cypress tests, monitoring, and product-close operation of the matching platform
  • Test-first product development: frequently used Cypress as the working browser to specify, run, and debug matching, PWA, and platform behavior directly while building
  • BI & automation: Dashboard development, Slack bot, automated KPI reporting
  • Product strategy: Customer feedback, user experience, and learning loops
  • Security & privacy: Data protection compliance, secure infrastructure monitoring
  • Team leadership: Agile development, cross-functional collaboration, technology selection

Operating mode

Methodology

  • Agile development: Scrum, Kanban, cross-functional teams
  • Product management: Customer feedback, user experience, market validation
  • Startup methodology: Rapid iteration, MVP-first, product-market fit
  • AI/ML integration: Data-driven matching, algorithm optimization
  • DevOps: AWS infrastructure, automated deployments, Cypress as a working and test browser, monitoring, and operations as part of product ownership

Technical context

Technology stack

The tools are not the point by themselves. What matters is which system layers had to work together.

10Areas
49Technologies

DevOps

6
GitDocker SwarmAWS LambdaAWS CloudFrontMonitoringPlatform Operations

Databases & Storage

7
ArangoDBPostgreSQLPostgreSQL FunctionsPL/pgSQLFlywayAWS S3AWS RDS

Frontend

7
PWA CachingTailwind CSSNext.jsReactTypeScriptJavaScriptPWA

Messaging & Event Streaming

2
AWS SNSAWS SQS

Mobile

5
Apple Push NotificationsAndroid Push NotificationsWeb Push NotificationsAndroid SDKiOS

Practices

6
Test-Driven DevelopmentAgile TestingProduct OwnershipProduct StrategyAgile LeadershipEnd-to-End Delivery

Backend

4
Node.jsREST APIsPythonFirebase

Data & AI

4
Natural Language Processing (NLP)Machine LearningConversion Analyticsk-Means algorithm

Tools

6
pgTAPCypressSlackJiran8nConfluence

CI/CD & Delivery Pipelines

2
Drone CICI/CD Pipeline

Next step

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