# KALQORE PRIVATE LIMITED

> Software & AI systems, built to run in production

- Canonical listing: https://agencysource.com/agencies/kalqore-private
- Categories: [Web Development](https://agencysource.com/categories/web-development), [AI & LLM Development](https://agencysource.com/categories/ai-development), [Cloud & DevOps](https://agencysource.com/categories/cloud-devops), [Custom Software Development](https://agencysource.com/categories/custom-software-development)
- Services: Custom Software Development, AI Development, API Development, Cloud Consulting & SI, Generative AI, Web Development
- Team size: 2 - 9
- Hourly rate: $25 - $49 / hr
- Minimum project: $10,000+
- Founded: 2026
- Website: https://www.kalqore.com
- Listing: Not yet claimed
- Agency Source rating: no Agency Source reviews yet

## About

Kalqore Technologies

We build and ship production platforms — fast.

Most recently: a complete online exam-preparation platform for the Indian competitive exam market. Student auth, a cheat-resistant timed test engine, a question bank with AI-assisted paper digitisation, payments with GST and refunds, wallet and referrals, coupons and influencer attribution, exam-centre seat booking, email and WhatsApp campaigns, admin analytics. Around 110 database tables and 390 API endpoints, live in production with paying users.

Built in four months. Not by a team of six.

That speed comes from having done it before. Auth, payments, admin roles, background jobs, infrastructure and CI/CD are solved problems in our codebase, so client work starts at the part that's actually specific to the client. Timelines agencies quote in months, we quote in weeks.

- What We Build
- Backend platforms: FastAPI, PostgreSQL, Celery, Redis, AWS
- Payments and commerce: gateways, GST, refunds, reconciliation, wallets

AI features safe to put in front of real users: retrieval-grounded chatbots, document extraction, LLM content with human approval gates, per-request cost metering with spend limits, kill switches that work without a deploy

- Infrastructure: Terraform-managed AWS, CI/CD, WAF, monitoring
- On AI

Most LLM projects fail in production for three reasons: runaway costs, wrong output reaching customers, no way to shut it off. Our systems compute facts in code and let the model write only prose, validate every output against a schema before storing it, and never auto-publish anything a user will see. Three such subsystems are running today.

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