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What do our Python development services include?
Python development services with us focus on AI and data: FastAPI services around language models, ingestion pipelines that turn messy sources into clean data, retrieval-augmented generation (RAG), and model fine-tuning with background workers. Our web products are usually built on PHP/Symfony or Node.js with React; Python is the language we add where AI and data work needs it.
A typical Python engagement covers:
- FastAPI services that wrap model calls, retrieval and business logic behind a typed API your product can call.
- LLM integration with OpenAI, Anthropic Claude or open models, including prompt management and evaluation sets.
- RAG: document ingestion, chunking, embeddings, vector and graph retrieval, with permission checks.
- Data pipelines: parsers for many source formats, normalization, deduplication and scheduled syncs.
- Fine-tuning and inference: LoRA adapters on open models, GPU workers and serving.
- Background processing with job queues, retries and visible status for long-running work.
When should you use Python in your product?
Use Python when the work is AI, machine learning or heavy data processing, because that is where its ecosystem is strongest. For the rest of the product, such as accounts, billing, admin and business rules, a PHP or Node.js back end is often the better fit, and many of our products run both.
| Task | Where we usually build it | Why |
|---|---|---|
| Calling OpenAI or Claude, streaming answers | Your existing back end (PHP or Node.js) | No new service needed |
| Ingesting and normalizing many document or data formats | Python service | Mature parsing and data libraries |
| RAG over large or frequently changing data | Python service | Embedding, retrieval and evaluation tooling |
| Fine-tuning, LoRA training, open-model inference | Python with GPU workers | The ML ecosystem lives in Python |
| Accounts, billing, roles, admin panels | PHP/Symfony or Node.js | Mature product and admin tooling |
Our own product AI Resume Master follows this split: PHP and Symfony for the product, React for the interface, Python for AI resume and cover letter generation. It reached 50,000 monthly active users.
How do you build AI services with FastAPI?
An AI service in FastAPI is a small, typed API that hides model calls, retrieval and prompt logic from the rest of the product. The product asks a question or submits a job; the service decides which model and which data to use and returns a result in a fixed format.
Our defaults:
- Typed contracts for every request and response, with OpenAPI documentation generated from them.
- Prompts stored as data, versioned and editable without a release where the product needs it.
- Jobs, not long requests: anything that takes more than a few seconds runs in background workers with status the product can poll or receive by callback.
- Model fallbacks and timeouts, so one slow provider does not stall the product.
- Evaluation sets checked in CI, so a prompt or model change is measured before it ships.
- Logging of inputs, outputs and token spend, with sensitive data handled according to your rules.
In the AI Grief Companion for a US startup, the AI platform runs on Python, FastAPI and Celery with PostgreSQL, Redis and Neo4j. Each training stage is a typed contract that produces artifacts for the next, and completion travels back by callback through the gateway to the product API.
How do you build data pipelines and RAG?
Good RAG starts with good ingestion. Most retrieval problems come from data that was never cleaned: duplicates, broken formatting, missing owners and mixed languages. We build the pipeline first and the chat second.
The pipeline: a parser per source format; normalization into one schema; deduplication and validation; chunking that follows the document structure; embeddings and an index; then retrieval with access checks so users only see what they are allowed to see. Every step logs what it processed and what failed. Our RAG development page and RAG implementation guide go deeper.
Two published examples of this discipline:
- In the AI Grief Companion, a Python gateway parses ten chat-export formats (WhatsApp, Messenger, Instagram, Discord, iMessage from an iPhone backup, Android SMS and others) into one normalized message table and encrypts every message at ingest. Memory comes from three retrieval layers over a Neo4j fact graph.
- The sports events calendar for a UK-based sports tech company, built with PHP and Python, has a custom parsing engine that aggregates and normalizes fixtures from hundreds of calendars and feeds, with time-zone handling, APIs and embeddable widgets for B2B clients.
When does fine-tuning make sense?
Fine-tuning makes sense when a model must hold a specific voice, format or behavior that prompts and RAG cannot keep reliably. For most business products, prompts plus retrieval over your own data get further, cost less and are easier to update when the data changes.
When fine-tuning is the right call, we use parameter-efficient methods such as LoRA on an open model, with a dataset built from your data and an evaluation set to measure the result. In the AI Grief Companion, the pipeline builds a dataset, trains LoRA adapters on Qwen2.5-7B with 4-bit QLoRA, and vLLM loads the right adapter per request. Inference degrades on purpose: the chat works with the base model minutes after upload and gets closer to the person as each training stage finishes. More on model choices is on our generative AI development page.
Can you add a Python service to an existing PHP or Node.js product?
Yes, and that is the most common way we use Python. Your product keeps its back end, accounts and billing; a separate Python service handles AI or data work and exposes a small typed API. The main back end calls it with the user's context, and long-running jobs report back by callback or a status endpoint. Permissions stay in the main back end, so the Python service never decides on its own who may see what.
This keeps the risk low: the new service can be deployed, scaled and rolled back on its own, and the rest of the product does not change language or framework. See PHP development and Node.js development for the other side of the integration.
How much does Python development cost?
Python development cost depends on data volume, model strategy and how the service connects to your product. As orientation, with us an AI feature on hosted models starts at $10,000, an AI app over company data with RAG costs $10,000 to $40,000, and a product with custom models (fine-tuning, RAG memory, GPU inference, data pipelines) lands at $60,000 to $150,000. Run costs for tokens, vector stores and GPUs come on top; see our guide to AI app development cost.
How we work on Python projects
We start with a short call about the product, the data and what the AI should do, then agree the first milestone with a fixed quote. For AI work, the first milestone usually includes an evaluation set, so quality is measured from week one. Delivery runs in milestones with weekly demos, in your repositories and cloud accounts. Senior engineers review every change, and AI coding agents handle routine work such as tests and refactors. To connect AI to an existing product, see AI integration.
Tell us about your AI or data project on our contact page, or describe the scope in our project estimate form.
