Hire Python and FastAPI Developers in Pakistan
Bracket Mind is a Python and FastAPI development company in Gujranwala, Pakistan, building AI backends, APIs and background-job pipelines for businesses. Our own product, Aidfolk, runs on Python 3.14, FastAPI, Pydantic AI and PostgreSQL with pgvector. It has been live at aidfolk.com since October 2026 and passes 1,077 backend tests.
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What Python systems has Bracket Mind built?
Our own AI customer-support product, Aidfolk, is Python from end to end: FastAPI on Python 3.14, SQLAlchemy with Alembic migrations, PostgreSQL 18 with pgvector, and a Taskiq worker on Redis for everything slow. It answers customer questions from a business's own documents and cites where each answer came from.
Documents are parsed with Docling and split on headings, and unchanged pages are never re-embedded. Retrieval fuses pgvector similarity and Postgres full-text search with reciprocal rank fusion in a single query. OpenAI, Anthropic and Gemini models are called through Pydantic AI, and answers fail over between providers when one goes down.
On a client's AI backend, Python that the model writes runs in a fresh Docker container with 1 CPU, 512 MB of memory and no credentials inside, and the container is force-removed after each run.
- FastAPI services and REST APIs with Pydantic validation
- AI features on Pydantic AI: typed model output and provider failover
- Background jobs on Taskiq and Redis for ingestion, crawling and embedding
- PostgreSQL with pgvector, SQLAlchemy and Alembic migrations
- RAG pipelines: Docling parsing, hybrid search and cited answers
How do you run background jobs in Python without losing work?
In Aidfolk, ingestion, web crawling and embedding run in a worker, not inside the API. The API queues a job on a Redis Stream and returns, so a job is never lost on an API restart or deploy. Delivery is at least once: a worker acknowledges a job only after running it, so a job whose worker crashed or was redeployed is picked up by another, and every job has to be safe to run twice.
We went further than the library does by default. Out of the box, the Redis broker reclaims a crashed job only when new messages arrive, so on a quiet queue it could wait indefinitely. Our broker reclaims abandoned jobs on every poll, hands each one to exactly one worker, and keeps a heartbeat on running jobs so a long one is not handed to a second worker mid-run. A reconciler re-queues work left pending without a job, such as an upload made while Redis was down.
How do you keep a Python AI backend reliable?
Tests run against real infrastructure. Aidfolk has 1,077 backend tests, and its integration tests run against PostgreSQL and Redis as the app's own database role, so row-level security is tested the way production enforces it. On every pull request, CI lints the code, runs the tests, checks that the migrations build exactly the schema the models describe, and builds the production image.
Model output is checked, not trusted. Where Aidfolk needs structured output, such as triaging a support email, Pydantic AI validates the reply against a typed model and asks once more when it does not fit. Every AI call is priced into an append-only ledger the database refuses to edit, and dependencies are locked with uv, taking new releases only once they are at least a week old.
Proof: systems we built
AI Customer Support SaaSLive product
Aidfolk
AI customer support that answers from a business's own docs. Add your files, website and help pages, and get a chat agent for your site that cites where every answer came from — set up in the browser, no code. Our own product, running in production.
- In production since October 2026
- Every answer cites its source
- 1,077 backend tests passing
Founder-built · Read the case study
Multi-Tenant AI BackendProduction
Shared AI Platform Backend
One production .NET backend running the AI features of four client products — a medical platform, a community platform and two storefronts — instead of four separate AI stacks.
- One AI backend, four products
- 8 provider types, routed per task
- Risky tool calls wait for a human
Team project · Read the case study
Questions buyers ask
Is FastAPI a good choice for an AI backend?
For most AI backends, yes. FastAPI is asynchronous, so one server can hold many streaming model responses open at once, and its Pydantic models validate every request and generate the API's OpenAPI document. Aidfolk runs on FastAPI, streams answers to its chat widget over SSE, and moves slow work such as ingestion to a Taskiq worker.
Can you build AI features in Python with Pydantic AI?
Yes. Aidfolk calls OpenAI, Anthropic and Gemini models through Pydantic AI, failing over between them when a provider goes down, and prices every call into a ledger so AI costs show up per customer. Where we need structured output, Pydantic AI validates the model's reply against a typed schema instead of trusting free text.
Do you use Python only for AI, or for whole backends?
Whole backends. Aidfolk's Python backend handles workspaces, teams, plan limits, usage metering and file version history as well as retrieval and model calls. We usually suggest Python when AI, data processing or background jobs are the core of the product, and Node.js or .NET when they are not.
What Python stack do you work with?
Python 3.14 with FastAPI, Pydantic and Pydantic AI, SQLAlchemy with Alembic migrations, PostgreSQL with pgvector, and Redis with Taskiq for background jobs. We test with pytest, lint with Ruff, lock dependencies with uv and ship in Docker. That is the stack Aidfolk runs on in production.
How do I hire Python developers in Pakistan?
Send us a spec, an existing repository or a rough idea, and we reply with a written scope, timeline and fixed quote. You can engage us per project or as a standing team alongside your own engineers. Code lands in your repositories from the first sprint, and you work directly with the engineers writing it, in hours that overlap the UK and Europe and part of the US day.