We build Day 1-ready infrastructure with automated testing, documentation, and CI/CD pipelines from the start.
We do not force a single tech stack. We adapt our architecture based on your business stage—whether you need to prove a concept fast or secure enterprise data.
| Feature | 🏁 The 60-Day MVP | 🏰 The Enterprise Scale |
|---|---|---|
| Objective | Validate market fit instantly | Reduce OpEx, ensure compliance, and own the data |
| AI Layer | API-First (OpenAI / Claude) | Hybrid AI: Self-Hosted + Private API Models |
| Infrastructure | Serverless / Zero DevOps (Cloud Run) | Containerized workloads on managed K8s |
| Data Engine | In-Memory Analysis (DuckDB) | Distributed Analytics (ClickHouse / Data Warehouse) |
| Architecture | Fast Monoliths (FastAPI) | Modular Services with Fault Isolation |
We solve your business problem first — then we code. Our process prevents budget waste by validating feasibility before any development begins.
- Discovery & Feasibility: We stress-test the idea against technical and budget constraints to ensure it’s truly viable.
- Stack Selection: We choose pragmatic tools that fit your long-term goals, avoiding unnecessary complexity and costly overengineering.
- Deliverable: A validated architectural roadmap and technical execution plan.
- Engineering: Development of a “Working Alpha” using Python or Go, focused on core functionality and real-world usability.
- Infrastructure: Setup of secure development and staging environments with baseline security best practices from day one.
- Deliverable: A functional system ready for internal testing and iteration.
- Testing & Deployment: Structured testing, bug fixing, and production deployment to ensure production readiness for MVP scope.
- Handover: Full IP transfer. You own the codebase, infrastructure access, and documentation.
- Deliverable: A market-ready MVP you fully control.
We use battle-tested technologies to ensure stability.
- Languages: Python (FastAPI), TypeScript, Go, SQL.
- AI Orchestration: LangChain, Custom RAG Pipelines, Local LLM Inference.
- Database: PostgreSQL (pgvector), Redis, DuckDB, ClickHouse.
- Cloud: AWS, Google Cloud Run, Docker, Kubernetes, Terraform.
SiftScore - AI Recruitment Platform
- Challenge: Analysts were bottlenecked by manually validating millions of data points.
- Architecture: We built a pgvector backend on Azure to enable natural language querying of massive datasets.
- Outcome: Weeks of manual research compressed into seconds with instant, ranked target lists.
Nostrada AI - Political Intelligence
- Challenge: Processing thousands of hours of speeches and voting records is impossible for humans.
- Architecture: A real-time RAG pipeline coordinating 650 autonomous agents to process live web data.
- Outcome: A unified intelligence tool that predicts outcomes from fragmented public records.
Säker Canine - E-Commerce Agent
- Challenge: Standard LLMs hallucinate numbers, which is unacceptable for safety equipment sizing.
- Architecture: A Hybrid Architecture combining conversational AI with a strict JSON logic engine for factual data retrieval.
- Outcome: Zero-hallucination recommendations that strictly follow business rules.
Headquartered in Belgrade, Serbia, with leadership in the US. We offer native/fluent support in:
🇺🇸 English • 🇷🇺 Russian • 🇷🇸 Serbian • 🇺🇦 Ukrainian • 🇪🇸 Spanish
Visit one of our profiles and get a feasibility audit in 12 hours.