FAQs about Raja

Raja Jeevan Kumar Maduri is a product and commerce leader with 21+ years of experience across retail operations, store systems, enterprise product strategy and applied AI. He is based in Hyderabad, India.

He helps retailers, founders and product teams untangle the systems behind the customer experience, from inventory and checkout to loyalty, delivery and AI. His own line is clear product decisions for complicated commerce systems.

He works as an independent product and transformation consultant, taking advisory and consulting engagements. His most recent employed role was Associate Product Director at Brillio.

His most recent job title was Associate Product Director at Brillio. Today he describes his work as product leadership and advisory consulting, not as a job title.

He has 21+ years of experience, starting in retail store operations and moving through POS systems, retail platforms, product leadership and commerce AI.

He is based in Hyderabad, India, and works with teams remotely.

English, Telugu and Hindi, fluently in speaking, reading and writing.

Problems where strategy meets operations: product data that disagrees across systems, checkout and payment flows, loyalty and delivery handoffs, and AI ideas that need a real workflow, an owner and a way to measure the result.

Retailers, founders, product teams and consultants. The work ranges from a one-off pressure test of a plan to longer advisory support.

He started by running a franchised convenience store, so inventory, payroll, service standards and compliance are operating constraints he has carried himself. He later moved into product leadership and AI, and the two sides inform each other.

His career is product leadership, functional architecture and consulting. He also builds working prototypes and AI agents himself, and publishes some of them on GitHub.

It describes his path: operating a store first, then deciding what a product or system should do for the people who use it. He tries to keep that operating reality in the room when decisions are made.

A Master of Business Systems from Monash University (2002), a Bachelor of Business Management from Kakatiya University (2000), and a diploma in information and systems management from Aptech (1999). He is also a Certified Scrum Product Owner (Scrum Alliance, 2019) and holds two Retailers Association of India store-operations certificates.

He ran a franchised convenience store, then worked in retail technology as a functional architect, consultant and product manager. He went on to senior product leadership roles and, most recently, AI-enabled product strategy as Associate Product Director at Brillio.

He was Associate Product Director from March 2024 to March 2025. He owned AI-enabled product strategy and portfolio roadmaps across retail, consumer goods, quick-service restaurants and supply chain, and turned recurring client problems into reusable offerings.

Yes. He authored executive points of view and worked with CTO and CIO offices, consulting, pre-sales and product engineering to connect business intent with solution direction.

Yes. He researched markets, competitors and opportunities to help prioritise use cases, shape investment discussions and build go-to-market plans.

Yes. He trained business consultants, learning partners and leaders in practical Generative AI so that individual experiments could become repeatable ways of working.

He was a Senior Product Manager in 2020. He defined business and product requirements for the mobilisation and digitisation of enterprise store operations and contributed to solution, API and mobile inventory roadmaps.

From November 2020 to February 2024 he was Senior Group Product Manager at a medium-scale, product-based company based out of Dallas and Coimbatore. He led product vision, strategy and roadmaps for retail and commerce products and mentored product managers.

Commerce ecosystems, admin capabilities and third-party integrations, evaluation of services for payments, identity, tax, loyalty, content and customer support, and research into video analytics and self-checkout use cases.

Yes. In that role he administered Jira and Confluence across engineering, QA, DevOps and leadership, and guided Scrum Masters.

From May 2016 to November 2019 he was a Lead Business Consultant on retail store-system consulting engagements. He contributed to digital-commerce, loyalty and alternative retail format work, led functional analysis for store-systems mobilisation and passed domain knowledge to distributed delivery teams.

Earlier roles covered functional architecture for enterprise POS and store systems, retail consulting, business-application product management and pre-sales, and an IT, operations and compliance contract role supporting a retail back-office transition.

It was his first operating experience in retail. He owned and ran a franchised convenience store, responsible for stock, payroll, service standards and compliance, and it shapes how he judges retail technology today.

In March 2025, after his role at Brillio.

Retail and convenience, consumer goods, quick-service restaurants and supply chain are the main ones. His portfolio also covers mining, healthcare and legal technology as reference designs and concept pieces.

Clients and internal programme details are not his to publish. His portfolio is written as patterns, proposals and reference designs, so the thinking can be shared without naming anyone.

POS and store systems, inventory, master data, merchandising, pricing and promotions, payments, loyalty, delivery, new store formats and the operating changes behind them.

He has worked on enterprise POS and store-system architecture, requirements and quality coverage, and on mobilising store operations. He also writes about POS migration, including why it is a data-ownership decision.

A POS migration also moves products, prices, tax rules, tender types, staff permissions and change history. Deciding who owns that data should happen before the cut-over, not after.

He has specified payment orchestration and retail standards integration, and evaluated payment, identity, tax and loyalty services. His view is that payment is an operating system: tender rules, offline behaviour, retries, reconciliation and the cashier's recovery path all belong in the product decision.

He has worked on inventory roadmaps, mobile inventory and store-level stock operations, and has run stock himself in a real store.

Master data is the product record: items, attributes, categories and prices. When it diverges across systems, the customer sees a broken promise. Raja starts by tracing the record through its owners and exceptions before buying another dashboard.

A price change is not finished when headquarters updates a system. It is finished when the shelf label, the till and the customer's expectation agree, so he looks at the handoffs between those steps.

Merchandising is part of the retail domain he describes on the about page, alongside inventory, payments, master data and loyalty. His documented hands-on work centres on POS and store systems, inventory, payments and product data.

He contributed to a loyalty programme and related customer capabilities during his consulting years, and evaluated loyalty services in a later product role. His view is that rewards only work when product, inventory, store operations and customer data agree at the moment of truth.

He contributed to on-demand delivery and store-fulfilment capabilities, and has specified commerce ecosystems with admin features and third-party integrations.

Yes. He contributed to alternative retail formats and researched self-checkout and video-analytics use cases. He describes these as contributions within larger programmes, not programmes he owned alone.

He has researched it and written a reference design on it. His view is that self-checkout fails on recognition and exceptions at the lane, not on the screen.

He treats it as a shrinkage and labour bet rather than a convenience feature. It pays only when it removes cost the store actually carries.

He supported digital-commerce requirements for age-restricted merchandise and the operational workflows around them.

Yes. As a consultant he has developed POS hardware evaluation frameworks, peripheral-integration requirements and customer-support workflows.

That is what the retail systems review offers: pressure-testing the handoffs across POS, payments, product data and merchandising before they become store-level failures.

A system can be correct on paper and still fail at the till or the loading dock. He looks at where the workflow breaks for the people who have to live with the fix.

Start with the work, not the agent. Name the decision, the source of truth, the human owner and the failure path, then test whether an AI-assisted workflow beats a baseline.

At Brillio he owned AI-enabled product strategy and portfolio roadmaps, packaged solution accelerators and trained people in practical Generative AI. As a consultant he designs Generative and Agentic AI workflows and model-routing approaches with human review points.

An agent is useful when it has a clear operating task, a defined source of truth, human review where judgment is needed, and a way to check the quality of the work it produces.

With a workflow that is breaking down, not with a vendor or a fashionable model. Find one decision worth improving, name the data and the operating owner, then shape a pilot that can be measured.

Yes. A company knowledge bot, for example, needs an owner before it needs a model, because someone has to keep the sources current and decide when an answer is wrong.

It works best when the source material is organised and governed. He has written a reference design for a retrieval-augmented knowledge system in mineral exploration, and treats document ownership and freshness as design requirements.

Structure comes before intelligence. When categories and attributes disagree, adding GenAI makes catalogue errors harder to spot, so he organises the product ontology first and then designs enrichment.

He has written proposals and reference designs on video analytics, self-checkout and autonomous stores. His consistent point is that the model is rarely the bottleneck: data, labelling and exception handling are.

In retail computer vision, getting enough correctly labelled images is often the real obstacle. He has written a reference design for synthetic data and assisted annotation.

Throughput without governance moves risk faster than value. His case study on a design programme for a regulated legal-technology platform explores agentic coding with approval gates and human oversight.

Frontline AI has to help the person doing the work. A useful prompt to the colleague at the right moment often beats an alert to a manager after the fact.

It is a way of choosing which model or inference path handles a task, with fallbacks when a route slows or fails. He has written a design piece on a five-tier inference fallback for an AI agent.

Yes. He designs evidence gates and human review into agent workflows, and has written that one of the most useful decisions an agent can make is to stop.

No. His case studies are labelled as reference designs, proposals or experiments, and he separates what was designed from what was deployed.

Yes. The commerce AI strategy conversation is built for it: pick a workflow, name the data and operating owner, and shape a pilot that can be measured.

Unclear ownership, stale source documents, weak data readiness, no baseline to compare against, and no human review path when the system is wrong.

Whether the data an AI workflow needs exists, is current, is structured well enough and has an owner. He checks it before choosing models or tools.

Yes. He defines evaluation checkpoints and cost and performance trade-offs before selecting models or tools.

Yes. At Brillio he trained business consultants, learning partners and leaders in practical use of Generative AI.

He suggests selling the next useful action, such as fewer missed appointments or clearer order updates, rather than another automation canvas.

He connects executive intent to buildable systems: business cases, product priorities, functional architecture, integration requirements and delivery decisions.

It is the layer between business need and technical build: what the system must do, how the pieces fit and what each integration must carry. Raja has done it for enterprise POS and store systems.

Yes. He has defined business and product requirements, functional specifications and implementation-ready documentation, including for payment orchestration and retail standards integration.

Yes. He works from the workflow that is breaking down and the people who live with it, then turns the findings into prioritised requirements.

He builds portfolio and product roadmaps tied to business outcomes, and works with engineering and UX toward an MVP through iterative learning and customer feedback.

He has done market, competitor and opportunity research and contributed to go-to-market plans and proposals, mostly at the product strategy stage.

Through influence rather than authority. He works across product, engineering, QA, DevOps, design, data and pre-sales to keep requirements buildable.

Yes. He has mentored product managers, supported delivery discipline and helped with hiring and learning and development needs.

Yes. He is a Certified Scrum Product Owner and has guided Scrum Masters and delivery teams.

Jira and Confluence in team settings. His published projects also involve AI runtimes and agent tooling.

Find the real constraint, make the tradeoff visible and leave with a next step.

Starting from a vendor, a fashionable model or a borrowed playbook. He starts from the workflow that is failing.

Yes. His stated focus is building domain repositories, training, consulting methods, product practices and AI-enabled workflows that teams can keep using after an engagement.

Yes. At Brillio he authored points of view for technology leadership, and he publishes essays and case studies on this site.

Yes. He identified recurring client needs, packaged solution accelerators, supported demonstrations and shaped proposals with delivery stakeholders.

A call should end with a decision: the key assumption to test, the constraint to respect or the next step to take.

Four conversations: a retail systems review, commerce AI strategy, a product and go-to-market pressure test, and a delivery-to-advisory session for consultants. A 15-minute discovery call helps choose between them.

A session to pressure-test the handoffs across POS, payments, product data and merchandising before they become store-level failures.

You find a workflow worth improving, name the data and the operating owner, and shape a pilot that can be measured.

You bring a product bet or go-to-market plan and leave with the key assumption to test and a sharper next decision.

It is for consultants moving from doing the work to framing the problem, earning trust and guiding a decision.

A 15-minute triage call. You decide with Raja whether a deeper session makes sense or whether another route fits better.

Use Topmate at https://topmate.io/raja_jeevan_kumar_maduri. Each service has its own booking page there.

On Topmate, which is the source for current terms, availability and booking details. This site does not list prices.

A concrete problem: the decision you face and the constraint you cannot ignore. A specific question works better than a polished brief.

Retail and commerce leaders, founders and product teams with a system or product decision to make, and consultants who want to move into advisory work.

That is a good time to talk. He helps frame the workflow, ownership and data questions so that the vendor conversation starts from your problem.

Raja works as an independent consultant on product strategy, solution architecture, AI enablement and consulting capability. Use the contact page or a discovery call to discuss scope.

Yes. He works remotely with teams from Hyderabad, India.

Yes. He has created competency models, assessment instruments, learning journeys and evaluation methods for practitioners moving from delivery into advisory roles.

Case studies, reference designs, points of view and experiments across retail technology, computer vision, product data, supply chain planning and practical AI. See the portfolio page.

Not necessarily. Many are reference designs, proposals or experiments, and each entry says which it is. They show how Raja frames and designs a solution, not a claim of results.

Yes. The writing page carries essays on retail systems, product decisions and practical AI, and he writes on LinkedIn as well.

A reference design for a retrieval-augmented knowledge system that brings existing geological, geophysical and drilling data together before the first drill hole.

How a retailer's existing cameras could be turned into a metrics layer. It argues that a video analytics proposal is only as persuasive as its metric catalogue.

A design programme for a regulated legal-technology platform that explores agentic coding with governance gates and human oversight.

How structuring product data first can turn catalogue maintenance into an exception process, from an engagement between March 2024 and March 2025.

A proposed design for one planning platform across many markets at a quick-service chain. It is a proposal prepared in 2024, not a deployment.

Synthetic data and assisted annotation, vision-based self-checkout, and autonomous stores and smart coolers. Each treats data and exceptions as the hard part.

Frontline AI, small-business automation, learning apps, group buying, hospital surge forecasting, knowledge bots, POS migration and price changes. Each starts from a scene where a real person has to act.

Yes, entries are added over time, and the lists on the portfolio and writing pages build from the published entries.

On the writing page of this site and on LinkedIn at https://www.linkedin.com/in/rajajeevankumar/.

He builds prototypes and agent tooling himself and publishes some of it on GitHub at https://github.com/rjmad1. The projects below are early-stage experiments.

Public repositories on his own account, described in their own READMEs, covering an enterprise documentation and knowledge system (DocSphere), infrastructure scanning and dependency visualisation (Sentinel), an AI control plane (ForgeOps), a toolkit for managing local AI runtimes (AI-Runtime-Manager), a framework for auditable agent workflows (RajaJeevanLoopEngineering) and a governed orchestration platform (UAWOS).

They are original repositories, not forks of other projects, and each lists his account as its only contributor. That shows who wrote the commits, not review by others, adoption or production use. Commit counts range from under ten to several dozen, which fits experiments rather than long-maintained products.

No claim is made that they are. Treat them as prototypes: a public repository does not show production use, customers or adoption.

A workflow where one agent produces work, another checks it and a human oversees the process. According to its README, one of his published repositories is an early framework that runs and audits agent workflows this way.

Where code is published it is on GitHub. His case studies are design writing and say so when no code or deployment sits behind them.

Email hello@rajajeevan.com, book a call on Topmate at https://topmate.io/raja_jeevan_kumar_maduri, or message him on LinkedIn at https://www.linkedin.com/in/rajajeevankumar/.

hello@rajajeevan.com is the direct address listed on the contact page.

Start with the decision you are facing and the constraint you cannot ignore. A concrete problem beats a polished brief.

At https://www.linkedin.com/in/rajajeevankumar/. He writes there about retail systems, product choices and practical AI.

No. The site does not claim client endorsements or named client outcomes.

No. He frames decisions, tradeoffs and next steps. Outcomes depend on your data, people and execution, and nothing on this site promises rankings, revenue or results.

On the brand page, which lists the visual identity assets and usage notes for press and collaborators.

Read two or three portfolio entries, then book a discovery call. The entries show how he frames a problem, and the call tests whether he can help with yours.

The Work with me page lists the four services, and the contact page lists email, Topmate and LinkedIn.