01
Teams shipping user-facing products where Python’s strengths compound over time.
Fits you
Senior Python · 5-day deploy
99.97%
Uptime
180ms
P95 latency
100K
Concurrent users
Python
Python for web. Python for AI. Python for data. One team for all three.
Most shops do one Python well. We do all three, because real systems need all three working together.
# FastAPI + Pydantic + async
from fastapi import APIRouter
from pydantic import BaseModel
router = APIRouter()
class Order(BaseModel):
items: list[str]
total: float
@router.post('/orders')
async def create(order: Order) -> Order:
return await service.save(order)
SaaSFintechHealthtechE-commerceEdtechLogisticsSaaSFintechHealthtechE-commerceEdtechLogisticsSaaSFintechHealthtechE-commerceEdtechLogistics
Who this is for
When Python is the right pick.
If any of these sound like you, we should talk.
02
Startups avoiding the "rewrite in year two" trap by picking a stack that scales with the team.
Fits you
03
Enterprises modernising legacy monoliths, module by module, without big-bang rewrites.
Fits you
04
Product teams who want senior engineering partners, not staff-augmentation bodies.
Fits you
The proof
How we structure a production Python project.
LAYER 1
Architecture
Clear module boundaries. Typed contracts. No circular dependencies. Easy to onboard new engineers in a week, not a month.
LAYER 2
Testing
Unit for logic, integration for boundaries, E2E for critical flows. 80%+ coverage on money-handling paths.
LAYER 3
Observability
Structured logs, traces, metrics. Every deploy has an alarm trail. Every 3AM incident has a trail back to root cause.
What we build
Where we deploy Python.
01
Greenfield products
v1 through to first scale.
02
Team augmentation
Senior engineers embedded with yours.
03
Legacy modernisation
Strangler-fig migration to Python.
04
Code review & audit
Independent technical audit for investors or acquirers.
ClientAn AI product companySaaS
Data pipelines + FastAPI services + PyTorch models. <em>All Python, all coherent</em>.
A Python project where architecture decisions made in week one held up under 100× scale. No rewrite. No regrets.
01
99.97%
Uptime
02
180ms
P95 latency
03
100K
Concurrent users
04
14
Engineers on the codebase
What it's like working with us
Three Python agencies told us the job would take 9 months. Ligio shipped in 14 weeks. Better code than the last two hires.
HP
Harish P.
CTO · An AI product company
SaaS · India
Tech stack
The tools. Chosen for your reasons.
10technologies in rotation
01Python 3.12
02FastAPI
03Django
04SQLAlchemy
05Pandas
06PyTorch
07LangChain
08Airflow
09Pytest
10Ruff
Process
How we actually work.
06 stages
01
Scoping
Is Python right for your problem?
Week 1
02
Architecture
Data model, services, deployment.
Weeks 1-2
03
Scaffold
CI/CD, lint, tests, observability.
Week 2
04
Build
Features. Python idioms. Senior code.
Weeks 3-12
05
Harden
Load test, security, docs.
Weeks 12-14
06
Launch
Staged rollout. 30-day on-call.
Week 14
Questions
Answers, without the fluff.
Still have questions? Talk to us — we answer within a business day.
05common questions
01Django or FastAPI?
Django for full-stack apps with admin, auth, and ORM built-in. FastAPI for APIs-only, especially AI-adjacent.
02How long does a typical engagement take?
Most projects run 10-18 weeks from kickoff to production launch. We share a milestone plan in week one and update weekly.
03Do you sign an NDA?
Yes. Standard mutual NDA on request, before the first technical conversation.
04Who owns the code and IP?
You do. Code is in your GitHub org from day one. All IP transfers unambiguously on delivery.
05What does your pricing model look like?
For v1 builds: fixed scope, fixed milestones. For ongoing work: monthly retainer with a defined team. We don't do time-and-material surprise billing.
Related
Explore more of what we do.
Python done properly.
A 30-minute technical conversation. Bring your architecture questions.
NDA on request.