I work beneath the interface—turning messy workflows, ambiguous rules, fragmented data and physical-world constraints into operating models that teams can build and organisations can run.
Production product with observed operational improvement.
Project 02Price & Market Intelligence Platform
A data intelligence system that turns unstable, fragmented e-commerce sources into trusted competitive market information.
01AmbiguityWhat is tangled?
The same product appears with different titles, images, variants, sellers and languages.
02Product logicWhat must become explicit?
Confidence decides: automate, review or re-check.
03Working systemWhat does the product do?
Crawl → normalise → match → human review → quality control.
04EvidenceHow do we know?
40 sites · 10 countries · 200k product records · 20 customers.
Project 04Multi-Campus Asset Management System
An operational system for asset identity, ownership, location, lifecycle status and accountable transfers across campuses.
01AmbiguityWhat is tangled?
Asset identity, status, location and responsibility scattered across spreadsheets.
02Product logicWhat must become explicit?
Status, location and responsibility are independent dimensions.
03Working systemWhat does the product do?
Barcode → assign → verify → transfer → receive → lifecycle history.
04EvidenceHow do we know?
Organisation-wide production system rolled out after pilot.
System ExplainerPortfolio-created model · not product UI
Created to explain the underlying product model — not an original product UI.
0→1 products•Enterprise workflows•AI + human judgement•Data operations•Connected products•Multi-campus systems
01 Product thesis
The interface is only the visible layer
I design the operating logic that lets a product know, act, decide and prove.
A feature request often hides a harder system problem: unclear ownership, inconsistent data, unmodelled exceptions or decisions that should not be fully automated. My job is to expose that logic and make the trade-offs executable.
01
Record
What is true?
The state, history and ownership the organisation must be able to trust.
→
02
Action
What can happen next?
Permissions, transitions, dependencies and exceptions that shape user action.
→
03
Judgement
What needs interpretation?
Where rules should decide, where confidence matters and where people stay in the loop.
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04
Evidence
What proves it works?
Operational signals, product outcomes and boundaries between observation and claim.
Different products emphasise different layers. The discipline is making all four visible before the interface hides them again.
02 Featured projects
Depth before breadth
Two projects that best show how I think.
These project cases reveal the product model beneath the interface: the entities, decisions, workflows and evidence that make the system coherent.
System ExplainerPortfolio-created system model · not product UI
Transformed fragmented e-commerce data into trusted competitive pricing and market intelligence across a multi-stage automated and human-reviewed pipeline.
03 Product range
Different constraints, same product discipline
Software, operations, data and the physical world.
Led discovery, information architecture and delivery for three distinct enterprise portals spanning knowledge, operations and global rotation programmes.
System ExplainerPortfolio-created system model · not product UI
Turned fragmented spreadsheet records and asset policies into an organisation-wide system of record for registration, assignment, transfer, verification, repair and lifecycle history.
Modelled donor relationships, overlapping funding cycles, student associations, extensions and outcomes as one operational system for partnerships, campuses and leadership.
Project 06i-calQ / ELADO / Activgard2013–2014
Adapting a smartphone diagnostic platform for India
Owned product vision, roadmap and beta delivery for a two-sided consumer platform combining visual polls, communities, campaigns and structured brand signals.
04 Independent product builds
Self-initiated products, taken beyond the concept
Small enough to build directly. Serious enough to expose real product decisions.
These products were initiated outside formal client or organisational mandates. They test product strategy, workflow design, technical architecture, usability, packaging and release execution in one continuous loop.
A desktop product that scans a music library, builds explainable duplicate candidates and helps the user resolve them through a safe quarantine workflow rather than irreversible deletion.
Current stateRelease candidate packaged for WindowsPackaged release candidate
02Student planning system · workload and study operations
StudyOps
A student operations prototype that connects what must be learned with when the student is actually available, what is already complete, what remains uncertain and what deserves attention next.
An interactive digital companion to AI Workflow Foundations, turning a downloadable workbook into a stateful web product with guided completion, saved inputs, export behaviour and privacy-aware pilot feedback.
05 Product artifacts
The work behind the work
A case tells the story. An artifact exposes the reasoning.
PRDs, workflows, state models, rollout plans and evaluation frameworks are where product thinking becomes executable—and where assumptions become reviewable.
Product implicationA laptop can be Active, assigned to an employee and physically remote at the same time. Collapsing these dimensions destroys the operating model.
Ideas that became products. Product practice that became writing.
I write to turn working practice into something other people can inspect, exercise and use—from AI product workflows to the mathematics behind machine learning.
A structured mathematics reference for machine-learning learners, bringing core mathematical foundations and practice into one long-form digital volume.
I work where product strategy meets operating reality.
My work spans enterprise workflow platforms, AI-assisted decision systems, consumer products, medtech localisation and connected hardware. Across those environments, I have repeatedly taken ambiguous operational problems and turned them into product models that teams could build, test and run.
Shashikant SethyProduct Manager · Systems thinker · Builder India
I am most useful when the interface is only a small part of the problem—when the product also depends on policy, data quality, physical processes, human judgement, integrations or organisational change.
I work comfortably across discovery, product strategy, information architecture, requirements, delivery, rollout and evidence. The objective is not to produce more documentation; it is to create enough shared clarity for the right product decisions to happen.
2010 → nowProduct work across changing technology eras and operating contexts.0→1 → productionFrom problem framing and prototypes through rollout, adoption and operating reality.Software ↔ physicalEnterprise platforms, AI/data systems, mobile products, diagnostics and connected hardware.
01
Make the model explicit
Clarify entities, states, rules, ownership and exceptions before the interface obscures them.
02
Keep judgement visible
Use automation where it creates leverage, and preserve human responsibility where uncertainty matters.
03
Connect delivery to evidence
Define what changed, what remains uncertain and what the available evidence can honestly support.
Product strategy0→1Workflow systemsAI-assisted productsB2B SaaSInternal platformsConnected productsUX systems
09 Ways to collaborate
Engagements organised around the problem
Three situations where this product practice creates leverage.
These are not service packages. They are common entry points for conversations about difficult product systems.
01
Build a new product system
Shape a zero-to-one product where the workflow, rules, data model and operating model still need to become explicit.
02
Untangle an existing platform
Reframe a product whose complexity has accumulated across teams, policies, integrations, users or operational exceptions.
03
Strengthen product execution
Turn product intent into requirements, state models, release slices, QA priorities and evidence that delivery teams can use.
09 Continue the conversation
Continue the conversation
Shashikant Sethy
Product Manager · Systems thinker · Builder
I work across enterprise workflows, AI-assisted products, operational platforms,
mobile systems and connected hardware.
Mention the portfolio when you connect so I have context for the conversation.
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Start with the problem
Have a product problem that refuses to stay inside the screen?
Complex workflows. Operational systems. AI-assisted products. Connected products. I am interested in conversations where the real work is clarifying how the whole system should behave.
