Case studies

Backend and product case studies

Each case study focuses on business goals, constraints, and measurable outcomes. Backend engineering is presented as a means to deliver product impact.

B2B calculator, Autenti integration, and auto-validation for Useme Plus.

PythonDjangoDjango REST FrameworkPostgreSQLCelery

Business value: New revenue line based on a membership product, customer service automation, and faster user onboarding.

Outcome: Functional Useme Plus product with cost calculator, electronic document flow, and automated membership validation.

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New search engine and SEO tags deployed safely via feature flags.

PythonDjangoDjango REST FrameworkPostgreSQLCelery

Business value: Simplified job and freelancer search, better Google visibility through SEO tags, and safer deployments via feature flags.

Outcome: Rebuilt search engine with full filtering by categories, tags, and phrase, SEO tag system with CSV import, and secure deployment process.

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From MVP to a fully launched AI SaaS with online billing.

PythonDjangoDjango REST FrameworkPostgreSQLRedis

Business value: Fast launch of a monetizable digital service combining expert knowledge with personalized user experience.

Outcome: Operational SaaS product with a predictable revenue model and secure data handling.

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Automated ad monitoring and real-time Discord notifications.

PythonPlaywrightSQLitesystemdHTTP scraping

Business value: Faster response to relevant listing changes and lower operational load through event-based alerts.

Outcome: Near-continuous automation that catches new ads, sends ready emails, and provides full real-time operational oversight.

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Automated data pipeline for outbound scaling.

PythonDjangoPostgreSQLautomation workflowsintegrations

Business value: More qualified leads at lower operational cost and with higher data quality.

Outcome: Repeatable lead acquisition and handling process, ready for campaign volume scaling.

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API integrations and async jobs that streamline cross-system data flow.

PythonDjangoAPI integrationsasynchronous processingdata automation

Business value: Shorter process cycle time and fewer manual errors in business-critical workflows.

Outcome: Predictable operational workflows with clear reporting and data quality control.

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