What the client needed
HerKey needed a production-grade platform that could support multiple user roles, content moderation, and partner integrations without a rewrite every quarter.
How SparkScribe approached it
Before build, SparkScribe worked with HerKey to translate their SaaS / Marketplace goals into an actionable plan - not an off-the-shelf template.
Discovery & planning
- Mapped data ownership across candidates, employers, mentors, and admins before expanding engagement features.
- Profiled high-traffic listing and messaging endpoints to target caching and query optimization where it mattered.
- Aligned CI/CD and staging parity with the client's release cadence for community and employer launches.
Our engagement covered Backend development, API design, CI/CD, Performance optimization, Ongoing support - scoped in phases with staging parity and admin self-service so HerKey could run day-to-day operations without waiting on engineering.
How we solved it
SparkScribe extended and hardened the Django backend - REST APIs, role-based access, search and notification paths, and CI/CD pipelines for predictable releases.
We mapped data ownership across candidates, employers, mentors, and admins early. Audit-friendly patterns support profile exports, moderation queues, and partner integrations via APIs and webhooks.
Performance work targeted high-traffic listing and engagement endpoints with caching and query optimization where profiling showed pain.
How we helped the client
HerKey continues as India's largest AI-powered career community for women - supporting job discovery, reskilling, events, and employer engagement at scale.
- Multi-role platform: Candidates, employers, and admins operate in one system with clear permission boundaries.
- Release cadence: CI/CD and staging parity reduced regression risk on frequent feature launches.
- Engagement depth: Groups, events, and messaging flows support the community model described publicly on herkey.com.
- Long-term partnership: SparkScribe provides ongoing backend support as product scope grows.
Technologies we used
Technologies we used
- Python
- Django
- PostgreSQL
- Redis
- React
- AWS
- CI/CD
How we applied the stack
Django REST APIs with PostgreSQL back jobs, learning content, events, groups, and messaging. Redis supports caching on hot paths; React powers key web surfaces. AWS hosts production workloads; CI/CD pipelines keep frequent feature releases predictable.
