Our core capabilities.
We build software end-to-end. Below are our main areas of expertise and how we approach them.
Websites & web platforms
What we do
Marketing sites, product surfaces, and web platforms that load fast, read clearly, and convert. Bilingual and multilingual builds, quote and booking flows, content and SEO structure, and the back end that keeps it all running.
How we approach it
We focus on the user journey and conversion: who’s visiting, what they need to decide, and the one action that matters. Then we build it lean, ship it on cost-effective infrastructure, and measure what changed.
Our tech stack
Astro · TypeScript · Next.js · Postgres · Cloudflare
Example: Çakı Lojistik, a bilingual logistics site built around fast quote capture →
Complex systems
What we do
Production back ends that have to stay correct under load: search products, data-heavy product surfaces, multi-tenant systems, and the pipelines that feed them. We work primarily in TypeScript, Go, and Python, with Postgres as the default.
How we approach it
We start by mapping real user requests from the edge to the database. We build in small increments, favoring clean architecture and reliable data constraints over unnecessary complexity.
Our tech stack
TypeScript / Node · Go · Python · Postgres · search indexes · queues
Example: İçtihatAra, a search product over Turkish case-law archives →
Mobile apps
What we do
Native iOS and Android apps, with a focus on products people return to: capture flows, journals, and tools that have to feel immediate. We build offline-first apps that sync seamlessly.
How we approach it
We write native Swift and Kotlin. We design robust offline-first synchronization, ensuring the app feels fast and responsive regardless of network conditions. When we’re done, your engineers can ship the next feature.
Our tech stack
Swift · SwiftUI · Kotlin · Jetpack Compose · SQLite
Example: Dreampedia, an iOS dream journal with generated art →
AI & RAG systems
What we do
Retrieval systems, model evaluation, inference infrastructure, and data engineering. We take AI models and make them run reliably in the real world, with predictable latency and costs.
How we approach it
We treat models as components in a system. The interesting work is almost always around them: retrieval quality, deterministic evaluation, and the boundary between model output and the deterministic systems that act on it. We’ll tell you when the right answer is a query, not a model.
Our tech stack
Python · pgvector · embeddings · vector search · OpenAI / Anthropic / local models
Project fit
We focus on long-term partnerships and comprehensive builds. We are typically not the right fit for isolated audits or short-term, disposable prototypes.
If we feel another team would be better suited for your specific needs, we will tell you early and offer a recommendation.