Swapnil Tiwari · Product Manager

Building scalable enterprise products.
Across HR Tech, analytics, and automation.

I turn complex, manual, and fragmented workflows into scalable, intelligent product systems with measurable impact.

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~70%
TAT reduction (BGV)
45 → 12
Days, end-to-end
4d → real-time
Process initiation
5+
Enterprise products shipped
§ 01 · Selected Work

Products built to compress complexity.

§ 02 · Case Studies

Problem → approach → impact.

Each case study walks the full arc — from the friction in front of users, through the trade-offs taken, to the measurable outcome shipped. Click any title to expand.

01
HR Tech · Hiring Platform

Smart Rekruit

A unified hiring platform that replaced fragmented, manual recruiter workflows with automated screening, integrated assessments, and a single source of truth.

Problem
  • Recruiters were spending excessive hours on manual resume screening.
  • Hiring workflows were disconnected across tools and teams.
  • No standardized assessments — evaluation quality varied widely between recruiters.
Approach
  • Conducted recruiter interviews across business units to map real workflows.
  • Walked the end-to-end hiring journey from intake to offer.
  • Identified the highest-friction bottlenecks: screening, assessments, and HRIS integrations.
Decisions & Trade-offs
  • Build vs Buy → chose a vendor-led approach for screening to compress time-to-market.
  • Depth vs Speed → automated the highest-impact steps first; deferred edge cases to phase two.
  • Prioritized recruiter throughput over recruiter UI polish in the first release.
Solution
  • Automated resume screening with configurable rule sets per role.
  • Integrated assessments inside the recruiter workflow — no tab switching.
  • Native Darwinbox integration so candidate data flows end-to-end.
  • Streamlined recruiter pipeline with stage-level SLAs.
Impact
  • Material reduction in screening time per requisition.
  • Recruiter bandwidth freed up for higher-leverage activities like sourcing and closing.
  • Hiring efficiency improvements across teams adopting the platform.
Learnings
  • Initial workflows were too rigid; v2 redesigned around configurable stages.
  • Recruiter training was as critical as product capability — adoption needed structured rollout.
02
Operations · TAT Reduction

BGV Automation

Compressed background verification turnaround from 45+ days to 12 by removing the manual initiation step entirely.

Initiation latency
4 days Real-time
End-to-end TAT
45+ days 12 days
TAT reduction
— ~70%
Problem
  • 4-day delay before BGV could even be initiated for a new hire.
  • End-to-end TAT was 45+ days, blocking onboarding and frustrating candidates.
  • Ops team was overloaded with repetitive coordination work.
Approach
  • Decomposed the BGV process into micro-steps with explicit time stamps on each.
  • Identified initiation — not verification — as the dominant bottleneck.
  • Mapped trigger points where automation could replace manual handoffs.
Decisions & Trade-offs
  • Focused on initiation first instead of attacking the full lifecycle at once.
  • Automated trigger points (offer rolled → BGV kicked off) instead of building a UI for ops to push buttons faster.
  • Built exception-handling as a first-class flow, not an afterthought.
Solution
  • Real-time initiation triggered automatically off the offer-acceptance event.
  • Vendor integration eliminated manual data entry into the BGV partner system.
  • Visibility dashboards for ops, candidates, and HR business partners.
Impact
  • Initiation went from 4 days to real-time.
  • End-to-end TAT compressed from 45+ days to 12 days.
  • Approximately 70% reduction in overall turnaround time.
Learnings
  • Exception handling needed dedicated design — early flows broke on edge cases.
  • Visibility dashboards drove adoption more than the automation itself.
03
Analytics · Discovery

Centralized Analytics Platform

Re-framed an "analytics adoption" problem as a discovery problem and shipped a unified hub instead of rebuilding BI tools.

Problem
  • Dashboards were scattered across multiple BI tools and teams.
  • Analytics adoption was low despite heavy investment in the underlying tools.
  • Users wasted significant time hunting for insights they knew existed somewhere.
Approach
  • Listened to users and reframed the problem: this was a discovery layer issue, not a dashboard quality issue.
  • Avoided the trap of rebuilding BI tools that already worked.
  • Focused on the access and navigation layer above existing tools.
Decisions & Trade-offs
  • Built an aggregation layer instead of replacing underlying BI infrastructure.
  • Introduced an AI assistant for natural-language navigation instead of more menus.
  • Made the platform itself opinionated about which dashboard to surface to whom.
Solution
  • Unified analytics hub with a single entry point for every team.
  • Role-based access so users see only what is relevant to them.
  • Search plus an AI chatbot for navigation across the dashboard catalog.
Impact
  • Time-to-insight reduced for end users.
  • Analytics tool usage increased measurably after launch.
Learnings
  • Initial search relevance was weak — improved by introducing structured tagging.
  • Onboarding mattered more than expected; users needed a guided first experience.
04
HR Tech · Talent Platform

CV Database

A first-principles redesign of the candidate database — structured ingestion, deduplication, and AI-powered search.

Problem
  • Candidate data was fragmented across sources, inboxes, and personal drives.
  • Duplicate profiles inflated the database and wasted recruiter time.
  • Recruiters were searching manually across systems to find existing talent.
Approach
  • Designed the system from first principles rather than retrofitting existing tools.
  • Anchored everything on structured data and searchability as the foundation.
  • Sequenced features so the foundation shipped before the AI layer.
Decisions & Trade-offs
  • Invested in deduplication early instead of treating it as cleanup later.
  • Held AI-based scoring out of the MVP to avoid distracting from core search quality.
  • Standardized the candidate schema before building UI on top of it.
Solution
  • Structured CV ingestion via API from multiple sources.
  • Deduplication engine that merges profiles using deterministic and fuzzy matching.
  • Advanced search with filters tuned to recruiter workflows.
  • AI recommendations for candidate-to-role fit, layered on after the core was stable.
Impact
  • Recruiter productivity improved on candidate discovery tasks.
  • Faster talent surfacing for open roles, especially repeat hires.
Learnings
  • Search UX needed deliberate simplification — early version exposed too many filters.
  • AI scoring required tuning against real recruiter feedback before it earned trust.
05
Analytics · Ecosystem

Analytics Central Ecosystem

Consolidated multiple BI tools into one ecosystem with opinionated modules — including an AI Incentive Planner that turned analytics into action.

Problem
  • Multiple BI tools coexisted with no unified experience or shared mental model.
  • Users had dashboards but lacked guidance on what to do with them.
Approach
  • Pursued a single ecosystem strategy rather than parallel best-of-breed tools.
  • Designed for guidance, not just visualization — what should the user do next?
Decisions & Trade-offs
  • Unified modules under a shared shell instead of preserving tool-specific UIs.
  • Built an AI Incentive Planner as the first guidance-led feature to prove the thesis.
Solution
  • Unified modules covering Incentive, KPI, and AI tools.
  • AI Incentive Planner that recommends actions, not just shows numbers.
Impact
  • Better decision visibility across leadership.
  • Increased usage of analytics tools after the consolidation.
Learnings
  • Users do not need more dashboards; they need guidance on what to do with the ones they have.
·
§ 03 · About

I work where business, ops, and engineering meet — and structured product thinking unlocks outsized impact.

I’m a Product Manager focused on solving high-friction operational problems across enterprise systems. Most of my work has been in HR Tech, analytics, and workflow automation — areas where the underlying processes are messy, the stakeholders are many, and the impact of getting it right compounds quickly.

What I enjoy most: taking ambiguity and converting it into a structured problem statement that a team can actually go build against. The frameworks matter less than the discipline of doing it out loud, with the right people in the room.

I optimize for outcomes — speed, efficiency, adoption — over feature count. Every project on this site can be summarized in a sentence about what changed for the user, not what we shipped.

Problem structuring Execution ownership Stakeholder alignment Building scalable systems
Skills
Product
  • Strategy
  • Roadmap
  • Prioritization
Execution
  • Delivery
  • Vendor management
  • Cross-functional coordination
Domain
  • HR Tech
  • Analytics
  • Workflow automation
Advanced
  • AI features
  • Enterprise UX
§ 04 · Achievements

Recognition for outcomes shipped.

§ 05 · Contact

Let’s build something with measurable impact.

I’m open to roles, collaborations, and structured conversations about ambiguous product problems. The fastest way to reach me is email.

Email
swapniltiwari150195@outlook.com
LinkedIn
/in/swapnil-tiwari95

I build products that reduce operational complexity and drive measurable business outcomes.