Series B HR SaaS
- Challenge
- No distributed tracing; 4-hour MTTR on hiring API.
- Simplileap solution
- OpenTelemetry, Grafana SLOs, and error-budget alerts.
- Outcome
- MTTR 4h → 22 minutes.
// Build: Backend
Python & Django development for data-driven and AI-ready platforms
Scalable Django REST APIs, data-heavy backends, Celery task pipelines, and ML model integrations. Python as a production engineering choice, not just a scripting language.
// Capabilities
Python/Django development scope
Django REST Framework APIs
ViewSets, serialisers, authentication, permissions, pagination, filtering, and versioning. Clean, testable API design built for mobile and frontend consumption.
Data-heavy Backends
Complex database models, multi-step data pipelines, Django ORM optimisation, raw SQL where needed, and aggregation queries for reporting and analytics.
Async Task Processing
Celery with Redis or SQS for background jobs, scheduled tasks, email pipelines, report generation, and data sync without blocking the request cycle.
Admin & Internal Tools
Django Admin customisation and django-grappelli, or Wagtail-backed CMS for editorial teams who need powerful content management with no vendor lock-in.
ML & AI Integration
Serving scikit-learn, TensorFlow, or PyTorch models via REST endpoints. Pipeline orchestration, model versioning, and integration with data stores.
Multi-tenant SaaS Architecture
Schema-based or row-level tenant isolation, subscription management, and feature flagging patterns for B2B SaaS products built on Django.
// Engineering standards
Django as an engineering platform, not just a framework
Django's power lies in its consistency and batteries-included design. Used correctly, it dramatically accelerates data model design, admin tooling, and API development. Misused, it creates God-views, serialiser spaghetti, and ORM queries that kill database performance.
We enforce service-layer separation, write tests at the service level (not just the view level), profile queries in CI, and design background task architecture before it becomes a production bottleneck.
// Standards
- Service-layer abstraction above views and ORM
- pytest + factory_boy for data-driven tests
- select_related / prefetch_related enforced in queryset review
- Django Debug Toolbar and django-silk for query profiling
- Environment-based settings with django-environ
- Celery beat for periodic tasks, not cron hacks
- Type hints across the codebase with mypy in CI
- docker-compose for local environment parity
Production patterns and experience depth: Python expertise ›
// In practice
How python/django development engagements run
Django engagements pair ORM models with Celery task queues, admin workflows, and REST or GraphQL APIs — common for data-heavy internal tools, ML feature stores, and LLM orchestration layers. We document data boundaries and PII handling before the first migration ships.
// Stack & frameworks
Stack we use for this
Frontend
- Next.js App Router
- React 18+
- Tailwind CSS
- Core Web Vitals tuning
Backend & data
- Node.js / Python APIs
- PostgreSQL
- Redis caching
- REST & GraphQL
Operations
- CI/CD pipelines
- Docker / Kubernetes
- Observability
- Security hardening
// Delivery
Simplileap execution framework
01
Architecture mapping
Dependencies, API contracts, compliance constraints, and performance budgets documented before sprint one.
02
Secure sprints
Two-week increments with GitHub access, demo recordings, and QA checkpoints, client visibility at every stage.
03
QA & handover
Automated tests on critical paths, security review, runbooks, and knowledge transfer to your team.
// Proof
Real deployments from Bangalore
B2B marketplace
- Challenge
- 45-minute pipelines blocked daily releases.
- Simplileap solution
- Turborepo cache, Docker layer reuse, parallel e2e shards.
- Outcome
- Builds median 11 minutes; daily deploys achieved.
// Engagement models
How teams engage us
Currency
| Package | Ideal for | Investment | Includes |
|---|---|---|---|
| Website or app design / build | Mid-market B2B | ₹75K – ₹12L |
|
| Product MVP | SaaS & startups | ₹8L – ₹25L |
|
| Legacy modernisation | Regulated industries | Phased proposal |
|
// Company and service positioning
Company and Service positioning is reviewed for production delivery standards by Harsha Parthasarathy (Co-Founder, Strategy & Operations 24+ years IT veteran, IBM, Global Delivery, Program Management) and Keshav Sharma (Co-Founder, Engineering and Lead Architect, Full-stack engineering, product delivery and technical standards).
CIN
AAU-8582
Startup India
DIPP83124
Founded
November 2020
Office
Residency Rd, Bengaluru, India
// FAQ
Python/Django development questions
When does Python/Django make sense over Node.js?+
Python is the right choice when your backend needs to interface with data science libraries, ML models, or scientific computing, the ecosystem is unmatched. Django also makes sense for data-heavy CRUD applications where its ORM, admin, and batteries-included approach accelerate development significantly.
How do you structure a scalable Django project?+
We use app-based separation of concerns with service layer abstraction above Django's views and ORM. Serialisers handle validation and representation. Business logic lives in service classes, not views. Signal overuse is avoided in favour of explicit service calls.
How do you handle performance in Django at scale?+
N+1 query elimination using select_related and prefetch_related, database index review, query annotations instead of Python loops, connection pooling via PgBouncer, Redis caching for expensive computations, and async views in Django 4+ for I/O-heavy endpoints.
Can you integrate Python ML models into a Django web product?+
Yes. We integrate ML inference into Django via REST endpoints, background Celery tasks, or async views depending on latency requirements. Model versioning, feature stores, and monitoring are handled as engineering concerns, not just data science experiments.
Ready to scope your next initiative?
Share your goals with our Bangalore team. We respond within one business day with a clear path from discovery to delivery.