Multi-Channel AI Messaging Platform
A prototype messaging backend that normalizes channel operations behind adapters and moves message work through Redis-backed queues.
DHIRENDRAYADAV.
MISSION BRIEF / 00
I build security-first platforms, intelligent automation, and useful AI systems that move from prototype to production.
Selected work
01 / WORK
Selected security, automation, and product engineering projects shaped by security thinking and practical delivery.
NEPSE Market Intelligence
A research system connecting price ingestion, technical indicators, news sentiment, ensemble prediction, backtesting, an API, and a dashboard.
Secure Portfolio Platform
A Flask portfolio and content-management application with database fallbacks, contact protections, response headers, and public-page contract tests.
RunPod Media Orchestrator
Prototype RunPod workflow for provisioning GPU compute, transferring prompts, running media jobs, retrieving outputs, and tearing down resources.
Hotmail Automation
A prototype workflow concept for organizing inbox triage, message templates, and operator-approved follow-up actions.
ERP System
A prototype enterprise resource planning concept connecting inventory, purchasing, sales, and operational reporting.
School Management
A prototype school operations concept for student records, classes, attendance, communication, and progress views.
Restaurant Management
A prototype restaurant operations concept connecting menus, tables, orders, kitchen flow, stock, and daily reporting.
Attendance Management
A prototype attendance concept for check-in records, approvals, schedule context, and role-aware reporting.
Accounting Software
A prototype accounting product concept for chart of accounts, journal entries, reconciliation, and review states.
Billing Software
A prototype billing concept for customer accounts, invoices, line items, payment status, and operator review.
Staff Management System
A prototype staff operations concept for profiles, roles, leave, attendance context, and manager workflows.
01B / FIELDWORK PROTOCOL
How to read the work.
Every case file separates what exists, what it demonstrates, and what still needs hardening. The goal is a portfolio that can be inspected rather than a gallery of unsupported claims.
Claim → source → boundary.
Project statements are connected to source records, schemas, tests, captures, or reports. Constraints remain visible so a prototype is never presented as a production deployment.
- CLAIMWhat the system is intended to show
- SOURCEWhere the statement can be checked
- BOUNDARYWhat is explicitly outside the evidence
Progress without ambiguity.
Prototype marks an interaction or architecture under validation. Research System marks an investigative workflow. In Development marks active construction without a finished-production claim.
PROTOTYPERESEARCH SYSTEMIN DEVELOPMENT
Small slices, visible behavior.
Work begins with the user, assets, and trust boundaries; moves through the smallest testable architecture; and ends with evidence, operating signals, and a clear next-hardening list.
Security is treated as a delivery constraint from the first model—not a final checklist.
01OBSERVEUsers · assets · failure paths
02MODELBoundaries · contracts · controls
03BUILDFocused · reversible · observable
04VERIFYTests · evidence · next risks
CORE EXPERTISE
02 / EXPERTISE
Security thinking across the stack.
My work connects threat awareness, data, software engineering, and automation rather than treating them as isolated disciplines.
Security Engineering
Threat analysis, vulnerability assessment, network visibility, and defensive automation.
Applied AI / ML
Practical models, retrieval systems, automation, and responsible AI integration.
Product Engineering
Flask, Django, APIs, databases, responsive interfaces, and maintainable delivery.
Operations & Automation
Reliable workflows, reporting, role-based systems, and deployment infrastructure.
03 / WRITING
Notes from the field.
Research, technical breakdowns, and observations across cybersecurity, AI, and engineering.
Writing is being prepared. The research log will be published here.
RESEARCH LAB
04 / LAB
Questions turned into controlled experiments.
The lab holds bounded investigations rather than finished-product claims. Each dossier states the research hypothesis, test method, evidence target, and condition that would invalidate the direction.
01INTAKEQuestion and risk
02ISOLATEScope and boundary
03TESTSmallest useful experiment
04OBSERVEEvidence and limits
IN DEVELOPMENT
Threat-model workbench
- RESEARCH HYPOTHESIS
- A compact asset-and-boundary worksheet can expose abuse paths before implementation choices become expensive.
- METHOD
- Map actors, assets, entry points, trust crossings, misuse cases, and proposed controls.
- EVIDENCE TARGET
- A reviewable model that connects each risk to an owner, control, and verification step.
- CONSTRAINT
- The model supports decisions; it does not prove a system is secure.
IN DEVELOPMENT
Detection workflow
- RESEARCH HYPOTHESIS
- Alerts become more actionable when collection, context, prioritization, and human review are visible in one flow.
- METHOD
- Use synthetic events to exercise normalization, rule evaluation, evidence capture, and escalation states.
- EVIDENCE TARGET
- Repeatable test cases showing why an event was promoted, suppressed, or sent for review.
- CONSTRAINT
- No claim of live monitoring coverage is made without a deployed data source.
IN DEVELOPMENT
Retrieval-grounded assistant
- RESEARCH HYPOTHESIS
- Answers are more useful when every response is bounded by approved sources and exposes its supporting context.
- METHOD
- Compare retrieval, source filtering, refusal behavior, and citation coverage on a controlled document set.
- EVIDENCE TARGET
- Evaluation cases for grounded answers, missing context, conflicting sources, and safe refusal.
- CONSTRAINT
- Generated output remains advisory and requires verification for consequential decisions.
IN DEVELOPMENT
Deployment observability
- RESEARCH HYPOTHESIS
- A small release is safer when health, configuration, migrations, and rollback evidence are treated as one deployment record.
- METHOD
- Exercise readiness checks, structured logs, failure states, and recovery notes against a controlled service.
- EVIDENCE TARGET
- A concise release ledger showing what changed, what was checked, and how recovery would proceed.
- CONSTRAINT
- Local checks do not establish production reliability without live operational evidence.
05 / ABOUT
Curiosity turned into systems.
A BSc IT graduate focused on cybersecurity and intelligent software, based in Nepal and building for real operational needs.
I approach technology as both an engineering discipline and a way to make complex work clearer, safer, and more effective.