AI hiring and career platform
HulChul
Product engineering across matching, job ingestion, the Raj career agent, analytics, and production operations.
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AI product development & platform engineering
Techly Assist is a founder-led AI product development and platform engineering studio in New Delhi, working globally to turn complex ideas into reliable systems for real users.
Selected work
AI hiring and career platform
Product engineering across matching, job ingestion, the Raj career agent, analytics, and production operations.
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Evidence-led intelligence platform
Team contributions spanning product architecture, insight workflows, evidence review, platform reliability, and governed delivery.
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AI education and coding environments
Platform delivery across interactive learning, browser-based coding, AI tutoring, releases, payments, and operations.
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These are selected team and product contributions. Scope is stated carefully; private repositories, client data, and internal infrastructure stay private.
Capabilities
Strategy matters because code is expensive. Operations matter because launch day is only the beginning.
Useful AI experiences grounded in real workflows—from research and career agents to voice, retrieval, and evidence-aware systems.
Backend-heavy systems with clear domains, durable data, asynchronous work, and interfaces teams can keep extending.
Shipping is the midpoint. Releases are instrumented, monitored, documented, and improved with operational reality in view.
Delivery model
A compact, transparent process that reduces risk without turning momentum into meetings.
Align the product problem, user, evidence, success measures, and the smallest valuable release.
Design and build the interfaces, services, data flows, and AI behavior with quality gates from day one.
Ship, observe real use, strengthen reliability, and make the next decision from evidence—not theatre.
About Techly Assist
Founded by Rahul Jalan, Techly Assist brings product direction, architecture, implementation, cloud operations, and release discipline into one working relationship.
The pattern across the work is consistent: understand the real problem, build the smallest durable system, expose the evidence, and stay close enough to production to learn what matters next.
Have a hard product problem?
Share where the product is today, what is stuck, and what a useful first outcome would look like.
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