Engineering Case Studies

Real production systems, presented by the engineers who built them — architecture, tech stack and verified impact.

Engineering Case Studies

Meet the engineers behind the systems

Each engineer walks through a real production build — the problem, the architecture, and the measured impact.

🇮🇪Denis · Automation & OSINT Engineer · Ireland
AI OSINTEnterprise Risk ManagementRegulatory Automation

Autonomous OSINT Due Diligence — Counterparty & Company Audit

The Challenge

Corporate legal and procurement departments waste dozens of manual hours cross-referencing fragmented government registers, court databases, and corporate ownership filings. Manual verification creates severe operational bottlenecks and exposes organizations to shell companies, undisclosed liabilities, and toxic counterparties.

Solution & Architecture

  1. Parallel Ingestion: Automated scrapers query 10+ public and statutory company registries concurrently.
  2. Entity Resolution: Python scripts normalize registry data, track ownership trees, and reconcile discrepancies.
  3. Semantic Risk Scoring: LLM evaluation (Claude 3.5 Sonnet) analyzes filings for red flags, political exposure, and insolvency signals.
  4. Sandboxed Delivery: Dynamic generation of an interactive HTML report rendered in a sandboxed iframe.

Key Metrics & Impact

< 25s
audit latency (from 2-3 hours)
10+
registries queried
100%
sandboxed execution

Tech Stack

  • n8n
  • Python 3.11
  • OpenRouter API
  • Claude 3.5 Sonnet
  • Tailwind CSS
  • Sandboxed Iframe
Full video transcript

Hey, I’m Denis. I build automated workflows and autonomous AI agents from Ireland. Before closing deals, corporate legal and finance teams waste hours manually digging through fragmented public registers, risking contracts with shell companies. We automated this entire background check. Using n8n, Python, and Claude, our pipeline scrapes official registries, runs entity resolution, and generates semantic risk scores on the fly. Over 10 registries audited in under 25 seconds, rendered inside a sandboxed security report. Check the workflow architecture below.

🇵🇱Igor · Full-Stack & SaaS Architect · Poland
Enterprise B2B SaaSProfessional Services Automation (PSA)

Chill CRM — Multi-Tenant Agency PSA & Profitability Platform

Open live product →

The Challenge

Digital agencies routinely lose up to 25% of their billable revenue. Disjointed timers, untracked out-of-scope tasks, and delayed invoicing erode project margins and burden project managers with manual administrative tasks.

Solution & Architecture

  1. Multi-Tenant Schema Isolation: Dedicated PostgreSQL schema per tenant, guaranteeing zero cross-customer data leakage and strict GDPR compliance.
  2. Automated Financial Operations: Real-time retainer burn forecasting, hierarchical rate cards, and direct Stripe Billing integration.
  3. External Spend Synchronization: Live bi-directional integration with major ad-platform APIs (Meta, Google Ads).
  4. Global Readiness: Native multi-language localization (EN, UK, RU, PL).

Key Metrics & Impact

+28%
agency margin increase
15+ hrs/week
saved on PM
EU / US
live in production

Tech Stack

  • Next.js 15
  • TypeScript
  • Node.js REST
  • PostgreSQL (Schema Isolation)
  • Stripe Billing
  • Ad-Platform APIs
Full video transcript

Hi, I’m Igor. I build scalable SaaS architectures and web platforms. Digital agencies forfeit up to 25% of their billable revenue through scattered spreadsheets, unlogged overtime, and delayed manual invoicing. To plug this leak, we engineered Chill CRM — a multi-tenant PSA platform with strict schema isolation, real-time ad-platform sync, and automated Stripe billing. Clients boosted their profit margins by 28% and saved 15 hours of weekly project management overhead. Test the live platform at chill-crm.com or explore the stack below.

🇩🇪Valera · AI Systems & Backend Engineer · Germany
EdTech AIEnterprise Agent OrchestrationClean Architecture

prismOS — Adaptive AI Teacher Copilot with On-Premise Isolation

The Challenge

Teachers spend up to 65% of their work week on administrative overhead: drafting bespoke test materials, manual grading, and tailoring lesson plans. Using consumer AI raises severe data-privacy violations and exposes schools to unvalidated hallucinations.

Solution & Architecture

  1. Hybrid Multi-Model Orchestration: Intelligent routing between Gemini 2.5 Pro (complex reasoning) and self-hosted local Qwen 2.5 (sensitive student PII).
  2. Strict Data Privacy: Complete isolation of student PII, meeting German DSGVO / GDPR requirements.
  3. Low-Latency Streaming: Bi-directional WebSocket channels for instant token streaming.
  4. Test-Driven Reliability: 157 pytest integration test cases enforcing schema validation via Pydantic.

Key Metrics & Impact

up to 70%
teacher hours saved
157
automated pytest cases
100%
DSGVO-compliant local isolation

Tech Stack

  • Python 3.12
  • FastAPI
  • Pydantic V2
  • Gemini 2.5 Pro
  • Local Qwen 2.5
  • WebSockets
  • Docker
  • Pytest
Full video transcript

Hi, I’m Valera. I engineer AI architectures and backend systems from Germany. Educators waste up to 65% of their time manually creating exercises, grading homework, and personalizing curricula. We engineered prismOS — an adaptive copilot built on Python 3.12 and FastAPI. It orchestrates Gemini 2.5 Pro with a self-hosted local Qwen model, ensuring zero data leakage for student privacy. Powered by WebSocket streaming and validated by 157 automated tests, it saves teachers up to 70% of prep time. Deep-dive into the architecture and test suite below.