GIL and async confusion
Mixing threading, multiprocessing and asyncio without a clear mental model produces deadlocks and degraded throughput. We design concurrency strategies explicitly and document them.
FastAPI LangChain EU AI Act GDPR
Python Development Services for AI, Data and Backend Workloads
Python is our default for AI, ML and data engagements — FastAPI for low-latency inference gateways, LangChain and LlamaIndex for RAG over private corpora, PyTorch for fine-tuning, Pydantic for contract-first APIs. Every AI engagement ships with an EU AI Act risk classification document on day one.
We deliver Python engineering for four buyer profiles: AI and LLM product teams building RAG pipelines, agents and inference services; data engineering teams orchestrating ETL from operational databases to analytics warehouses; SaaS teams choosing FastAPI for high-throughput APIs or Django for admin-heavy portals; and regulated industries — healthtech, fintech, legaltech — where EU AI Act compliance, GDPR data handling and explainable model decisions are delivery requirements.
Challenges
Industry challenges we solve
Packaging and dependency hell
Conflicting transitive dependencies break deployments silently. We use Poetry for deterministic locking, multi-stage Docker builds and a private PyPI mirror.
PII leaking to third-party LLMs
User prompts often contain names, emails and health data. We implement presidio-based redaction and zero-data-retention endpoint configuration before any prompt leaves the perimeter.
LLM inference latency under load
Cold-start and token generation latency spike under concurrent requests. We batch, stream with SSE, implement semantic caching and deploy async FastAPI workers.
Model quality without an eval harness
Prompt changes ship without regression checks and silently degrade outputs. We build RAGAS-based eval harnesses before the first prompt goes to production.
Vector DB choice paralysis
pgvector, Qdrant, Pinecone, Weaviate — teams delay because the landscape is fragmented. We select based on your existing infrastructure, consistency requirements and query patterns.
Solutions
Solutions we build
RAG over private corpora
Retrieval-augmented generation pipelines over internal documents, knowledge bases and databases — with source attribution and hallucination controls.
LLM inference gateways
FastAPI services wrapping OpenAI, Anthropic or self-hosted models — with streaming, caching, rate limiting and multi-provider fallback.
Data pipelines and ETL
Async pipelines with Celery or Dagster moving data from operational sources to Snowflake, BigQuery or PostgreSQL analytics schemas.
Django admin portals
Full-stack Django applications with custom admin, Celery background tasks and PostgreSQL — for CMS, operator workstations and internal tooling.
Classical ML services
Scikit-learn and PyTorch model training, validation, MLflow experiment tracking and FastAPI serving with A/B routing.
Python ↔ Node/.NET bridges
gRPC and REST bridges connecting Python AI services to Node.js or .NET product backends — with generated clients and contract tests.
Stack
Technology stack
Python 3.12, FastAPI, Pydantic v2, SQLAlchemy 2, Celery, LangChain, LlamaIndex, PyTorch, MLflow, Ragas, Poetry, Docker, Kubernetes.
Compliance
Compliance & regulations
GDPR-aligned · EU AI Act-aware · SOC 2-capable · HIPAA-capable · CCPA-acknowledged
EU
- EU AI Act — risk classification, conformity assessment, technical file.
- GDPR Art. 22 — automated decision-making, DPIA, human oversight.
- DSA — transparency disclosures for recommender and content systems.
- GDPR — data residency, DSR automation, lawful basis.
US
- NIST AI RMF — govern, map, measure, manage framework alignment.
- CCPA/CPRA — automated decision opt-out and data subject rights.
- SR 11-7 — model risk management for financial services.
- HIPAA — de-identification, minimum necessary, audit logging.
Shared: OWASP LLM Top 10, prompt-injection hardening, SBOM per build.
Cases
Selected Python case studies
Social Media · Consumer Tech
JoyJet
Production social platform — App Store + Google Play, live across the US and EU — with geo Radar, encrypted messaging and a virtual economy.
LegalTech · Mobile · CRM
Signatory Pro
Native iOS and Android e-signature clients with a Symfony + React CRM for a cross-border law firm — KYC onboarding and a defensible evidence trail for US & EU matters.
HealthTech · Endoscopy
ArgoView
Tablet-first endoscopy recording, patient records, and DICOM/HL7 export — built on Laravel + React with browser-tier WebRTC capture for US & EU clinics.
Why YuSMP
Why Python teams choose YuSMP
Vendor-neutral LLM orchestration
We integrate OpenAI, Anthropic, Mistral and self-hosted models through a unified router — so you can switch providers without rewriting application logic.
Eval harness on every prompt change
No prompt ships without a regression eval. RAGAS metrics, golden-set comparisons and business-specific benchmarks run in CI on every merge.
EU AI Act classification on day one
Every AI engagement starts with a risk classification workshop. High-risk systems get conformity assessment plans; limited-risk systems get transparency disclosure templates.
FAQ
Python FAQ
How do you classify EU AI Act risk for our product?
We run a structured workshop covering intended purpose, user population, autonomy of decision-making and sector to assign the correct risk tier (unacceptable, high, limited or minimal). High-risk systems get a full conformity assessment plan; limited-risk systems get the transparency disclosures. We document the classification in your technical file.
FastAPI or Django for a new project?
FastAPI for APIs and inference gateways where latency and async throughput matter. Django for admin-heavy applications, CMS features and teams that prefer batteries-included conventions. We often combine them: Django for auth and admin, FastAPI for performance-critical endpoints.
How do you prevent PII from leaking to third-party LLMs?
We implement PII detection and redaction (presidio or custom NER) before prompts leave the perimeter, use zero-data-retention API endpoints where available (OpenAI ZDR, Azure OpenAI with no-logging config), and keep EU personal data within EU-region endpoints.
RAG or fine-tuning — which approach for our use case?
RAG is right for dynamic, frequently updated corpora where source attribution matters — legal documents, product catalogs, support knowledge bases. Fine-tuning is right for consistent tone, format or domain vocabulary that RAG alone cannot reliably produce. We recommend RAG first and evaluate fine-tuning only when RAG plateaus.
How do you evaluate LLM output quality?
We build an eval harness before writing the first prompt: golden-set Q&As, RAGAS metrics for RAG (faithfulness, relevance, context recall) and custom business metrics. Every model or prompt change runs the eval before merge.
What vector database do you use?
pgvector on PostgreSQL for teams already running Postgres — zero new infrastructure, transactional consistency, SQL joins. Qdrant for standalone deployments needing filtered vector search at scale. We have production experience with both.
How do you handle Python packaging and dependency management in production?
Poetry for dependency locking, multi-stage Docker builds to keep images lean, and a private PyPI mirror for air-gapped environments. We pin direct and transitive dependencies and run pip-audit in CI.
Can you defend against prompt injection?
We apply structured output schemas (JSON mode / Pydantic), separate system and user content with clear delimiters, validate model outputs against expected schemas, and run adversarial injection test sets in CI. No single technique is complete — defence in depth.
Build AI products with Python engineers fluent in EU AI Act
Response within 1 business day. NDA on request.