Python backends and AI services for real products
Python is the language of the AI and data ecosystem, which makes it the natural choice when your product needs language models, document processing, recommendations or analytics. Our team builds Python services with FastAPI or Django that sit behind your mobile or web app, handling prompts, retrieval, file processing and the business rules around them. We have shipped AI features in our own apps, including a Turkish-language chatbot, AI-generated fitness plans and document scanning with OCR.
What we deliver
LLM-powered features
Chat, summarisation, content generation and structured extraction using hosted models, with prompt versioning and output checks.
Retrieval and knowledge search
Retrieval-augmented generation over your documents or product data, with embeddings, vector search and source citations.
Document and image processing
OCR, PDF parsing and image analysis pipelines that turn uploaded files into structured, searchable data.
FastAPI and Django backends
Typed APIs, admin interfaces and background workers, deployed in containers and connected to your existing systems.
Cost and quality monitoring
Logging of model usage, latency and cost per feature, plus evaluation sets so you can change models without guessing.
How we approach it
When Python is the right call
Choose Python when AI, data processing or scientific libraries are central to the product. For a standard CRUD API without AI work, Node.js or a managed backend may be simpler.
AI behind a stable API
Models change often. We keep model calls behind your own service layer, so you can switch providers, add caching or adjust prompts without releasing a new app version.
Trade-offs stated upfront
AI features have running costs and can give wrong answers. We design limits, fallbacks and clear user expectations, and estimate per-user model cost before you commit.
Technologies
- Python
- FastAPI
- Django
- OpenAI / Anthropic APIs
- LangChain / LlamaIndex
- pgvector
- Celery
- Docker
Built by our team
AI features we have shipped in our own apps: Turkish-language chat, personalised workout and diet plans, and OCR document scanning.
How we work
- 01
Discovery
A short call to understand your goals, users and constraints. You get a clear scope, timeline and estimate.
- 02
Design
User flows and high-fidelity screens that follow Apple’s Human Interface Guidelines — reviewed together before a line of code.
- 03
Build
Iterative development in Swift and SwiftUI with regular TestFlight builds, so you can try the app on your own iPhone as it grows.
- 04
Launch
App Store assets, submission and review handled end to end. Your app goes live under your own developer account.
- 05
Support
Bug fixes, iOS version updates and new features after launch — based on real user feedback and analytics.
Questions
Do we need our own AI model?
Rarely at the start. Most products get the best results from hosted models combined with good prompts, retrieval over your own data and careful output checks. Training or fine-tuning becomes worth it only with clear evidence and enough data.
How do you keep AI costs under control?
We choose the smallest model that does the job, cache repeated requests, limit usage per user and track cost per feature, so you can see what each AI capability costs to run.
Can the AI answer from our own documents?
Yes. We build retrieval pipelines that index your documents and let the model answer with references to the source, which reduces made-up answers.
FastAPI or Django?
FastAPI suits lean, API-only services such as an AI backend. Django is better when you also need a built-in admin, user management and a larger relational data model.
Can AI features support languages other than English?
Yes. Our Genius app answers in Turkish, and we test prompts and outputs in each language your users speak rather than assuming English quality carries over.
Have a project in mind?
Tell us about it. Our team in Istanbul replies within one business day with next steps and a free intro call.
Start a project