RAG Chat Copilots
Streaming chat backends with citation tracking, hybrid search, and prompt caching.
Python 3 Development for AI-First MVPs
Ship a Python 3 AI MVP in 2-3 weeks. We use Python 3.10+ pattern matching, structural typing, and async-first architecture to build agentic apps, RAG systems, and ML-backed APIs that survive contact with real users.
85+
Python 3 engineers on staff
2-3 wk
Average AI MVP delivery
94%
Production AI uptime SLA
400+
Python AI projects shipped
Why Python 3 wins for AI MVPs in 2026
1
AI library gravity
- LangChain, LlamaIndex, DSPy, CrewAI all Python-native
- OpenAI, Anthropic, Cohere reference SDKs ship Python first
- Hugging Face Transformers and Diffusers are Python-only
- Vector DB clients (Pinecone, Weaviate, Qdrant) prefer Python
2
Modern language ergonomics
- Match statements simplify agent tool dispatch
- Strict type hints catch tool-calling mismatches at lint time
- Structural pattern matching for streaming token handlers
- Native asyncio for parallel inference and tool execution
3
Production-ready patterns
- FastAPI + uvicorn handles 5k req/s on a $20 droplet
- Pydantic v2 enforces structured outputs for tool calls
- OpenTelemetry tracing for full LLM observability
- Celery / Dramatiq for long-running fine-tune jobs
4
Founder velocity
- Same code in notebook, script, and production service
- Hot reload via uvicorn cuts iteration loops to seconds
- Type-checked with pyright; tested with pytest-asyncio
- Deploys to Vercel, Fly, Railway, Modal, or Lambda
What we build with Python 3
Production AI features founders can ship to paying users
Agentic Workflows
LangGraph or CrewAI agents that plan, call tools, and recover from failures.
LLM-powered APIs
FastAPI services with rate limiting, structured outputs, and per-tenant cost tracking.
Document AI Pipelines
Async OCR, chunking, and embedding pipelines that scale on demand.
Eval Harnesses
Pytest-based regression suites for prompts and agents: catch drift before users do.
Notebook to Production
Take working Jupyter prototypes and refactor into testable, deployable services.
Python 3 advantages our team ships against
Type-safe LLM tools
Pydantic schemas drive both the prompt and the parser, eliminating malformed JSON failures.
Async-first by default
Every external call is async, LLMs, vector search, DBs, so users never wait for serial round-trips.
Cost observability
Token counters and per-route cost dashboards built into every Python AI MVP.
Eval-driven prompts
Prompts versioned in code, regression-tested, and reverted on quality drops.
Boring deploy targets
Modal, Fly, or AWS Lambda: predictable runtime, no Kubernetes for an MVP.
Fast handoff
Codebase ships with README, makefile, eval suite, and Loom, your team can extend day one.
Python 3 advantages our team ships against
Type-safe LLM tools
Pydantic schemas drive both the prompt and the parser, eliminating malformed JSON failures.
Async-first by default
Every external call is async, LLMs, vector search, DBs, so users never wait for serial round-trips.
Cost observability
Token counters and per-route cost dashboards built into every Python AI MVP.
Eval-driven prompts
Prompts versioned in code, regression-tested, and reverted on quality drops.
Boring deploy targets
Modal, Fly, or AWS Lambda: predictable runtime, no Kubernetes for an MVP.
Fast handoff
Codebase ships with README, makefile, eval suite, and Loom, your team can extend day one.
Python 3 for AI MVPs, FAQ
Python 2 is end-of-life. Python 3.10+ unlocks pattern matching, parameter spec, and structural pattern matching that make agentic code dramatically cleaner. The entire AI ecosystem (LangChain, LlamaIndex, Hugging Face) requires Python 3.10 or newer.
For AI MVPs the bottleneck is the LLM, not Python. We use async I/O so the runtime overhead is under 5%. When CPU-bound work appears we drop to NumPy, PyTorch, or a Rust extension. Production Python AI services routinely handle thousands of concurrent users.
FastAPI is the default for new AI MVPs: it gives async, automatic OpenAPI docs, Pydantic validation, and dependency injection. Django stays for content-heavy products with admin needs.
We pin every package with uv or poetry, lock CUDA/torch versions per environment, and ship a Dockerfile so prod matches dev byte-for-byte.
Yes. Most migrations take 1-2 weeks: 2to3 + pyupgrade for syntax, mypy for type sweep, and feature flags so the cutover ships safely.
