Product-minded builder crafting
clear systems and prototypes
Around 3 years across consulting, startup work, and product-facing operations, working on workflow design, documentation, automation, and prototype-led problem solving alongside product and engineering teams.
At EMELEX Business Solutions Pvt Ltd, I work across the PI DOT product wing and AXIOM Creative Studio, helping convert founder-led ideas into product direction, client-aware workflows, and repeatable systems.
Around 3 years of real work across product operations, workflows, automation, and fast-moving environments. Available immediately. Open to full-time, contract, and remote roles globally.
Product thinker. Systems builder. I take messy business problems and turn them into workflows, docs, prototypes, and clear execution plans.
I'm Vidur Ramachandran, a product-oriented founder's office operator with around 3 years across consulting, startup work, product operations, and automation-heavy workflows.
At Deloitte USI, I worked on enterprise CDP systems for Fortune 500 clients, building the discipline to investigate systems, document failure patterns, and coordinate fixes. Before that, at Acumen Connect, I handled product and operations tasks in a fast-paced startup environment.
Now at EMELEX Business Solutions Pvt Ltd, I support PI DOT product development with customer/client POV inputs while also assisting AXIOM Creative Studio across marketing-agency workflows.
Hyderabad, India
Open to remote & relocation
B1/B2 US Business Visa — valid · 10-year
Comfortable with EST / PST-aligned shifts & overnight hours
A curated product lab across portfolio concepts, client-facing execution, and personal rebuilding. The common thread is simple: understand the system, shape the workflow, build proof.
Labs is the proof-of-work layer behind vidur.life: product ideas, freelance systems, and personal rebuilds that show how I think through ambiguity, workflows, and execution.
A professional-grade, AI-powered corporate simulation platform that bridges the gap between campus life and the corporate world — built as a working prototype to give shape to a founder’s vision for what AI-native corporate readiness training could be.
EMELEX Solutions is building the next generation of corporate readiness training. Inspired by the founder’s vision, I wanted to build a demo prototype for him — my own understanding of what he was trying to create, translated into something tangible and explorable.
This prototype is something I built independently, thinking like a product person: mapping the user journey end-to-end, defining the information architecture, prototyping the core experience, and documenting how it all fits together. It’s not a final product — it’s a high-fidelity signal of what this kind of product could feel and function like.
The VDE is a multi-app interface containing everything a junior professional would need on day one. Users don’t “answer questions” — they actually work inside these tools simultaneously.
Intelligent email client where users manage AI-generated stakeholder communications. Claude responds dynamically to tone, clarity, and analytical depth.
Real-time chat simulation for team collaboration, plus a Kanban system for managing deliverables — mirroring actual project workflows.
Professional document editor for BRDs, specs, and reports — plus a video call simulator for stakeholder presentations and stand-ups.
Analytics and scheduling tools to round out the experience — modelling real analyst and PM workflows.
Proprietary 8-pillar scoring: Communication, Analytical Thinking, Urgency, Stakeholder Management, and more — updated in real-time as you work.
AI Engine: Claude/Anthropic generates dynamic content (emails, documents) and provides objective managerial grading on all user outputs. The AI acts as your manager, colleague, and evaluator simultaneously.
User enters through a professional portal and browses a library of Experience Templates — e.g. “Business Analyst at JPMorgan” or “Risk Analysis at McKinsey”. Each shows difficulty, skills practiced, and partner certifications available.
User is dropped into their Virtual Desktop. An onboarding email arrives in InboxAI. A welcome message appears on SlackSim from their AI manager. Then the problem lands: “Our regulatory reporting system is failing — we need a BRD by Wednesday.”
Draft requirements in DocVault. Analyse data in DataStudio. Track progress on the TaskBoard. Schedule stakeholder syncs in TeamCalendar. AI stakeholders respond dynamically to tone and clarity — CRS updates in real-time.
Halfway through, the AI triggers a disruption — a stakeholder change, a data error, a shifting deadline. How you prioritise under pressure directly impacts your CRS score.
The journey culminates in a simulated video call where the user presents findings to a panel of AI stakeholders — practising verbal communication and executive presence.
A comprehensive debrief breaks down Professionalism, Urgency, Analytical Thinking, Communication, and more. Pass the threshold and earn a verified digital certificate shareable on LinkedIn or with partner recruiters.
Journey: Landing → Dashboard → Catalog → Virtual Work (VDE) → Real-time Feedback → Certified CRS Score
Design aesthetic: Premium B2B SaaS — clean light theme, sophisticated slate-gray accents, high-readability typography. Deliberately moved away from typical educational platform aesthetics toward something closer to Linear or Notion.
This is a prototype — some flows may be incomplete. The goal is to convey the product vision and architecture, not production-ready polish.
Lightweight people operations for early-stage startups — onboarding workflows, doc management, and headcount planning without enterprise overhead.
At Acumen Connect, I watched a 15-person startup collapse its own onboarding under Notion chaos. Policies were scattered, contracts were tracked in emails, and nobody was sure who'd signed what. I spent weeks converting that into documented SOPs — and the improvement was immediate.
That experience — combined with my BA work at Deloitte around process reliability — made me realise the problem is structural, not individual. Startups don't fail at HR because they're careless. They fail because the tools available are either too heavy or too unstructured. This is a systems problem, and it's one I know how to scope.
They're spending 3–4 hours a week on HR admin that should take 20 minutes. Not because the tasks are hard — because there's no system. Every new hire means re-inventing the onboarding checklist. Every policy update means chasing people on Slack. They need structure, not software complexity.
Design constraints — deliberately out of scope
Payroll processing is a compliance minefield. Integrate with Razorpay or Zoho Payroll instead.
Goal-setting and review cycles are culturally loaded. Keep it out of v1.
Attendance punching belongs in factories. Early-stage startups run on trust.
Stack rationale: Supabase gives a production-grade backend with zero infra ops. n8n keeps automation logic visible and modifiable without code changes. The goal is a 2-person team being able to ship and maintain this.
Step-by-step clinic automation concept for medicine reminders, voice-note triage, doctor escalation, and pharmacy refill delivery using n8n, Telegram, Google Sheets, and Groq.
ClinicBot is designed as a practical, low-cost care follow-up system for small clinics. It reminds patients to take medicines, listens to replies in text or voice, flags risky symptoms, and helps clinics coordinate pharmacy refill delivery without adding admin burden to doctors.
The interaction model is intentionally simple for elderly patients: they receive prompts in their preferred language, reply with short text or voice notes, and get timely support when something feels wrong.
Patients, Medicines, CheckIns, Orders, Pharmacies, and CheckUps. Structure is designed so non-technical clinic staff can update records directly in spreadsheet format while workflows run automatically in the background.
Stack rationale: every core piece has a free starting path, setup is reproducible by a non-enterprise clinic, and operations remain transparent because data and workflow state stay human-auditable.
A behavior-first financial assistant that uses "Judgmental AI" to bridge the gap between earning well and building a legacy. Not a tracker — a financial Tamagotchi for adults.
Most personal finance apps are passive trackers. They show you pretty graphs of where your money went, but they don't help you change your behaviour in the moment. The result is the "Financial Ostrich Effect" — people avoid their own bank balances because opening the app feels like guilt, not growth.
I'm building RoastMoney because the emotional layer is completely missing from fintech. Behavioural economics tells us that friction and social cost are the most effective ways to interrupt impulse spending — but no one has built that in a way that's actually fun to use.
They aren't broke — they're "fine." That's exactly the problem. Being "fine" is the enemy of being wealthy. They earn decently, spend without tracking, and avoid their balance because looking at it feels bad. They don't need a budgeting lecture; they need something that makes finance feel like it matters today, not in retirement.
Converts dry transaction data into witty, hard-hitting commentary that actually sticks in your mind. The AI doesn't just tell you what happened — it gives you the emotional context your bank never would.
Translates today's spending into its compounded future cost. A ₹300 daily coffee becomes ₹6,000+ per month in opportunity cost — visible, concrete, impossible to ignore.
Clubs multiple credit cards into a single chronological due-date timeline. No more hidden dues, no more missed payments ruining your credit score.
Chat with your money instead of searching for answers. Ask "Can I afford that iPhone if I want to hit my SIP goal?" and get a data-backed answer, not just feelings.
The Avatar is RoastMoney's standout differentiator — a Ditto-Baymax hybrid character (Marshmallow) that reacts physically to your spending in real time. Overspend and it gets visibly angry. Hit a savings goal and it celebrates with you.
Behavioural economics confirms this works: users don't just see a number go down — they feel like they're letting their AI companion down. This is the "Emotional Friction" moat that makes RoastMoney a coach, not just a spreadsheet.
Vision: To become the world's first financial app that users are "scared" to disappoint — resulting in the highest savings-rate-per-user in the industry.
Early prototype — core flows demonstrated: Roast Engine · Pay Hub · Money Time Machine · AI Chat Modal
An AI-powered idea validation platform that goes deeper than a basic search — analysing what people actually complain about across Reddit, LinkedIn, forums, and the open web to help you find real gaps before you build.
This is an early-stage prototype built to demonstrate the core idea — not a finished product. The current version runs on a Gemini API key with limited quota, which means some analyses may be throttled or incomplete in the live demo.
When deployed properly with a production-grade API key and full data connectors (Reddit API, LinkedIn data, web scraping pipelines), the platform will deliver richer, real-time multi-source intelligence. This prototype is a proof of concept — the architecture and UX are real, the data depth will scale.
Enter a plain-language description of your idea. Then select your industry (e.g. FinTech, HR Tech, EdTech), sector (B2B SaaS, Consumer App, Marketplace), and target audience (e.g. solo founders, enterprise ops teams, students). These selectors focus the analysis so you don’t get generic results.
The platform runs queries across Reddit threads, LinkedIn discussions, product review sites, and the open web to find: what people are saying about this problem space, which companies already exist, and — critically — what users complain those companies are getting wrong.
A structured report covering: existing competitors and their gaps, recurring user frustrations in the space, signals of unsolved demand, and a positioning recommendation for how your idea can differentiate. Actionable — not just a list of links.
Unlike asking ChatGPT, this platform is structured around searching live sources — not generating answers from training data. The goal is to give founders information that feels like they did 10 hours of research themselves.
Current limitation: The Gemini API key used in the prototype has rate limits and quota caps, so deep multi-source analysis may be throttled. This is a demo of the product vision — full deployment requires a production API setup and dedicated data pipeline infrastructure.
This is a prototype — built to prove the concept and explore the product architecture. Not production-ready, but the core idea is real.
A rule-based pipeline that automatically cleans transaction data, detects anomalies, and generates daily risk summaries — replacing manual spreadsheet review entirely.
Real-time transaction data arrives every day. Manually checking it is slow, inconsistent, and error-prone — analysts miss patterns that only become visible in aggregate.
This pipeline runs automatically when new data arrives: cleaning it, identifying what's unusual, and producing a structured report — with no manual intervention required after initial setup.
Prepares raw data to ensure it's consistent and safe for analysis. Handles the messy reality of real transaction files.
# 1. Load raw data df = pd.read_csv('../data/transactions.csv') # 2. Standardize column names df.columns = df.columns.str.lower().str.strip() # 3. Remove exact duplicate rows df = df.drop_duplicates() # 4. Convert date column to datetime df['date'] = pd.to_datetime(df['date'], errors='coerce') # 5. Handle missing or invalid amounts df['amount'] = pd.to_numeric(df['amount'], errors='coerce') df['amount_was_missing'] = df['amount'].isna() median_amount = df['amount'].median() df['amount'] = df['amount'].fillna(median_amount) # 6. Remove transactions with missing critical identifiers df = df.dropna(subset=['transaction_id', 'customer_id', 'merchant']) # 7. Remove negative or zero-value transactions df = df[df['amount'] > 0] # 8. Sort for consistency (audit-friendly) df = df.sort_values(by=['customer_id', 'date']) # 9. Save cleaned data df.to_csv('../output/cleaned_data.csv', index=False)
Identifies transactions that don't match a customer's normal spending behavior. Three independent rules — any one hit triggers a flag.
Transaction exceeds 2× the customer's average historical spend. Personalized baseline per customer_id.
Transaction amount exceeds ₹20,000 threshold regardless of customer history.
Merchant appears on a configurable high-risk list. Combined with Rule 2 for a final anomaly flag.
# 1. Load cleaned data df = pd.read_csv('../output/cleaned_data.csv') # 2. Customer-level baseline avg_spend = df.groupby('customer_id')['amount'].mean() df['avg_customer_spend'] = df['customer_id'].map(avg_spend) # 3. Rule 1: Spend spike anomaly df['spend_anomaly'] = df['amount'] > (2 * df['avg_customer_spend']) # 4. Rule 2: High absolute transaction value df['high_value_anomaly'] = df['amount'] > 20000 # 5. Rule 3: High-risk merchant high_risk_merchants = ['Apple', 'Dell'] df['merchant_risk'] = df['merchant'].isin(high_risk_merchants) # 6. Final anomaly flag (company logic) df['final_anomaly'] = ( df['spend_anomaly'] | (df['high_value_anomaly'] & df['merchant_risk']) ) # 7. Save outputs df[df['final_anomaly']].to_csv('../output/anomalies.csv', index=False)
Converts raw anomaly data into a clear, actionable daily summary for business and risk teams.
# 1. Load anomaly data df = pd.read_csv('../output/anomalies.csv') # 2. Handle case where no anomalies exist if df.empty: print("No anomalies detected for this period.") else: # 3. Summary metrics total_anomalies = len(df) unique_customers = df['customer_id'].nunique() total_amount = df['amount'].sum() avg_anomaly_amount = df['amount'].mean() print(f"Total flagged transactions : {total_anomalies}") print(f"Unique customers impacted : {unique_customers}") print(f"Total amount at risk (₹) : {total_amount:,.2f}") print(f"Average anomaly amount : {avg_anomaly_amount:,.2f}") # 4. Top risky merchants print("\nTop merchants by anomaly count:") print(df['merchant'].value_counts().head()) # 5. Top customers by total anomaly spend print("\nTop customers by anomaly spend:") print( df.groupby('customer_id')['amount'] .sum() .sort_values(ascending=False) .head() ) # 6. Save daily summary summary_df = pd.DataFrame([{ 'total_anomalies': total_anomalies, 'unique_customers': unique_customers, 'total_amount_at_risk': total_amount, 'average_anomaly_amount': avg_anomaly_amount }]) summary_df.to_csv('../output/daily_anomaly_summary.csv', index=False)
Design rationale: Vanilla Python and pandas keeps the system readable, auditable, and easy to hand off. No framework dependencies means nothing breaks when an external library updates. The modular structure lets each script be tested, replaced, or extended independently.